Risk aversion or risk management?: How measures of risk aversion affect firm entry and firm survival

Size: px
Start display at page:

Download "Risk aversion or risk management?: How measures of risk aversion affect firm entry and firm survival"

Transcription

1 Economcs Workng Paprs ( Economcs Rsk avrson or rsk managmnt?: How masurs of rsk avrson affct frm ntry and frm survval In Soo Cho Iowa Stat Unvrsty, Ptr Orazm Iowa Stat Unvrsty, Follow ths and addtonal works at: Part of th Economcs Commons Rcommndd Ctaton Cho, In Soo and Orazm, Ptr, "Rsk avrson or rsk managmnt?: How masurs of rsk avrson affct frm ntry and frm survval" (2011. Economcs Workng Paprs ( Ths Workng Papr s brought to you for fr and opn accss by th Economcs at Iowa Stat Unvrsty Dgtal Rpostory. It has bn accptd for ncluson n Economcs Workng Paprs ( by an authorzd admnstrator of Iowa Stat Unvrsty Dgtal Rpostory. For mor nformaton, plas contact dgrp@astat.du.

2 Rsk avrson or rsk managmnt?: How masurs of rsk avrson affct frm ntry and frm survval Abstract Th lnk btwn masurd rsk avrson and th dcson to bcom an ntrprnur s wll stablshd, but th lnk btwn rsk prfrncs and ntrprnural succss s not. Standard thortcal modls of occupatonal choc undr uncrtanty mply a postv corrlaton btwn an ndvdual's dgr of rsk avrson and th xpctd rturn from an ntrprnural vntur at th tm of ntry. Bcaus th xpctd rturn s th rsk nutral quvalnt valu, a hghr xpctd rturn mpls a hghr survval probablty, and so mor rsk avrs ntrprnurs should survv mor frquntly than thr lss rsk avrs countrparts. W tst that prdcton usng succssv ntry cohorts of young ntrprnurs n th Natonal Longtudnal Survy of Youth 1979 (NLSY79. Th mprcal rsults soundly rjct th prdcton: th most succssful ntrprnurs ar th last rsk avrs. Ths surprsng fndng calls nto quston th ntrprtaton of common masurs of rsk avrson as masurs of tast for rsk. Instad, masurd rsk atttuds prform as f thy ar ndcators of ntrprnural ablty-- th last rsk avrs ar apparntly thos who can bst assss and manag rsks. Indd, our ntrprtaton s consstnt wth th work of rcnt xprmntal studs that fnd that th lss rsk avrs hav hghr cogntv ablty. Kywords ntrprnurshp, frm survval, rsk avrson, human captal, hazard rat Dscplns Economcs Ths workng papr s avalabl at Iowa Stat Unvrsty Dgtal Rpostory:

3 Rsk Avrson or Rsk Managmnt?: How Masurs of Rsk Avrson Affct Frm Entry and Frm Survval In Soo Cho, Ptr Orazm Workng Papr No August 2011 IOWA STATE UNIVERSITY Dpartmnt of Economcs Ams, Iowa, Iowa Stat Unvrsty dos not dscrmnat on th bass of rac, color, ag, rlgon, natonal orgn, sxual orntaton, gndr dntty, gntc nformaton, sx, martal status, dsablty, or status as a U.S. vtran. Inqurs can b drctd to th Drctor of Equal Opportunty and Complanc, 3280 Bardshar Hall, (

4 Rsk Avrson or Rsk Managmnt?: How Masurs of Rsk Avrson Affct Frm Entry and Frm Survval Insoo Cho Ptr F. Orazm Dcmbr 2011 Abstract Th lnk btwn masurd rsk avrson and th dcson to bcom an ntrprnur s wll stablshd, but th lnk btwn rsk avrson and ntrprnural succss s not. Standard thortcal modls of occupatonal choc undr uncrtanty mply a postv corrlaton btwn an ndvdual s dgr of rsk avrson and th xpctd rturn from an ntrprnural vntur at th tm of ntry. Bcaus th xpctd rturn s th rsk nutral quvalnt valu, a hghr xpctd rturn mpls a hghr survval probablty, and so mor rsk avrs ntrprnurs should survv mor frquntly than thr lss rsk avrs countrparts. W tst that prdcton usng succssv ntry cohorts of young ntrprnurs n th Natonal Longtudnal Survy of Youth 1979 (NLSY79. Th mprcal rsults soundly rjct th prdcton: th most succssful ntrprnurs ar th last rsk avrs. Ths surprsng fndng calls nto quston th ntrprtaton of common masurs of rsk avrson as masurs of tast for rsk. Instad, masurd rsk atttuds prform as f thy ar ndcators of ntrprnural ablty th last rsk avrs ar apparntly thos who can bst assss and manag rsks. Indd, our ntrprtaton s consstnt wth th work of rcnt xprmntal studs that fnd that th lss rsk avrs hav hghr cogntv ablty. Ky words: ntrprnurshp, frm survval, rsk avrson, human captal, hazard rat JEL Classfcatons: L24, J24, M1, D81 Dpartmnt of Economcs, Iowa Stat Unvrsty, Ams, IA USA. Cho: cho@astat.du Orazm: pfo@astat.du

5 1. Introducton Numrous studs hav shown that masurd rsk avrson affcts occupatonal and human captal nvstmnt dcsons. to pck prvat sctor jobs (Pffr, Lss rsk avrs ndvduals ar mor lkly Lss rsk avrs ndvduals ar also mor lkly to bcom ntrprnurs (Van Praag and Cramr, 2001; Hartog t al., 2002; Cramr t al., 2002; Eklund t al., 2005; Kan and Tsa, 2006; Ahn, Smlarly, lss rsk avrs ndvduals ar mor lkly to ntr occupatons and ducatonal nvstmnts charactrzd by hghr arnngs varancs (Orazm and Mattla, 1991; Shaw, 1996; Bonn t al., 2007; Isphordng, A mssng lmnt n ths mprcal analyss of th ffcts of rsk avrson on occupatonal or ducatonal dcsons s whthr thos rsk avrson also affct th outcoms of thos dcsons. 1 For xampl, f rsk atttuds affct th dcson to bcom an ntrprnur, thy should also affct th rsknss of th vntur condtonal on bcomng an ntrprnur. Mor rsk avrs ntrprnurs should slct safr vnturs whl lss rsk avrs ntrprnurs should opt for rskr frms. As a consqunc, holdng constant obsrvabl sklls, mor rsk avrs ntrprnurs should survv mor frquntly than thr lss rsk avrs countrparts. Ths papr shows that n thory, thr s a postv corrlaton btwn an ndvdual s dgr of rsk avrson and th xpctd rturn from an ntrprnural vntur at th tm of ntry. Bcaus th xpctd rturn s th rsk nutral quvalnt valu, hghr xpctd rturn mpls a hghr survval probablty. From that proposton, w post a hypothss that mor rsk avrs ntrprnurs hav hghr survval probablty than thr lss rsk avrs countrparts. W tst th hypothss usng succssv ntry cohorts of young ntrprnurs n th Natonal Longtudnal Survy of Youth 1979 (NLSY 79. W 1 Excptons nclud Rauch and Frs (2007 and Calndo t al. (2010 whch wll b dscussd blow. 1

6 show that th prdcton s soundly rjctd: th most succssful ntrprnurs ar th last rsk avrs. Ths surprsng fndng calls nto quston th ntrprtaton of common masurs of rsk avrson such as thos usd n th NLSY as masurs of tast for rsk. Instad, ths masurs prform as f thy ar ndcators of ntrprnural skll th last rsk avrs ar apparntly thos who can bst assss and manag rsks. Indd, ths ntrprtaton s consstnt wth rcnt xprmntal vdnc prsntd by Frdrck (2005, Bnjamn t al. (2006, Burks t al. (2009 and Dohmn t al. (2010 who fnd that th last rsk avrs hav supror cogntv ablty masurd by IQ. Ths suggsts an altrnatv ntrprtaton: agnts wth th lowst masurd rsk avrson may hav unusually hgh ndowmnts of unmasurd ablty, and t s ths human captal advantag that lads thm to bcom ntrprnurs, prvat sctor mploys, and ntrants nto occupatons and ducaton chocs wth gratr arnngs varanc. Th nxt scton drvs th thorzd postv rlatonshp btwn rsk avrson and probablty of busnss succss condtonal on ntrprnural ntry. Scton 3 prsnts an mprcal mthodology that wll tst th rol of masurd rsk avrson on ntrprnural survval by ntry cohort. slcton procss and th masur of rsk avrson. Scton 4 rvws th sampl Emprcal rsults that soldly rjct th hypothszd rlatonshp btwn masurd rsk avrson and ntrprnural succss ar prsntd n scton Thortcal motvaton Pratt (1964 dfnd a rsk prmum as th dffrnc btwn th xpctd 2

7 rturn and th crtanty quvalnt rturn. Th hghr rqurd rsk prma mak t lss lkly that mor rsk avrs agnts wll choos to bcom ntrprnurs. Howvr, th mor rsk avrs ndvduals who nvrthlss ntr slf-mploymnt should nvst n vnturs wth gratr xpctd rturn and lss rsk compard to ntrprnurs who ar lss rsk avrs. Ths scton dmonstrats that ths prdctons follow from a standard thortcal modl of occupatonal choc undr uncrtanty. 2.2 Thory Agnts ngag n choosng on of svral altrnatv occupatons. Th ndvdual s utlty dpnds on th montary and hdonc rturns to occupatonal ntry, U(y j,α j, whr y j s th prsnt valu of arnngs and α j s a postv or ngatv hdonc rturn from an occupaton j. Th utlty functon s concav and strctly ncrasng n arnngs so that t can rflct an ndvdual s rsk avrson. Assumng addtv sparablty, th utlty can b dscrbd as U = u( y + α (1 j j j whr u s an ncrasng and strctly concav functon n y, u > 0 and u < 0. j For smplcty, suppos that thr ar two occupatons, ntrprnurshp ( and wag work (w. W assum that th prsnt valu of ncom from wag work s known but that th rturn from ntrprnurshp s uncrtan. 2 Howvr, th dstrbuton of ntrprnural arnngs s assumd to hav known man and varanc. W also assum that th hdonc rturn from all occupatons s known wth crtanty. 3 2 Th thory s for a pont n tm occupatonal dcson subjct to xpctd arnngs n th occupaton at that tm. W do not consdr occupatonal rswtchng, although any plannd rswtchng could b ncorporatd nto th xpctd arnngs stram at that pont n tm. 3 W gt smlar mplcatons f th rturns to wag work ar uncrtan but hav lowr varanc than rturns to ntrprnurshp, but th drvaton s mor complcatd. Our drvaton s consstnt wth 3

8 Th xpctd utlty of choosng ntrprnurshp can b approxmatd by th scond ordr Taylor srs xpanson of (1 around man of arnngs, : EU 1 2 = u( + u ( E( y + u ( E(( y + α = u( + u ( σ + α (2 2 2 whr = E y and σ = Var(. ( y An ndvdual wll ntr th rsky occupaton f hs xpctd utlty n th rsky job ( s gratr than that n th saf job (w: EU > EUw. Th agnt s ndffrnt btwn th two altrnatvs whn EU = EU, 4 w or 1 2 u( + u ( σ + α = u( yw + αw (3 2 Dvdng both sds of (3 by u and rarrangng ylds ( 1 2 u( u( yw + α αw γσ = > 0 2 u ( u ( whr γ = > 0 s th Arrow-Pratt coffcnt of absolut rsk avrson u ( (ARA. 5 6 (4 W nd to stablsh how an ncras n rsk avrson affcts th rqurd rturn usng th varanc n wag work as a basln varaton. Hamlton (2000 found that th standard dvaton of arnngs from slf-mploymnt 2-4 tms largr than th standard dvaton of arnngs from wag work. In th NLSY, th coffcnt of varaton of log hourly arnngs from slf-mploymnt s 67% to 89% largr for slf-mploymnt than wag work. 4 2 Rcall that w hav mposd σ w = 0 and w = y w. 5 Altrnatvly, w could assum rlatv rsk avrson, whch gnrats th sam rsult as ARA dos. S Appndx1 for th drvaton. 6 Notc that as th known hdonc rturn from ntrprnurshp ncrass rlatv to th hdonc rturn from wag work, th rqurd gap n xpctd ncom, u( u( y ncssary to lav th ndvdual w ndffrnt btwn and w gts smallr. Ths s consstnt wth Hamlton s (2000 concluson that ntrprnurs accpt lowr pay n ordr to hav thr own busnsss. 4

9 n th rsky job n ordr to kp an ndvdual ndffrnt btwn and w for any gvn rsk, σ. Th answr dpnds on th xpctd rturn from at th tm ndvduals ar choosng an occupaton. Takng th partal drvatv of wth rspct to γ n (4 ylds = γ [ u ( ] σ [ u ( ] 2 u ( [ u( u( y 1 2 w + α α ] σ u ( = 2 [ u( u( yw + α α w] u ( u ( u ( w 1 2 σu ( = 2 > 0 u ( + γ[ u( u( y + α α ] w w > 0 bcaus u > 0 du to th concavty of u, γ > 0, and γ (5 u( u( + α α > 0 from quaton (4. Th postv sgn ndcats that as an y w w ndvdual bcoms mor rsk avrs, h rqurs a hghr xpctd rturn from to b as wll off n th rsky occupaton as n th saf occupaton w. If rsk avrson affcts th rqurd xpctd rturn from ntrprnurshp at th tm of ntry, t should also affct th probablty of ntrprnural survval. Th xpctd rturn s th rsk nutral quvalnt prsnt valu of ntrng ntrprnurshp, and so rqurng an vn hghr xpctd rturn mpls a hghr survval probablty. To formalz ths proposton, lt T 0 dnot th duraton of th frm s xstnc so that f an ntrprnur xts a busnss t yars aftr start-up, T = t. T has a cumulatv dstrbuton functon, Ft ( = Pr( T t, whch s th probablty 5

10 of frm falur by tm t. Th assocatd probablty dnsty functon s f ( t. An ntrprnur dcds to shut down hs busnss f th ralzd prsnt valu of th montary and hdonc rturns from opratng th busnss xcds th opratng cost of tm whch quals th prsnt valu of th stram of pcunary and hdonc rturns from wag work at t. Th ralzd rturn s dcomposd nto xpctd rturn and unxpctd rturn: R π,, + α < y,( x + α (6 t wt w π = ( γ + ξ (7 R t,, t,, R whr π t,, dnots s ralzd prsnt valu of rturns from th ntrprnural vntur at tm t. W dcompos th ralzd rturn to ntrprnurshp nto two parts: : th rturn xpctd at th tm of start-up whch s a functon of s dgr of rsk avrson; and ξ t,,: a random ngatv or postv shock to th xpctd stram of rturns to ntrprnurshp that s ralzd as of tm t. Th shock ξ t,, s drawn from th dstrbuton g( ξ t,,. Th th ntrprnur wll survv n busnss unlss thy rcv a suffcntly bad draw on ξt,, that th xpctd prsnt valu of pcunary and hdonc rturns to th vntur fall blow th ntrprnur s opportunty costs of tm. In (6, th opportunty costs ar rprsntd from th prsnt valu of pcunary and nonpcunary rturns from wag work. That would nclud y, ( x whch s th prsnt valu of antcpatd wags whch ar basd on s human captal and sococonomc background dsgnatd by th vctor x ; plus th hdonc rturn from wag work, wt α w. 6

11 Th cumulatv dstrbuton functon of T can b spcfd by th probablty of falur at tm t : Ft ( = Pr( T t = Pr( π + α y ( x + α R t,, wt, w = Pr( ( γ + ξ + α y + α,, t w, t w = Pr( ξ y ( x ( γ + α α t,, wt, w = = (8 * * Pr( ξt,, a G( a whr * a = yw, t( x ( γ + αw α dnots th rsrvaton proft lvl at whch th ntrprnur s ndffrnt btwn shuttng down and stayng n busnss; and G s cumulatv dstrbuton functon of ξ t,,. Th rsrvaton proft s dcrasng n rsk avrson bcaus of (5, whch mans that mor rsk avrs ntrprnurs hav a lowr rsrvaton proft: * a = < 0. γ γ Th probablty that an ntrprnur xts s gvn by a* * g( t d G( a (9 η = ξ ξ = Assumng th proft shocks ξt,, ar d random xpctaton rrors, th xpctd duraton n busnss bfor rcvng a bad draw s: 1 1 St ( 1 Ft ( η = Ga = = (10 * ( whr St ( th probablty that th frm survv untl tm t. Equaton (10 7

12 ndcats that bcaus * Ga ( s dcrasng n γ, th xpctd lngth of tm n busnss s ncrasng n γ. As a rsult, condtonal on havng ntrd ntrprnurshp, th most rsk-avrs agnts hav to hav th lowst probablty of xt bcaus thy rqurd th hghst xpctd rturns from th vntur at th tm of ntry. Our thortcal prdctons contrast wth a thory advancd n psychology that th most succssful ntrprnurs hav mdum lvls of rsk avrson. Atknson and Brch (1978 argu that ntrprnurs ar motvatd by conflctng motvatons to achv succss and to avod falur. In ffct, thr modl assums utlty s th product of probablty of succss, P, and probablty of falur, 1-P. Utlty s maxmzd at P= 0.5 whch thy ntrprt as ntrprnurs wth ntrmdat rsk prfrncs. Mrdth t al. (1982 prsnt a smlar thory that succssful ntrprnurs ar modrat rsk-takrs. Thy argu that ntrprnurs lk to challng thmslvs wth dffcult tasks bcaus thy gt satsfacton by accomplshng dffcult tasks. On th othr hand, ntrprnurs want to avod dsutlty from falur. As a consqunc, succssful ntrprnurs pck projcts of ntrmdat rsk that offr rasonabl probablty of succss and at last som modrat challng. Ths thors fal to ncorporat xpctd rturns nto thr modls whch lad thm to confus th rsknss of th projcts undrtakn wth th rsk prfrncs of th ntrprnurs. By mphaszng projct survval rathr than antcpatd or ralzd rturn on nvstmnt, thy dvalu hgh rsk and hgh rward vnturs. Nvrthlss, thr prdcton that ntrmdat rsk prfrncs ar most succssful can b tstd aganst th data as w xamn our prdcton that succss s hghst among th most rsk avrs. 8

13 3. Emprcal mthodology In ordr to nvstgat th xtnt to whch rsk avrson affcts frm falur, w ncorporat a hazard rgrsson varant of th xt probablty Ft = a = Ga n (8. Th hazard rat at whch splls ar compltd at * * ( Pr( ξ,, t ( duraton t condtonal on survvng up to t s dfnd as f ( t f( t ht ( = = 1 F( t S( t, (11 whr St (, f ( t, and Ft ( ar as dfnd n quaton (10. Assumng th survval tm t has a Wbull dstrbuton, th hazard functon and survval functon at tm t for ndvdual ar gvn by ht (, βδ, γ, a = xp( aβ+ δγ pt (12 * * p 1 St a = a + t (13 * * (,,, xp{ xp( p βδ γ β δγ } whr p s an ancllary shap paramtr to b stmatd from th data, γ s a catgorcal varabl ndcatng atttuds toward rsk wth hghr lvls rprsntng gratr accptanc of rsk, and * a s composd of human captal and othr sococonomc varabls that st th ntrprnur s pcunary ( y, ( x and wt nonpcunary ( α and α w costs of tm. W nclud ducaton, prvous labor markt xprnc, ag, and parntal slf-mploymnt/managmnt xprnc n th x. For our masurs of th rlatv hdonc rturns from slf-mploymnt and wag work, α and α w, w nclud martal status and numbr of chldrn. Such famly varabls may b affct th rlatv njoymnt n th two occupatons. Addtonal controls 9

14 nclud rgonal and ndustry dumms to account for sctoral and rgonal macroconomc condtons. Dfntons and dscrptv statstcs of th varabls mployd n our conomtrc analyss ar prsntd n Tabl 1. Th sgn on δ rvals whthr dcrasng lvls of rsk avrson (.., ncrasng lvls of wllngnss to tak rsk ncras th hazard of frm falur, consstnt wth th thortcal rqurmnt that mor rsk avrs ntrprnurs must hav hghr xpctd rturns from slctng at th tm of ntry. Ths tst mplctly assums that xpctaton rrors ar not systmatc, manng that ntrprnurs forcasts ar ratonal gvn nformaton at th tm of ntry. Thn, takng xpctatons across multpl ntry cohorts, xpctd and ralzd rturns to ntrprnurshp convrg to th sam man valu. Although w control for ntrprnurs obsrvd charactrstcs, thr may b unobsrvd factors that affct th ntrprnural survval or falur n addton to th obsrvd rgrssors. Hnc, a fralty modl s usd to account for th prsnc of unobsrvd htrognty among ndvduals. Bcaus fralty ( λ s not drctly stmatd from th data, w assum that t has unt man and fnt varanc (θ whr θ s stmatd from th data. Assumng λ s drawn from an nvrs Gaussan dstrbuton, th survval functon condtonal on th fralty s dfnd as 7 λ Sθ ( t, β, δθ, λ = { S( t, βδ, g( λ dλ 0 7 λ s ntroducd as a multplcatv ffct on th hazard, h(t λ=λh(t. So λ rprsnts th cumulatv ffct of omttd covarats. Explotng th rlatonshp btwn cumulatv hazard functon and survval functon, th survval functon condtonal on th fralty s gvn as follows: t f( u S( t λ = xp{ h( u λ du} = xp{ λ du} = { S( t} Su ( 0 λ 10

15 1 1/2 = xp{ (1 [1 2θ ln{ St (, β, δ}] } (14 θ whr g (λ s probablty dnsty functon of λ and th subscrpt θ ndcats th dpndnc of S(t on θ. Th log-lklhood functon can b wrttn as n * ( β, δθ,, γ = { ln θ(, βδθ,, + (1 ln θ(, βδθ,, } = 1 L a d f t d S t (15 whr d s a bnary ndcator dfnd such that d = 1 f th ntrprnur xtd from d hs busnss and 0 othrws; fθ( t, β, δθ, = Sθ( t, βδθ,, s probablty dnsty dt functon of survval duraton t. If w slct an napproprat basln hazard functon, unrlabl stmats can rsult (Hckman and Sngr, As an altrnatv, w us a smparamtrc Cox proportonal hazard modl whch rqurs no assumpton about th basln hazard functon n ordr to xamn th robustnss of our fndngs to altrnatv assumptons about th rror procss. Dfnton of survval and falur, and craton of ntry cohorts In ordr to construct th log-lklhood functon n (15, w nd to dfn busnss survval and falur. W rqur a common wndow of tm ovr whch to judg a vntur s succss. Our frst stp s to pck a sampl of frst-tm ntrprnurs who ntrd busnss n th sam yar. By analyzng ntrprnural succss from startup, w avod lft-cnsord ntrprnural splls that hav alrady slctd out th most pron to falur. Sttng a common startng pont also nsurs that all vnturs n th cohort ar subjct to th sam macroconomc nvronmnt. W dfn ntrprnural succss as rmanng n busnss at last 6 yars aftr startup and busnss falur as closng th busnss wthn th frst 6 yars. Two-thrds 11

16 of U.S. frms clos wthn 6 yars of ntry, and so frms that survv at last 6 yars hav prformd wll abov avrag (Dunn t al., 1988; Knaup and Pazza, Th data for th analyss s drawn from th Natonal Longtudnal Survy of Youth 1979 (NLSY79. Th frst yar th rspondnt rportd slf-mploymnt s assumd to b thr startup yar. In ordr to crat succssv cohorts by ntry yar, w dntfy thos who startd thr busnss n 1992, 1994, 1996, 1998, 2000 and Thos who rman slf-mployd as of th wav of th survy conductd 6 yars aftr ntry ar tratd as survvors. All othrs n th cohort ar tratd as falurs. Th xt yar s masurd as th mddl yar btwn th last rportd slf-mploymnt yar and th yar of nw mploymnt status. For xampl, f th rspondnt rportd slf-mploymnt n 1994 but pad-mploymnt n 1996, thn w us 1995 as th slfmploymnt xt yar. W can stack ths 6 cohorts nto a srs of ovrlappng sampl wndows of 6 yars ach. Fgur 1 shows cohorts dffrntatd by ntry yars and thr rlatd sx yar wndow wth ndvdual cass labld as succss or falur. Dummy varabls for yar of ntry control for th common macroconomc nvronmnt shapng th xpctatons of ach ntry cohort. Th 2002 cohort s th last on consdrd du to th rqurd sx yar sampl wndow. Th last yar for whch data ar avalabl n th NLSY79 s Sampl slcton 8 Dunn t al. (1988 found that 62% of manufacturng frms xtd wthn fv yars followng startup. Knaup and Pazza s (2007 xamnaton of th 1998 cohort of nw frm ntrants found that about twothrds of frms xtd by th sxth yar n ach of th 10 ndustrs xamnd. 9 Th NLSY79 conductd survy annually from 1979 through 1994 and bnnally thraftr. Du to th bnnal survy ovr th prod w ar ntrstd n, w assum that th startup s n an vn yar. 12

17 Th NLSY79 ncluds 12,686 ndvduals who wr yars old n th ntal survy yar. Focusng on our ntrprnurshp ntry cohorts btwn 1992 and 2002, our sampl of slf-mployd ndvduals wll b ntatng thr vnturs btwn ags 27 and 37, and dcdng to contnu th vntur or shut down btwn th ags of 28 and 45. Bcaus ths study s ntrstd n ntrprnural survval, w nclud only thos who had a frst-tm startup n on of th 6 vn yars btwn 1992 and Th slf-mployd ar dntfd by usng th class of workr catgory n th NLSY79. W consdr rspondnts to b slf-mployd f at last on job s rportd as slf-mploymnt among fv possbl jobs lstd. Unfortunatly, not all th rspondnts wr ntrvwd n ach survy yar. For th mssng cas of class of workr w trackd down thr mploymnt status by lookng at job tnurs bfor and aftr th mssng yar. For nstanc, f a rspondnt s job tnurs on slf-mploymnt ncrasd by four yars ovr two conscutv survys (.., 1994, 1998, w consdr hm as slf-mployd n th yar whn h was not ntrvwd (.., Whn multpl splls of ntry and xt ar rportd, only th frst ntry and xt ar ncludd. W drop ntrprnurs from th sampl f thy nrolld n school at th tm of startup. W also drop ndvduals wth ncomplt nformaton on ndvdual attrbuts at th tm of start-up as w nd to kp our vctor of covarat controls xognous to th progrss of th busnss ovr th nxt sx yars. 10 Th fnal sampl ncluds 588 ntrprnurs. Th survval and xt rats by ntry cohort ar summarzd n Tabl 2. Avrag xt rat vars from 49% to 71% across ntry cohorts. Th 2000 ntry cohort has th lowst xt rat whl th 1992 ntry cohort has th hghst xt rat. Ovrall, 10 For xampl, f martal status s not known n th yar of start-up but th ndvdual s lstd as marrd or dvorcd ovr th nxt sx yars, t s plausbl that th martal status s affctd by th succss of th busnss. 13

18 59 % of th slf-mployd xtd thr busnss wthn sx yars of start up, clos to th 65% xt rat rportd n natonal analyss of frm survval. Rsk avrson n th NLSY s lctd usng qustons closly rlatd to th smpl occupatonal choc modl prsntd n scton 2. Rspondnts ar prsntd a srs of hypothtcal occupatons wth dffrnt xpctd lftm ncom lvls and varancs. Th ndvdual s askd to choos btwn a saf job payng a fxd ncom and a scond rsky job that wll doubl th saf ncom wth 50% probablty or ls pay only a fracton of th saf ncom wth 50% probablty. 11 Th dgr of rsk avrson s masurd by th dgr to whch th rspondnt s wllng to accpt downsd rsk, masurd by th amount that pay could b rducd n th rsky job rlatv to th saf job. Th lftm ncom gambl qustons ar askd n 1993, 2002, 2004, and 2006 n th NLSY79. Bcaus th 2002 ntry cohort s th last on consdrd basd on th wndow of 6 yars, w mploy th rsk avrson masurd n 1993 n ordr to avod havng masurs of rsk avrson that follow th busnss ntry dcson and rflct th succss or falur of th ntrprs. Basd on thr rsponss to ths qustons n th 1993 wav of th NLSY, w plac our ntrprnurs nto on of four rsk prfrnc catgors rangng from th most rsk avrs (catgory1 to th last rsk avrs (catgory4. 12 Th rsk prfrnc catgors ar constructd as follows: 11 S Appndx 2 for th orgnal qustons. 12 For mor dtald lctaton, s Barsky t al. (1997 who fnd that survy-basd rsk avrson drvd by lctng rsponss to hypothtcal chocs ar corrlatd wth rsky bhavors such as smokng, drnkng, falng to hav nsuranc, and holdng stocks. 14

19 1 f 2 f Rsk ndx = 3 f 4 f rjct 1 3cut rjct 1 3cut accpt 1 3cut accpt 1 3cut & rjct 1 5cut & accpt 1 5cut & rjct 1 2cut & accpt 1 2cut ; th ; th most last rsk rsk avrs avrs Th dstrbuton of th masurd rsk avrson for our ntrprnural ntry cohorts s prsntd n Tabl 3. Ovrall, 40% of th ntrprnurs fall nto th most rsk avrs catgory and 28 % fall nto th last rsk avrs group. Thr s no apparnt systmatc pattrn to th dstrbuton of th masurd rsk avrson across cohorts, wth th 2002 and 1992 cohorts havng smlar varaton n rsk atttuds. Ths dstrbutons ndcat that thr s consdrabl varaton n masurd rsk atttuds n all th ntry cohorts ncludd n our sampl. 5. Estmaton rsults Th rsults of th falur hazard modls appld to th 6 stackd ntry cohorts of young ntrprnurs n th NLSY79 ar rportd n Tabl 4 Panl A. For robustnss, w show varous spcfcatons mplyng dffrnt assumptons about th natur of th rror trms and ndvdual unobsrvd htrognty. Th stmat of θ s statstcally dffrnt from zro and a lklhood rato tst for th prsnc of htrognty s statstcally sgnfcant, whch confrms th xstnc of unobsrvd ndvdual trats that affct probablty of busnss falur. Th stmatd shap paramtr p s gratr than 1 and statstcally sgnfcant, whch mans that th hazard of falur ncrass wth tm. 13 Th sgnfcant stmats of θ and p suggst that th Cox modl s msspcfd. 14 W wll thrfor focus our dscusson on th fralty 13 Notc that ths s th shap of ndvdual hazard functon. Whn fralty s sgnfcant, th populaton hazard functon wll tnd to bgn fall past a crtan pont (.., nvrs U-shap rgardlss of ndvdual hazard functon. Ths s bcaus mor fral ntrprnurs xt arlr and mor homognous populaton of survvor wll b lft as tm passs (Gutrrz Not that bcaus th hazard changs nonlnarly wth frm ag, mxng dffrnt frm ntry cohorts 15

20 modl rsults, although non of our qualtatv conclusons s snstv to th spcfcaton choc. W rport our rsults n trms of thr mpld hazard rato:.. th proportonal shft n th falur hazard functon du to on unt chang of th covarat, holdng fxd all othr factors ncludng th unobsrvd fralty. Th control masurs prform as n arlr studs of frm longvty. In ln wth Holtz-Eakn t al. (1994, Crssy (1996 and Taylor (1999, ag has a sgnfcant ffct on th hazard of falur. Th probablty of busnss falur s quadratc n ag, dcrasng ntally and thn ncrasng past a crtan ag (.., 37 n our data. Prvous labor markt work xprnc plays an mportant rol n lowrng th hazard of frm falur, consstnt wth Taylor s (1999 analyss. An addtonal yar of prvous xprnc cuts th hazard of falur by 10%. Educaton (n yars of schoolng has no sgnfcant mpact on frm hazard rat. Ths suggsts that acadmc succss s a poor ndcator of ntrprnural ablty (Taylor, Apparntly, practcal ntllgnc acqurd from work xprnc s mor mportant (Strnbrg, Fnally, our rsults show that gndr, rac and martal status hav lttl ffct on hazard rat. Havng slf-mployd parnts also dos not hav a sgnfcant mpact on busnss falur although t s found to ncras probablty of bcomng slf-mployd n th prvous ltratur (.g., Lntz and Laband, 1990; Dunn and Holtz-Eakn, Turnng to our man concrn, th frst column rports th ffct of rsk atttuds on frm xts whn rsk s th only rgrssor. Rcall that th rsk ndx gos from most (1 to last (4 rsk avrs. W hav th unxpctd rsult that as th ntrprnur bcoms lss rsk avrs, as masurd by standard masurs of rsk avrson, th can confound th rror structur of th hazard modl. That furthr supports our sampl stratgy of pckng ntrprnural ntry cohorts wth fxd samplng wndows whch mplctly holds fxd th hazard trajctory for mmbrs of ach ntry cohort. 16

21 lklhood of falur dcrass. Ths s th xact oppost of th prdctd rlatonshp btwn rsk avrson and frm survval. It s possbl that th unxpctd rsult s attrbutabl to a corrlaton btwn rsk atttuds and ndvdual human captal and dmographc varabls that ar known at th start of th ntrprnural spll. Howvr, whn w add ths masurs n Column 2 th mpact of rsk atttuds on ntrprnural hazard of falur s almost dntcal to that n th frst column. Th nxt column adds controls for ndustry. Tchncally ths masurs ar ndognous as th ntrprnur pcks th sctor at th tm of ntry, and so ths sctoral dumms should b xcludd. Nvrthlss, thy ar commonly found to affct frm survval (Taylor, 1999 du to sctor spcfc shocks that may dffrntally affct proftablty for frms n th sam cohort. Th most rsk avrs ar stll th most lkly to fal wth a smlar hazard rato, although th stmat loss prcson. Ovrall, th rsults from th four spcfcatons suggst that lss rsk avrson rsults n a lowr hazard and thrfor a longr survval tm. Mor prcsly, a unt ncras n rsk accptanc ndx (.., ncras n wllngnss to tak rsks s assocatd wth a 14% dcras n th hazard of falur. As a rsult, w soundly rjct th hypothss that mor rsk avrson ncrass th probablty of ntrprnural survval. Our fndng s also nconsstnt wth th psychologcal modls that argu th most succssful ntrprnurs hav modrat rsk avrson. In ordr to chck th psychologcal argumnt, w tst for th ntrmdat rsk atttuds aganst th most and th last rsk usng rsk atttuds dumms. To do so, w gnrat thr rsk atttud dumms: wllngnss to tak low, mdum, and hgh rsk. W us wllngnss to tak low rsk as bas catgory. As shown n Panl B of Tabl 4, w do not obsrv th 17

22 prdctd nonlnar rlatonshp btwn rsk avrson and hazard of frm falur. 15 Instad, th hazard rato drops progrssvly as wllngnss to tak rsk ncrass. Indd, th most succssful ntrprnurs ar th last rsk avrs as bfor. Our fndngs dffr from th fw prvous studs rlatng rsk avrsub and ntrprnural succss. Th mta analyss of past studs of rsk avrson and ntrprnural succss by Rauch and Frs (2007 rportd a postv but rlatvly small corrlaton. Hnc, thy conclud that rsk atttuds hav lttl ffct on ntrprnural succss. Howvr, dffrncs n rsk and succss masurs across studs mak t hard to gnralz fndngs. A mor rcnt study by Calndo t al. (2010 rportd a U-shapd rlatonshp btwn rsk atttuds and ntrprnural falur whch thy offrd as support for th psychologcal thors. 16 On dffculty wth thr study s th rlanc on nonstandard masurs of rsk avrson that do not ft th rqurd contxt of slctng rsky occupatons. 17 A furthr problm s that rsk atttuds wr masurd aftr startup for most rspondnts and so thr masur of rsk atttuds may rflct busnss succss or falur. Howvr, th most mportant problm common to past studs s th falur to focus on th dcsons of an ntry cohort but rathr on busnss survval of ntrprnurs who hav alrady managd to survv many yars pror to th survy. Th longr a frm survvs, th mor lkly t 15 Th rsults ar not snstv to dffrnt classfcaton of rsk dummy varabls. S th Appndx 3 for th stmaton rsults. 16 It s lkly that Calndo t al. (2010 msntrprt th psychologcal thory of achvmnt motvaton as an nvrs U-shapd rlatonshp btwn rsk prfrncs and survval. Atknson and Brch (1978, among othrs, argu that hghr achvmnt s postvly rlatd to propnsty to tak an ntrmdat rsk. But ths dos not ncssarly man an nvrs U-shapd rlatonshp btwn rsk prfrncs and achvmnt. In thr thortcal modl, Calndo t al. (2010 assum that lss rsk avrson s assocatd wth hghr xpctd payoff n th cas of succss, whch ylds th odd mplcaton that th most rsk avrs pck projcts wth th lowst rturn condtonal on succss, rgardlss of varanc. In addton, thy dd not modl utlty xplctly. 17 Two sts of qustons ar usd to masur rspondnt s rsk avrson. On st of qustons asks rspondnts f thy ar rsk avrs. Th othr asks rspondnts how thy would nvst lottry wnnngs, a contxt that could lct vry dffrnt rsk prfrncs than on whr thy ar allocatng arnd ncom, and on that confuss rsk prfrncs wth portfolo allocaton. 18

23 wll contnu to survv (Knaup and Pazza, 2007, and so th rsk atttuds of ntrprnurs that alrady fald bfor th survy ar not ncorporatd nto th analyss. That slcton problm bass any ntrprtaton of th rlatonshp btwn rsk avrson and frm survval. Snstvty analyss W frst tst snstvty of th rsults wth rspct to th tm wndows ovr whch busnss survval and falur ar dfnd. Th stmaton rsults ar summarzd n Tabl 5 Panl A. For brvty, w rport only th stmats of rsk atttuds n th four spcfcatons. Th basln stmaton rsults ar rdsplayd n th frst row of Tabl 5 for rfrnc. Th scond row rprsnts th rsults whn w dfn th survval as rmanng n busnss at last 4 yars aftr startup. Th thrd row s th cas whn w consdr frm survval as rmanng n busnss at last 8 yars aftr ntry. 18 Th rgrsson rsults rman stabl n all th spcfcatons. Th ngatv rlatonshp btwn rsk atttuds and hazard of falur holds for both th wndows of 4 and 8 yars although th stmats basd on 4 yars bcom nsgnfcant. On concrn w ras wth past studs s th us of masurs of rsk avrson that ar takn aftr th rspondnt has alrady bn n busnss. W llustrat th potntal bas of usng ndognous rsk prfrnc masurs n Panl B of Tabl 5. W r-stmat th hazard rgrsson wth rsk atttuds masurd n 2002, 2004, and Whl th coffcnt s stll ngatv, th stmat s nvr statstcally sgnfcant. It appars that th bas from usng rsk avrson lctd aftr startup s to lssn vdnc that th most rsk avrs ar th last succssful. Bcaus our own rsults rly on th NLSY s 1993 lctaton of rsk atttuds, t 18 W hav to adjust th sampl as w chang th survval wndow. W los th 2002 cohort whn w apply a wndow of 8 yars bcaus th NLSY 2010 wav was not yt avalabl. Wth th four yar wndow, w gan th 2004 ntry cohort. 19

24 s possbl that w should hav xcludd th 1992 ntrprnurshp cohort. Our fndngs abov suggst that ncluson of th 1992 cohort may hav basd our rsults. W rft th hazard rgrsson wthout th 1992 ntry cohort. Th rsults ar summarzd n Tabl 5 Panl C. In all cass, th fndng that th most rsk avrs ar most lkly to fal strngthns compard to th stmats n th frst row of Tabl 5, consstnt wth our assssmnt that ndognously lctd rsk avrson bas th stmats toward zro. Lastly, rathr than usng ntry cohorts, w us a sampl of survvng ntrprnurs as of 2002, a sampl comparabl to th typ usd n prvous studs. Such sampls should b basd by th xcluson of th last succssful ntrprnurs. W add a control for frm tnur along wth th othr ndvdual charactrstcs usd bfor. As shown n Panl D of Tabl 5, all coffcnts on rsk atttuds ar now postv and nsgnfcant, consstnt wth th thortcal ffct of rsk avrson of ntrprnural succss. Slcton bas from nappropratly xcludng unsuccssful ntrprnurs appars to bas upward coffcnts on rsk atttuds. Our concluson that ntrprnurs wth th lowst masurd rsk avrson ar th most succssful holds up wll. Whn rsarchrs us slctd sampls or ndognous masurs of rsk avrson, ths fndngs ar compromsd. W blv our rsults ar mor rlabl than past studs bcaus of our us of rsk atttuds lctd bfor startup and our ncluson of all mmbrs of ach ntrprnural ntry cohort n th analyss. Rsk avrson or Rsk managmnt? Our surprsng fndng that th most rsk avrs ntrprnurs ar th most lkly to fal calls nto quston th ntrprtaton of common masurs of rsk avrson such 20

25 as thos usd n th NLSY as masurs of atttuds toward rsk. 19 Our masurd rsk atttuds prform as f thy ar ndcators of ntrprnural ablty: utlzng nformaton and makng ntllgnt dcson undr uncrtanty. In othr words, th lss rsk avrs ar apparntly thos who ar bttr abl to assss and manag rsks. Although prvous studs hav found that gratr wllngnss to tak rsk ncrass probablty of ntrprnurshp, t s lkly that th last rsk avrs agnts may hav unusually hgh ndowmnts of ntrprnural talnt and t s ths human captal advantag that lads thm to bcom ntrprnurs. Indd, ths ntrprtaton s consstnt wth th rcnt vdnc prsntd by xprmntal studs that fnd that th last rsk avrs ar thos who hav supror cogntv ablty. Ths xprmntal studs lctd rsk avrson usng chocs btwn a lottry and a saf paymnt. Frdrck (2005 nvstgats how cogntv ablty s rlatd to rsk atttuds usng U.S. undrgraduat studnts. To do so, h dvlopd ntllgnc tst calld Cogntv Rflcton Tst (CRT that masurs rspondnts cogntv ablty. H fnds that ndvduals n th hgh CRT-scor group ar lss rsk avrs than thos n th low CRT-scor group. 20 H stats that, th rlaton s somtms so strong that th prfrncs thmslvs ffctvly functon as xprssons of cogntv ablty (p.26. Smlarly, Bnjamn t al. (2006 nvstgat whthr varaton n rsk atttuds s rlatd to varaton n cogntv ablty masurd by Scholastc Assssmnt Tst (SAT. Thy fnd that studnts wth hghr SAT scor n Chlan hgh school show gratr 19 Panl Study of Incom Dynamcs (PSID ncluds th sam hypothtcal gambl qustons as NLSY dos. Th only dffrnt fatur of PSID from NLSY s that PSID offrs on mor gambl quston (.., ncom cut by 75%, so t allows us to ordr ndvduals nto sx catgors. Usng NLSY79 (Ahn, 2009 and PSID (Kan and Tsa, 2006; Brown, 2008, t s shown that lss rsk avrs ndvduals ar mor lkly to choos ntrprnurshp. 20 Frdrck (2005 also uss Amrcan Collg Tst (ACT, th nd for cognton scal (NFC, th Wondrlc Prsonnl Tst (WPT n ordr to chck corrlaton of ach masur. H shows that all masurs corrlat postvly and sgnfcantly wth on anothr. 21

26 wllngnss to tak rsk. Thy suggst that masurd rsk atttuds may not fully rflct tasts for rsk du to cogntv lmtatons. Mor rcntly, Dohmn t al. (2010 assss th rlaton btwn rsk atttuds and cogntv ablty usng rprsntatv Grman data. Thy dscovr that lss rsk avrs ndvduals ar thos who hav hghr cogntv ablty as masurd by tsts of word fluncy and symbolc logc. Thy concludd that cogntv ablty convys nformaton about rsk avrson. Thr s modst support for a lnk btwn rsk accptanc and cogntv ablty n th NLSY sampl. Th Armd Forcs Qualfyng Tst (AFQT, a wdlyusd proxy for masurs of skll, was admnstrd to all ndvduals n th NLSY79. Th corrlaton btwn masurd rsk accptanc and th AFQT scor s 0.04 for thos who wr slf-mployd n 1993 and 0.03 for thos who vr had start-ups btwn 1992 and In contrast, th corrlaton btwn AFQT scor and rsk accptanc s ngatv n th populaton of wag workrs. 21 As a smpl tst of th comparatv statc rlatonshp btwn cogntv ablty and rsk accptanc, w rgrss masurd rsk atttuds on AFQT scor, controllng for th othr ndvdual charactrstcs w ncludd as masurs of opportunty cost of tm. Bcaus of th possbl nonlnar masurs of rsk atttud ndx, w also ncorporat ordrd probt modl. Th rgrsson rsults ar rportd n Appndx 4. In both modls, AFQT scor has a statstcally sgnfcant and postv ffct on rsk accptanc ndx. W also rport margnal and dscrt changs n probablts for ach outcom of rsk atttuds n Appndx 5. Th probablty chang s assocatd wth a standard 21 Th NLSY79 partcpants wr rcrutd to tak th Armd Srvcs Vocatonal Apttud Battry (ASVAB tst n ASVAB conssts of tn sctons masurng sklls rlatd to acadmc and vocaton. Four of th tn sctons comprs th AFQT. Th rsults ar consstnt wth thos wth ASVAB. 22

27 dvaton ncras n th corrspondng ndpndnt varabl cntrd on th man. Th ffct of AFQT scor s small: a on standard dvaton ncras n AFQT scor ncrass th probablty of bng n th most rsk accptng catgory by just 1.4 prcntag pont. Nvrthlss, t s much largr than th statstcally nsgnfcant 0.4 prcntag pont ncras n probablty of bng n th sam catgory from an addtonal yar of schoolng. Th mpld xstnc of an unlarnd or nascnt ntrprnural skll sparat from larnd sklls s smlar to th λ n Lazar s (2005 jack-of-all-trads modl of ntrprnurshp. Along wth th xprmntal vdnc of a postv corrlaton btwn masurd rsk accptanc and cogntv ablty suggst that masurs of rsk atttuds ar n fact masurs of ntrprnural ablty or th ablty to manag rsk. Th mor confdnc agnts hav n thr own ablty, th gratr confdnc thy wll hav n thr dcsons undr uncrtanty and so gratr wllngnss to tak rsks. 6. Concluson Ths papr prsnts a standard thortcal modl of occupatonal choc undr uncrtanty n ordr to xplan mor rsk avrs agnt rqurs hghr xpctd rturn at th tm of ntry nto ntrprnurshp. Bcaus th xpctd rturn s th valu adjustd by rsk avrson, hghr xpctd rturn mpls a hghr survval probablty. From that proposton, w hypothsz that mor rsk avrs ntrprnurs hav a hghr probablty of survval than thr lss rsk avrs countrparts. Ths papr mprcally tsts th hypothss applyng a fralty hazard modl to succssv ntry cohorts of young ntrprnurs n th NLSY79. Succss s judgd as survvng at last 6 yars whch puts th vntur n th uppr thrd of th tnur of start-ups. 23

28 Surprsngly, th mprcal tsts soundly rfut th hypothss. W fnd lss rsk avrs ntrprnurs ar lss lkly to fal than ar thr mor rsk avrs countrparts. Ths fndng also holds tru whn controllng for ndvdual human captal and dmographc varabls. Furthrmor, th unxpctd rsult s not snstv to dffrnt tm wndows ovr whch to judg a vntur s succss. Whn ncorporatng rsk prfrnc dumms n our rgrsson, w stll obsrv a monotoncally ngatv rlatonshp btwn rsk accptanc and frm falur, whch s nconsstnt wth th psychologcal thory that th most succssful ntrprnurs ar ntrmdat rsk-takrs. Consquntly, our concluson that th most succssful ntrprnurs ar th last rsk avrs holds up wll. Ths surprsng fndng casts doubt on th us of masurd rsk avrson as a masur of tast for rsk. Instad, masurd wllngnss to accpt rsks bhavs mor lk an ndx of ntrprnural skll. Th last rsk avrs appar abl to mak bttr dcsons n th uncrtan conomc nvronmnt of th busnss ownr. Our ntrprtaton s supportd by rcnt xprmntal studs (Frdrck, 2005; Bnjamn t al., 2006; Burks t al., 2009; Dohmn t al., 2010 that fnd th last rsk avrs ndvduals xhbt hghr cogntv ablty. 24

29 Rfrncs Ahn, T. (2009 Atttuds toward rsk and slf-mploymnt of young workrs, Labour Economcs, 17, Atknson, J.W., and Brch, D. (1978 Introducton to Motvaton, Nw York: D. Van Nostrand. Barsky, R., Justr, F.T., Kmball, M.S., and Shapro, M.D. (1997 Prfrnc Paramtrs and Bhavoral Htrognty: An Exprmntal Approach n th Halth and Rtrmnt Study, Th Quartrly Journal of Economcs, 112 (2, Bats, T. (1990 Entrprnur Human Captal Inputs and Small Busnss Longvty, Th Rvw of Economcs and Statstcs, 72, Bnjamn, D., Brown, S., and Shapro, J. (2006 Who s Bhavoral? Cogntv Ablty and Anomalous Prfrncs, workng papr, Harvard Unvrsty. Bonn, H., Dohmn, T., Falk, A., and Huffman, D. (2007 Cross-sctonal arnngs rsk and occupatonal sortng: Th rol of rsk atttuds, Labour Economcs, 14, Brown, S., Dtrch, M., Ortz, A., and Taylor, K. (2008 Slf-mploymnt and rsk prfrnc, Th Unvrsty of Shffld, Dpartmnt of Economcs n ts srs workng papr # Burks, S.V., Carpntr, J.P., Gott, L., and Rustchn, A. (2009 Cogntv sklls affct conomc prfrncs, stratgc bhavor, and job attachmnt, Procdngs of th Natonal Acadmy of Scncs, 106 (19, Calndo, M., Fossn, F., and Krtkos, A. (2010 Th mpact of rsk atttuds on ntrprnural survval, Journal of Economc Bhavor & Organzaton, 76, Cramr, J.S., Hartog, J., Jonkr, N., and Van Praag, C.M. (2002 Low rsk avrson ncourags th choc for ntrprnurshp: an mprcal tst of a trusm, Journal of conomc bhavor & organzaton, 48, Crssy, R. (1996 Ar Busnss Startups Dbt-Ratond? Th Economc Journal, 106(438, Dohmn, T., Falk, A., Huffman, D., Sund, U. (2010 Ar rsk avrson and mpatnc rlatd to cogntv ablty? Amrcan Economc Rvw, 100, Dunn, T. and Holtz-Eakn, D. (2000 Fnancal captal, human captal, and th transton to slf-mploymnt: vdnc from ntrgnratonal lnks, Journal of Labour Economcs, 18, Dunn, T., Robrts, M.J., and Samulson, L. (1988 Pattrns of Frm Entry and Ext n U.S. Manufacturng Industrs, Th RAND Journal of Economcs, 19 (4,

30 Eklud, J., Johansson, E., Jarvln M-R, and Lchtrmann, D. (2005 Slf-mploymnt and rsk avrson-vdnc from psychologcal tst data, Labour Economcs, 12, Evans, D.S., Lghton, L. (1989 Som mprcal aspcts of ntrprnurshp, Amrcan conomc rvw, 79, Frdrck, S. (2005 Cogntv Rflcton and Dcson Makng, Th journal of Economc Prspctvs, 19(4, Gutrrz, R.G. (2002 Paramtrc fralty and shard fralty survval, Th Stata Journal, 2(1, Hamlton, B.H. (2000 Dos Entrprnurshp Pay? An Emprcal Analyss of th Rturns of Slf-Employmnt, Th Journal of Poltcal Economy, 108 (3, Hartog, J., Frrr--Carbonll, A., and Jonkr, N. (2002 Lnkng masurd rsk avrson to ndvdual charactrstcs, Kyklos, 55, Hckman, J., Sngr, B. (1984 A mthod for mnmzng th mpact of dstrbutonal assumpton n Economtrc mthods for duraton data, Economtrca, 54, Holtz-Eakn, Joulfaan, D., Rosn, H.S. (1994 Stckng t Out: Entrprnural Survval and Lqudty Constrants, Th Journal of Poltcal Economy, 102(1, Isphordng, I. E. (2010 Rsky busnss-th rol of ndvdual rsk atttuds n occupatonal choc, Ruhr conomc paprs # 187. Kan, K. and Tsa, W. (2006 Entrprnurshp and rsk avrson, Small busnss conomcs, 26, Knaup A.E. and Pazza, M.C. (2007 Busnss Employmnt Dynamcs data: survval and longvty II, Monthly Labor Rvw, Sptmbr, Lazar, Edward Entrprnurshp. Journal of Labor Economcs 23(4: Lntz, B.F. and Laband, D.N. (1990 Entrprnural Succss and Occupatonal Inhrtanc among Proprtors, Th Canadan Journal of Economcs, 23(3, Mrdth, G.G., Nlson, R.E., and Nck, P.A. (1982 Th Practc of Entrprnurshp, Gnva: Intrnatonal Labour Offc. Orazm, F.P. and Mattla, J.P. (1991 Human Captal, Uncrtan Wag Dstrbutons, and Occupatonal and Educatonal Chocs, Intrnatonal Economc Rvw, Fbruary, Pffr, C. (2008 A Not on Rsk Avrson and Labour Markt Outcoms: Furthr Evdnc from Grman Survy Data, IZA Dscusson papr No Pratt, J.W. (1964, Rsk Avrson n th Small and n th Larg, Economtrca, 32,

31 136. Rauch, A. and Frs, M. (2007 Lt s put th prson back nto ntrprnurshp rsarch: A mta-analyss on th rlatonshp btwn busnss ownrs prsonalty trats, busnss craton, and succss, Europan Journal of Work and Organzatonal Psychology, 16 (4, Taylor, M.P. (1999 Survval of th Fttst? An Analyss of Slf-Employmnt Duraton n Brtan, Th Economc Journal, 109, Confrnc Paprs, C140-C155. Van Praag, C.M. and Cramr, J.S. (2001 Th roots of ntrprnurshp and labour dmand: Indvdual ablty and low rsk avrson, Economca, 2001, Van Praag, C.M. (2003 Busnss Survval and Succss of Young Small Busnss ownrs, Small Busnss Economcs, 21, Shaw, K.L. (1996 An Emprcal Analyss of Rsk Avrson and Incom Growth Journal of Labor Economcs, 14, Strnbrg, R.J. (2004 Succssful ntllgnc as a bass for ntrprnurshp, Journal of Busnss Vnturng, 19,

32 Tabl 1 Dfntons and dscrptv statstcs of varabls Rsk accptanc ndx (1-4 Educaton Work xprnc Ag Marrd, spous prsnt Numbr of kds Mal Wht F-proprtor/managr M-proprtor/managr Urban Northast North cntral South Wst Indus1 Indus2 Indus3 Indus4 Indus5 Indus6 Indus7 Indus8 Indus9 Indus10 Indus11 Dfnton Wllngnss to tak rsk: th rsk ndx 1 ndcats th most rsk avrs and 4 mans th last rsk avrs Yars of schoolng compltd Yars of prvous labor markt mploymnt xprnc bfor slf-mploymnt Ag n yars =1 f marrd and spous prsnt Numbr of bo/stp/adoptd chldrn n houshold =1 f mal =1 f wht =1 f fathr was/s a proprtor or managr =1 f mothr was/s a proprtor or managr =1 f rsd n urban ara =1 f rsd n northast =1 f rsd n north cntral =1 f rsd n south =1 f rsd n wst =1 f Agrcultur, Forstry and Fshrs =1 f Constructon =1 f Manufacturng =1 f Wholsal trad, Rtal trad =1 f Transportaton, Communcaton, Publc Utlts =1 f Fnanc, Insuranc, and Ral Estat =1 f Busnss and Rpar Srvcs =1 f Prsonal Srvcs =1 f Entrtanmnt and Rcraton Srvcs =1 f Profssonal and rlatd srvcs =1 f Publc admnstraton Man (Std ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (0.06 Mn Max

33 Tabl 2 Sx yar survval and xt rats by ntry cohort (NLSY Ag Ag Ag Ag Ag Ag Total Survvd Dd 16 (29% 39 (71% 31 (31% 69 (69% 63 (43% 84 (57% 44 (42% 60 (58% 34 (51% 33 (49% 56 (47% 63 (53% 243 (41% 345 (59% Total (100% (100% (100% (100% (100% (100% (100% Not: Numbr of obsrvatons s rportd wth prcntag n parnthss. Survval s masurd basd on 6 yar longvty n busnss. 29

34 Tabl 3 Dstrbuton of rsk avrson by ntry cohort (% NLSY79 Rsk accptanc ndx ovrall 1: Most rsk avrs 43 % 52 % 35 % 34 % 39 % 43 % 40 % 2 17 % 9 % 12 % 14 % 7 % 11 % 11 % 3 7 % 13 % 24 % 22 % 24 % 25 % 21 % 4: Last rsk avrs 33 % 26 % 29 % 30 % 30 % 21 % 28 % Total Obs

35 Tabl 4 Rgrssons xplanng probablty of falur from fralty hazard and Cox proportonal modl Panl A (1 Fralty Hazard (2 Fralty Hazard (3 Cox proportonal (4 Fralty Hazard Rsk accptanc ndx( (-2.35 b (-2.08 b (-1.66 c (-1.88 c Prvous Labor markt xprnc (n yars 0.919(-3.99 a (-3.33 a (-3.84 a Educaton ( ( (0.68 Educaton squard Mal Marrd, spous prsnt Numbr of Kds Wht Ag Ag squard Urban Fathr-proprtor Mothr-proprtor ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (-2.31 b (-1.81 c (-2.31 b (2.26 b (1.76 c (2.25 b ( ( ( ( ( ( ( ( (-0.95 Entry cohort dumms Ys Ys Ys Ys 4 rgon dumms Ys Ys Ys Industry dumms Ys θ (fralty varanc [1.936] a 5.578[2.344] b [2.300] a p (ancllary paramtr [0.134] a [0.147] a [0.147] a Log lklhood N Lklhood Rato (LR tst (6=31.55 a 2 (22= a a (22=48.93 (33=78.64 a LR tst for θ = 0 (1=45.1 a Panl B Wllngnss to tak mdum rsk (1=38.6 a (1=38.55 a 0.71 ( ( ( (-1.00 Wllngnss to tak hgh rsk 0.62 (-2.30 b 0.65 (-2.01 b 0.81 ( (-1.89 c Not: Hazard ratos ar xponntatd coffcnts. t-statstcs ar rportd n parnthss. Standard rrors ar n brackts. Rsk prfrnc ndx 1 ndcats th most rsk avrs. a/b/c sgnfcanc lvl at 1%/5%/10%. Th null dstrbuton of th LR tst statstc s a 50:50 mxtur of a χ 2 wth zro dgr of frdom and a χ 2 2 wth on dgr of frdom, dnotd as χ (1. Controls usd n Panl B rgrssons 31

36 ar th sam as n Panl A. Tabl 5 Snstvty of rsk atttuds: Ngatv rlatonshp btwn rsk atttuds and hazard Rsk accptanc ndx Panl A Man stmaton: 6 yar prod (1 Fralty hazard (2 Fralty hazard (3 Cox proportonal (4 Fralty hazard Sampl sz (-2.35 b (-2.08 b (-1.66 c (-1.88 c yar prod ( ( ( ( yar prod Panl B 2002 rsk prfrnc (-2.33 b (-2.09 b (-1.66 c (-1.97 b ( ( ( ( rsk prfrnc ( ( ( ( rsk prfrnc Panl C Omttng 1992 cohort wth 1993 rsk prfrnc Panl D Slf-mployd n 2002 Wth 1993 rsk prfrnc ( ( ( ( (-2.53 b (-2.61 a (-2.18 b (-2.40 b ( ( ( (0.10 Not: Top numbr s th stmatd hazard rato for th rsk atttud ndx whr frm succss s masurd undr altrnatv tm wndows. Th Rsk ndx vars from 1: most rsk avrs to 4: last rsk avrs. Columns corrspond to th spcfcatons usd n th corrspondng columns n Tabl 4. t- statstcs rportd n parnthss. a/b/c sgnfcanc lvl at 1%/5%/10%

Analyzing Frequencies

Analyzing Frequencies Frquncy (# ndvduals) Frquncy (# ndvduals) /3/16 H o : No dffrnc n obsrvd sz frquncs and that prdctd by growth modl How would you analyz ths data? 15 Obsrvd Numbr 15 Expctd Numbr from growth modl 1 1 5

More information

Review - Probabilistic Classification

Review - Probabilistic Classification Mmoral Unvrsty of wfoundland Pattrn Rcognton Lctur 8 May 5, 6 http://www.ngr.mun.ca/~charlsr Offc Hours: Tusdays Thursdays 8:3-9:3 PM E- (untl furthr notc) Gvn lablld sampls { ɛc,,,..., } {. Estmat Rvw

More information

Chapter 6 Student Lecture Notes 6-1

Chapter 6 Student Lecture Notes 6-1 Chaptr 6 Studnt Lctur Nots 6-1 Chaptr Goals QM353: Busnss Statstcs Chaptr 6 Goodnss-of-Ft Tsts and Contngncy Analyss Aftr compltng ths chaptr, you should b abl to: Us th ch-squar goodnss-of-ft tst to dtrmn

More information

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous ST 54 NCSU - Fall 008 On way Analyss of varanc Varancs not homognous On way Analyss of varanc Exampl (Yandll, 997) A plant scntst masurd th concntraton of a partcular vrus n plant sap usng ELISA (nzym-lnkd

More information

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP ISAHP 00, Bal, Indonsa, August -9, 00 COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP Chkako MIYAKE, Kkch OHSAWA, Masahro KITO, and Masaak SHINOHARA Dpartmnt of Mathmatcal Informaton Engnrng

More information

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D Comp 35 Introducton to Machn Larnng and Data Mnng Fall 204 rofssor: Ron Khardon Mxtur Modls Motvatd by soft k-mans w dvlopd a gnratv modl for clustrng. Assum thr ar k clustrs Clustrs ar not rqurd to hav

More information

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization THE UNIVERSITY OF MARYLAND COLLEGE PARK, MARYLAND Economcs 600: August, 007 Dynamc Part: Problm St 5 Problms on Dffrntal Equatons and Contnuous Tm Optmzaton Quston Solv th followng two dffrntal quatons.

More information

te Finance (4th Edition), July 2017.

te Finance (4th Edition), July 2017. Appndx Chaptr. Tchncal Background Gnral Mathmatcal and Statstcal Background Fndng a bas: 3 2 = 9 3 = 9 1 /2 x a = b x = b 1/a A powr of 1 / 2 s also quvalnt to th squar root opraton. Fndng an xponnt: 3

More information

Logistic Regression I. HRP 261 2/10/ am

Logistic Regression I. HRP 261 2/10/ am Logstc Rgrsson I HRP 26 2/0/03 0- am Outln Introducton/rvw Th smplst logstc rgrsson from a 2x2 tabl llustrats how th math works Stp-by-stp xampls to b contnud nxt tm Dummy varabls Confoundng and ntracton

More information

A Note on Estimability in Linear Models

A Note on Estimability in Linear Models Intrnatonal Journal of Statstcs and Applcatons 2014, 4(4): 212-216 DOI: 10.5923/j.statstcs.20140404.06 A Not on Estmablty n Lnar Modls S. O. Adymo 1,*, F. N. Nwob 2 1 Dpartmnt of Mathmatcs and Statstcs,

More information

Fakultät III Univ.-Prof. Dr. Jan Franke-Viebach

Fakultät III Univ.-Prof. Dr. Jan Franke-Viebach Unv.Prof. r. J. FrankVbach WS 067: Intrnatonal Economcs ( st xam prod) Unvrstät Sgn Fakultät III Unv.Prof. r. Jan FrankVbach Exam Intrnatonal Economcs Wntr Smstr 067 ( st Exam Prod) Avalabl tm: 60 mnuts

More information

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn.

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn. Modul 10 Addtonal Topcs 10.1 Lctur 1 Prambl: Dtrmnng whthr a gvn ntgr s prm or compost s known as prmalty tstng. Thr ar prmalty tsts whch mrly tll us whthr a gvn ntgr s prm or not, wthout gvng us th factors

More information

Econ107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13)

Econ107 Applied Econometrics Topic 10: Dummy Dependent Variable (Studenmund, Chapter 13) Pag- Econ7 Appld Economtrcs Topc : Dummy Dpndnt Varabl (Studnmund, Chaptr 3) I. Th Lnar Probablty Modl Suppos w hav a cross scton of 8-24 yar-olds. W spcfy a smpl 2-varabl rgrsson modl. Th probablty of

More information

Outlier-tolerant parameter estimation

Outlier-tolerant parameter estimation Outlr-tolrant paramtr stmaton Baysan thods n physcs statstcs machn larnng and sgnal procssng (SS 003 Frdrch Fraundorfr fraunfr@cg.tu-graz.ac.at Computr Graphcs and Vson Graz Unvrsty of Tchnology Outln

More information

Households Demand for Food Commodities: Evidence from Kurunegala Divisional Secretarial Division, Sri Lanka

Households Demand for Food Commodities: Evidence from Kurunegala Divisional Secretarial Division, Sri Lanka Housholds Dmand for Food Commodts: Evdnc from Kurungala Dvsonal Scrtaral Dvson, Sr Lanka U.W.B.M. Kumar and John Ngl Dpartmnt of Economcs and Statstcs, Unvrsty of Pradnya, Sr Lanka Kywords: Houshold dmand;

More information

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D Comp 35 Machn Larnng Computr Scnc Tufts Unvrsty Fall 207 Ron Khardon Th EM Algorthm Mxtur Modls Sm-Suprvsd Larnng Soft k-mans Clustrng ck k clustr cntrs : Assocat xampls wth cntrs p,j ~~ smlarty b/w cntr

More information

A Probabilistic Characterization of Simulation Model Uncertainties

A Probabilistic Characterization of Simulation Model Uncertainties A Proalstc Charactrzaton of Sulaton Modl Uncrtants Vctor Ontvros Mohaad Modarrs Cntr for Rsk and Rlalty Unvrsty of Maryland 1 Introducton Thr s uncrtanty n odl prdctons as wll as uncrtanty n xprnts Th

More information

You already learned about dummies as independent variables. But. what do you do if the dependent variable is a dummy?

You already learned about dummies as independent variables. But. what do you do if the dependent variable is a dummy? CHATER 5: DUMMY DEENDENT VARIABLES AND NON-LINEAR REGRESSION. Th roblm of Dummy Dpndnt Varabls You alrady larnd about dumms as ndpndnt varabls. But what do you do f th dpndnt varabl s a dummy? On answr

More information

Fakultät III Wirtschaftswissenschaften Univ.-Prof. Dr. Jan Franke-Viebach

Fakultät III Wirtschaftswissenschaften Univ.-Prof. Dr. Jan Franke-Viebach Unvrstät Sgn Fakultät III Wrtschaftswssnschaftn Unv.-rof. Dr. Jan Frank-Vbach Exam Intrnatonal Fnancal Markts Summr Smstr 206 (2 nd Exam rod) Avalabl tm: 45 mnuts Soluton For your attnton:. las do not

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

Today s logistic regression topics. Lecture 15: Effect modification, and confounding in logistic regression. Variables. Example

Today s logistic regression topics. Lecture 15: Effect modification, and confounding in logistic regression. Variables. Example Today s stc rgrsson tocs Lctur 15: Effct modfcaton, and confoundng n stc rgrsson Sandy Eckl sckl@jhsh.du 16 May 28 Includng catgorcal rdctor crat dummy/ndcator varabls just lk for lnar rgrsson Comarng

More information

Physics of Very High Frequency (VHF) Capacitively Coupled Plasma Discharges

Physics of Very High Frequency (VHF) Capacitively Coupled Plasma Discharges Physcs of Vry Hgh Frquncy (VHF) Capactvly Coupld Plasma Dschargs Shahd Rauf, Kallol Bra, Stv Shannon, and Kn Collns Appld Matrals, Inc., Sunnyval, CA AVS 54 th Intrnatonal Symposum Sattl, WA Octobr 15-19,

More information

Grand Canonical Ensemble

Grand Canonical Ensemble Th nsmbl of systms mmrsd n a partcl-hat rsrvor at constant tmpratur T, prssur P, and chmcal potntal. Consdr an nsmbl of M dntcal systms (M =,, 3,...M).. Thy ar mutually sharng th total numbr of partcls

More information

Advanced Macroeconomics

Advanced Macroeconomics Advancd Macroconomcs Chaptr 18 INFLATION, UNEMPLOYMENT AND AGGREGATE SUPPLY Thms of th chaptr Nomnal rgdts, xpctatonal rrors and mploymnt fluctuatons. Th short-run trad-off btwn nflaton and unmploymnt.

More information

A primary objective of a phase II trial is to screen for antitumor activity; agents which are found to have substantial antitumor activity and an

A primary objective of a phase II trial is to screen for antitumor activity; agents which are found to have substantial antitumor activity and an SURVIVAL ANALYSIS A prmary objctv of a phas II tral s to scrn for anttumor actvty; agnts whch ar found to hav substantal anttumor actvty and an approprat spctrum of toxcty ar lkly ncorporatd nto combnatons

More information

A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION*

A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION* A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION* Dr. G.S. Davd Sam Jayakumar, Assstant Profssor, Jamal Insttut of Managmnt, Jamal Mohamd Collg, Truchraall 620 020, South Inda,

More information

Optimal Ordering Policy in a Two-Level Supply Chain with Budget Constraint

Optimal Ordering Policy in a Two-Level Supply Chain with Budget Constraint Optmal Ordrng Polcy n a Two-Lvl Supply Chan wth Budgt Constrant Rasoul aj Alrza aj Babak aj ABSTRACT Ths papr consdrs a two- lvl supply chan whch consst of a vndor and svral rtalrs. Unsatsfd dmands n rtalrs

More information

CHAPTER 33: PARTICLE PHYSICS

CHAPTER 33: PARTICLE PHYSICS Collg Physcs Studnt s Manual Chaptr 33 CHAPTER 33: PARTICLE PHYSICS 33. THE FOUR BASIC FORCES 4. (a) Fnd th rato of th strngths of th wak and lctromagntc forcs undr ordnary crcumstancs. (b) What dos that

More information

EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12

EEC 686/785 Modeling & Performance Evaluation of Computer Systems. Lecture 12 EEC 686/785 Modlng & Prformanc Evaluaton of Computr Systms Lctur Dpartmnt of Elctrcal and Computr Engnrng Clvland Stat Unvrsty wnbng@.org (basd on Dr. Ra Jan s lctur nots) Outln Rvw of lctur k r Factoral

More information

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation

Lecture 14. Relic neutrinos Temperature at neutrino decoupling and today Effective degeneracy factor Neutrino mass limits Saha equation Lctur Rlc nutrnos mpratur at nutrno dcoupln and today Effctv dnracy factor Nutrno mass lmts Saha quaton Physcal Cosmoloy Lnt 005 Rlc Nutrnos Nutrnos ar wakly ntractn partcls (lptons),,,,,,, typcal ractons

More information

8-node quadrilateral element. Numerical integration

8-node quadrilateral element. Numerical integration Fnt Elmnt Mthod lctur nots _nod quadrlatral lmnt Pag of 0 -nod quadrlatral lmnt. Numrcal ntgraton h tchnqu usd for th formulaton of th lnar trangl can b formall tndd to construct quadrlatral lmnts as wll

More information

Epistemic Foundations of Game Theory. Lecture 1

Epistemic Foundations of Game Theory. Lecture 1 Royal Nthrlands cadmy of rts and Scncs (KNW) Mastr Class mstrdam, Fbruary 8th, 2007 Epstmc Foundatons of Gam Thory Lctur Gacomo onanno (http://www.con.ucdavs.du/faculty/bonanno/) QUESTION: What stratgs

More information

Chapter 13 Aggregate Supply

Chapter 13 Aggregate Supply Chaptr 13 Aggrgat Supply 0 1 Larning Objctivs thr modls of aggrgat supply in which output dpnds positivly on th pric lvl in th short run th short-run tradoff btwn inflation and unmploymnt known as th Phillips

More information

Lecture 3: Phasor notation, Transfer Functions. Context

Lecture 3: Phasor notation, Transfer Functions. Context EECS 5 Fall 4, ctur 3 ctur 3: Phasor notaton, Transfr Functons EECS 5 Fall 3, ctur 3 Contxt In th last lctur, w dscussd: how to convrt a lnar crcut nto a st of dffrntal quatons, How to convrt th st of

More information

Naresuan University Journal: Science and Technology 2018; (26)1

Naresuan University Journal: Science and Technology 2018; (26)1 Narsuan Unvrsty Journal: Scnc and Tchnology 018; (6)1 Th Dvlopmnt o a Corrcton Mthod or Ensurng a Contnuty Valu o Th Ch-squar Tst wth a Small Expctd Cll Frquncy Kajta Matchma 1 *, Jumlong Vongprasrt and

More information

Relate p and T at equilibrium between two phases. An open system where a new phase may form or a new component can be added

Relate p and T at equilibrium between two phases. An open system where a new phase may form or a new component can be added 4.3, 4.4 Phas Equlbrum Dtrmn th slops of th f lns Rlat p and at qulbrum btwn two phass ts consdr th Gbbs functon dg η + V Appls to a homognous systm An opn systm whr a nw phas may form or a nw componnt

More information

4. Money cannot be neutral in the short-run the neutrality of money is exclusively a medium run phenomenon.

4. Money cannot be neutral in the short-run the neutrality of money is exclusively a medium run phenomenon. PART I TRUE/FALSE/UNCERTAIN (5 points ach) 1. Lik xpansionary montary policy, xpansionary fiscal policy rturns output in th mdium run to its natural lvl, and incrass prics. Thrfor, fiscal policy is also

More information

From Structural Analysis to FEM. Dhiman Basu

From Structural Analysis to FEM. Dhiman Basu From Structural Analyss to FEM Dhman Basu Acknowldgmnt Followng txt books wr consultd whl prparng ths lctur nots: Znkwcz, OC O.C. andtaylor Taylor, R.L. (000). Th FntElmnt Mthod, Vol. : Th Bass, Ffth dton,

More information

Diploma Macro Paper 2

Diploma Macro Paper 2 Diploma Macro Papr 2 Montary Macroconomics Lctur 6 Aggrgat supply and putting AD and AS togthr Mark Hays 1 Exognous: M, G, T, i*, π Goods markt KX and IS (Y, C, I) Mony markt (LM) (i, Y) Labour markt (P,

More information

Technology Gap, Efficiency, and a Stochastic Metafrontier Function

Technology Gap, Efficiency, and a Stochastic Metafrontier Function Intrnatonal Journal of Busnss and Economcs, 00, Vol., No., 87-93 Tchnology Gap, Effcncy, and a Stochastc Mtafrontr Functon Gorg E. Batts Unrsty of Nw England, Australa D. S. Prasada Rao Unrsty of Nw England,

More information

External Equivalent. EE 521 Analysis of Power Systems. Chen-Ching Liu, Boeing Distinguished Professor Washington State University

External Equivalent. EE 521 Analysis of Power Systems. Chen-Ching Liu, Boeing Distinguished Professor Washington State University xtrnal quvalnt 5 Analyss of Powr Systms Chn-Chng Lu, ong Dstngushd Profssor Washngton Stat Unvrsty XTRNAL UALNT ach powr systm (ara) s part of an ntrconnctd systm. Montorng dvcs ar nstalld and data ar

More information

Representation and Reasoning with Uncertain Temporal Relations

Representation and Reasoning with Uncertain Temporal Relations Rprsntaton and Rasonng wth Uncrtan Tmporal Rlatons Vladmr Ryaov (*) Sppo Puuronn (*) Vagan Trzyan (**) (*) Dpartmnt of Computr Scnc and Informaton Systms Unvrsty of Jyvaskyla P.O.Box 5 SF-4051 Jyvaskyla

More information

SCITECH Volume 5, Issue 1 RESEARCH ORGANISATION November 17, 2015

SCITECH Volume 5, Issue 1 RESEARCH ORGANISATION November 17, 2015 Journal of Informaton Scncs and Computng Tchnologs(JISCT) ISSN: 394-966 SCITECH Volum 5, Issu RESEARCH ORGANISATION Novmbr 7, 5 Journal of Informaton Scncs and Computng Tchnologs www.sctcrsarch.com/journals

More information

Households' self-selection in a voluntary time-of-use electricity pricing experiment

Households' self-selection in a voluntary time-of-use electricity pricing experiment Housholds' slf-slcton n a voluntary tm-of-us lctrcty prcng xprmnt Torgr Ercson * Norwgan Unvrsty of Scnc and Tchnology, Dp. of Elctrcal Powr Engnrng, N-749 Trondhm, Norway. Optonal tm-dffrntatd lctrcty

More information

Capital Allocation and International Equilibrium with Pollution Permits *

Capital Allocation and International Equilibrium with Pollution Permits * Modrn conomy 3 87-99 http://dx.do.org/.436/m..36 Publshd Onln March (http://www.scrp.org/journal/m) Captal Allocaton Intrnatonal qulbrum wth Polluton Prmts * Prr-André Jouvt Glls Rotllon conomx Unvrsty

More information

Chapter 14 Aggregate Supply and the Short-run Tradeoff Between Inflation and Unemployment

Chapter 14 Aggregate Supply and the Short-run Tradeoff Between Inflation and Unemployment Chaptr 14 Aggrgat Supply and th Short-run Tradoff Btwn Inflation and Unmploymnt Modifid by Yun Wang Eco 3203 Intrmdiat Macroconomics Florida Intrnational Univrsity Summr 2017 2016 Worth Publishrs, all

More information

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline Introucton to Ornar Dffrntal Equatons Sptmbr 7, 7 Introucton to Ornar Dffrntal Equatons Larr artto Mchancal Engnrng AB Smnar n Engnrng Analss Sptmbr 7, 7 Outln Rvw numrcal solutons Bascs of ffrntal quatons

More information

Observer Bias and Reliability By Xunchi Pu

Observer Bias and Reliability By Xunchi Pu Obsrvr Bias and Rliability By Xunchi Pu Introduction Clarly all masurmnts or obsrvations nd to b mad as accuratly as possibl and invstigators nd to pay carful attntion to chcking th rliability of thir

More information

Investing on the CAPM Pricing Error

Investing on the CAPM Pricing Error Tchnology and Invstmnt, 2017, 8, 67-82 http://www.scrp.org/journal/t ISSN Onln: 2150-4067 ISSN Prnt: 2150-4059 Invstng on th CAPM Prcng Error José Carlos d Souza Santos, Elas Cavalcant Flho Economcs Dpartmnt,

More information

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches.

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches. Subjct Chmistry Papr No and Titl Modul No and Titl Modul Tag 8/ Physical Spctroscopy / Brakdown of th Born-Oppnhimr approximation. Slction ruls for rotational-vibrational transitions. P, R branchs. CHE_P8_M

More information

EXST Regression Techniques Page 1

EXST Regression Techniques Page 1 EXST704 - Rgrssion Tchniqus Pag 1 Masurmnt rrors in X W hav assumd that all variation is in Y. Masurmnt rror in this variabl will not ffct th rsults, as long as thy ar uncorrlatd and unbiasd, sinc thy

More information

Inflation and Unemployment

Inflation and Unemployment C H A P T E R 13 Aggrgat Supply and th Short-run Tradoff Btwn Inflation and Unmploymnt MACROECONOMICS SIXTH EDITION N. GREGORY MANKIW PowrPoint Slids by Ron Cronovich 2008 Worth Publishrs, all rights rsrvd

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm

2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm . Grundlgnd Vrfahrn zur Übrtragung dgtalr Sgnal (Zusammnfassung) wt Dc. 5 Transmsson of Dgtal Sourc Sgnals Sourc COD SC COD MOD MOD CC dg RF s rado transmsson mdum Snk DC SC DC CC DM dg DM RF g physcal

More information

Polytropic Process. A polytropic process is a quasiequilibrium process described by

Polytropic Process. A polytropic process is a quasiequilibrium process described by Polytropc Procss A polytropc procss s a quasqulbrum procss dscrbd by pv n = constant (Eq. 3.5 Th xponnt, n, may tak on any valu from to dpndng on th partcular procss. For any gas (or lqud, whn n = 0, th

More information

Econometrics (10163) MTEE Fall 2010

Econometrics (10163) MTEE Fall 2010 Economtrcs 063 MTEE Fall 00 Lctur nots for Mcro conomtrc part. Man rfrnc: Grn Wllam H. 008. Economtrc analyss. Uppr Saddl Rvr N.J.: rntc Hall. 6th Edton. Wllam Nlsson Dpartmnt of Appld Economcs Unvrstat

More information

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari snbrg Modl Sad Mohammad Mahd Sadrnhaad Survsor: Prof. bdollah Langar bstract: n ths rsarch w tr to calculat analtcall gnvalus and gnvctors of fnt chan wth ½-sn artcls snbrg modl. W drov gnfuctons for closd

More information

Discrete Shells Simulation

Discrete Shells Simulation Dscrt Shlls Smulaton Xaofng M hs proct s an mplmntaton of Grnspun s dscrt shlls, th modl of whch s govrnd by nonlnar mmbran and flxural nrgs. hs nrgs masur dffrncs btwns th undformd confguraton and th

More information

SØK/ECON 535 Imperfect Competition and Strategic Interaction. In the absence of entry barriers firms cannot make supernormal profits.

SØK/ECON 535 Imperfect Competition and Strategic Interaction. In the absence of entry barriers firms cannot make supernormal profits. SØK/ECON 535 Imprfct Comptton and Stratgc Intracton ENTRY AND EXIT Lctur nots 09.10.0 Introducton In th absnc of ntry barrrs frms cannot mak suprnormal profts. Barrrs to ntry govrnmnt rgulatons tchnologcal

More information

ACOUSTIC WAVE EQUATION. Contents INTRODUCTION BULK MODULUS AND LAMÉ S PARAMETERS

ACOUSTIC WAVE EQUATION. Contents INTRODUCTION BULK MODULUS AND LAMÉ S PARAMETERS ACOUSTIC WAE EQUATION Contnts INTRODUCTION BULK MODULUS AND LAMÉ S PARAMETERS INTRODUCTION As w try to vsualz th arth ssmcally w mak crtan physcal smplfcatons that mak t asr to mak and xplan our obsrvatons.

More information

ON THE COMPLEXITY OF K-STEP AND K-HOP DOMINATING SETS IN GRAPHS

ON THE COMPLEXITY OF K-STEP AND K-HOP DOMINATING SETS IN GRAPHS MATEMATICA MONTISNIRI Vol XL (2017) MATEMATICS ON TE COMPLEXITY OF K-STEP AN K-OP OMINATIN SETS IN RAPS M FARAI JALALVAN AN N JAFARI RA partmnt of Mathmatcs Shahrood Unrsty of Tchnology Shahrood Iran Emals:

More information

Small Countries and Preferential Trade Agreements * How severe is the innocent bystander problem?

Small Countries and Preferential Trade Agreements * How severe is the innocent bystander problem? Small Countrs and Prfrntal Trad Agrmnts * How svr s th nnocnt bystandr problm? M. Ayhan Kos a and Raymond Rzman b Rvsd: 9/2/99 Prntd: 1/6/99 Abstract: Ths papr xamns th wlfar mplcatons of prfrntal trad

More information

Electrochemical Equilibrium Electromotive Force. Relation between chemical and electric driving forces

Electrochemical Equilibrium Electromotive Force. Relation between chemical and electric driving forces C465/865, 26-3, Lctur 7, 2 th Sp., 26 lctrochmcal qulbrum lctromotv Forc Rlaton btwn chmcal and lctrc drvng forcs lctrochmcal systm at constant T and p: consdr G Consdr lctrochmcal racton (nvolvng transfr

More information

Ερωτήσεις και ασκησεις Κεφ. 10 (για μόρια) ΠΑΡΑΔΟΣΗ 29/11/2016. (d)

Ερωτήσεις και ασκησεις Κεφ. 10 (για μόρια) ΠΑΡΑΔΟΣΗ 29/11/2016. (d) Ερωτήσεις και ασκησεις Κεφ 0 (για μόρια ΠΑΡΑΔΟΣΗ 9//06 Th coffcnt A of th van r Waals ntracton s: (a A r r / ( r r ( (c a a a a A r r / ( r r ( a a a a A r r / ( r r a a a a A r r / ( r r 4 a a a a 0 Th

More information

Radial Cataphoresis in Hg-Ar Fluorescent Lamp Discharges at High Power Density

Radial Cataphoresis in Hg-Ar Fluorescent Lamp Discharges at High Power Density [NWP.19] Radal Cataphorss n Hg-Ar Fluorscnt Lamp schargs at Hgh Powr nsty Y. Aura, G. A. Bonvallt, J. E. Lawlr Unv. of Wsconsn-Madson, Physcs pt. ABSTRACT Radal cataphorss s a procss n whch th lowr onzaton

More information

ph People Grade Level: basic Duration: minutes Setting: classroom or field site

ph People Grade Level: basic Duration: minutes Setting: classroom or field site ph Popl Adaptd from: Whr Ar th Frogs? in Projct WET: Curriculum & Activity Guid. Bozman: Th Watrcours and th Council for Environmntal Education, 1995. ph Grad Lvl: basic Duration: 10 15 minuts Stting:

More information

Inheritance Gains in Notional Defined Contributions Accounts (NDCs)

Inheritance Gains in Notional Defined Contributions Accounts (NDCs) Company LOGO Actuarial Tachrs and Rsarchrs Confrnc Oxford 14-15 th July 211 Inhritanc Gains in Notional Dfind Contributions Accounts (NDCs) by Motivation of this papr In Financial Dfind Contribution (FDC)

More information

On Selection of Best Sensitive Logistic Estimator in the Presence of Collinearity

On Selection of Best Sensitive Logistic Estimator in the Presence of Collinearity Amrcan Journal of Appld Mathmatcs and Statstcs, 05, Vol. 3, No., 7- Avalabl onln at http://pubs.scpub.com/ajams/3// Scnc and Educaton Publshng DOI:0.69/ajams-3-- On Slcton of Bst Snstv Logstc Estmator

More information

Construction of asymmetric orthogonal arrays of strength three via a replacement method

Construction of asymmetric orthogonal arrays of strength three via a replacement method isid/ms/26/2 Fbruary, 26 http://www.isid.ac.in/ statmath/indx.php?modul=prprint Construction of asymmtric orthogonal arrays of strngth thr via a rplacmnt mthod Tian-fang Zhang, Qiaoling Dng and Alok Dy

More information

(Upside-Down o Direct Rotation) β - Numbers

(Upside-Down o Direct Rotation) β - Numbers Amrican Journal of Mathmatics and Statistics 014, 4(): 58-64 DOI: 10593/jajms0140400 (Upsid-Down o Dirct Rotation) β - Numbrs Ammar Sddiq Mahmood 1, Shukriyah Sabir Ali,* 1 Dpartmnt of Mathmatics, Collg

More information

Search sequence databases 3 10/25/2016

Search sequence databases 3 10/25/2016 Sarch squnc databass 3 10/25/2016 Etrm valu distribution Ø Suppos X is a random variabl with probability dnsity function p(, w sampl a larg numbr S of indpndnt valus of X from this distribution for an

More information

First derivative analysis

First derivative analysis Robrto s Nots on Dirntial Calculus Chaptr 8: Graphical analysis Sction First drivativ analysis What you nd to know alrady: How to us drivativs to idntiy th critical valus o a unction and its trm points

More information

Multivariate Linear and Non-Linear Causality Tests

Multivariate Linear and Non-Linear Causality Tests Th Thal Economtrcs Soct Vol. No. (Januar ) 59-68 Multvarat nar Non-nar Causalt Tsts Zhdong Ba a Wng-Kung Wong b Bngzh Zhang c a School of Mathmatcs Statstcs Northast Normal Unvrst Chna; Dpartmnt of Statstcs

More information

FEFF and Related Codes

FEFF and Related Codes FEFF and Rlatd Cods Anatoly Frnl Profssor Physcs Dpartmnt, Yshva Unvrsty, w Yor, USA Synchrotron Catalyss Consortum, Broohavn atonal Laboratory, USA www.yu.du/faculty/afrnl Anatoly.Frnl@yu.du FEFF: John

More information

Estimation of apparent fraction defective: A mathematical approach

Estimation of apparent fraction defective: A mathematical approach Availabl onlin at www.plagiarsarchlibrary.com Plagia Rsarch Library Advancs in Applid Scinc Rsarch, 011, (): 84-89 ISSN: 0976-8610 CODEN (USA): AASRFC Estimation of apparnt fraction dfctiv: A mathmatical

More information

I T L S WORKING PAPER ITLS-WP (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions

I T L S WORKING PAPER ITLS-WP (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions I T L S WORING PPER ITLS-WP-07-0 Th multpl dscrtcontnuous trm valu DCE modl: Rol of utlty functon paramtrs dntfcaton consdratons and modl tnsons By Chandra R. Unvrsty of Tas at ustn Novmbr 007 ISSN 8-570X

More information

Guo, James C.Y. (1998). "Overland Flow on a Pervious Surface," IWRA International J. of Water, Vol 23, No 2, June.

Guo, James C.Y. (1998). Overland Flow on a Pervious Surface, IWRA International J. of Water, Vol 23, No 2, June. Guo, Jams C.Y. (006). Knmatc Wav Unt Hyrograph for Storm Watr Prctons, Vol 3, No. 4, ASCE J. of Irrgaton an Dranag Engnrng, July/August. Guo, Jams C.Y. (998). "Ovrlan Flow on a Prvous Surfac," IWRA Intrnatonal

More information

A Panel Data Analysis of Code Sharing, Antitrust Immunity and Open Skies. Treaties in International Aviation Markets. W. Tom Whalen 1.

A Panel Data Analysis of Code Sharing, Antitrust Immunity and Open Skies. Treaties in International Aviation Markets. W. Tom Whalen 1. A Panl Data Analyss of Cod Sharng, Anttrust Immunty and Opn Sks Trats n Intrnatonal Avaton Markts by W. Tom Whaln May 6, 2005 Abstract Ths papr stmats th ffcts of cod sharng, anttrust mmunty and Opn Sks

More information

JEE-2017 : Advanced Paper 2 Answers and Explanations

JEE-2017 : Advanced Paper 2 Answers and Explanations DE 9 JEE-07 : Advancd Papr Answrs and Explanatons Physcs hmstry Mathmatcs 0 A, B, 9 A 8 B, 7 B 6 B, D B 0 D 9, D 8 D 7 A, B, D A 0 A,, D 9 8 * A A, B A B, D 0 B 9 A, D 5 D A, B A,B,,D A 50 A, 6 5 A D B

More information

Nan Hu. School of Business, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ U.S.A. Paul A.

Nan Hu. School of Business, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ U.S.A. Paul A. RESEARCH ARTICLE ON SELF-SELECTION BIASES IN ONLINE PRODUCT REVIEWS Nan Hu School of Busnss, Stvns Insttut of Tchnology, 1 Castl Pont Trrac, Hobokn, NJ 07030 U.S.A. {nhu4@stvns.du} Paul A. Pavlou Fox School

More information

Lecture 23 APPLICATIONS OF FINITE ELEMENT METHOD TO SCALAR TRANSPORT PROBLEMS

Lecture 23 APPLICATIONS OF FINITE ELEMENT METHOD TO SCALAR TRANSPORT PROBLEMS COMPUTTION FUID DYNMICS: FVM: pplcatons to Scalar Transport Prolms ctur 3 PPICTIONS OF FINITE EEMENT METHOD TO SCR TRNSPORT PROBEMS 3. PPICTION OF FEM TO -D DIFFUSION PROBEM Consdr th stady stat dffuson

More information

The Matrix Exponential

The Matrix Exponential Th Matrix Exponntial (with xrciss) by D. Klain Vrsion 207.0.05 Corrctions and commnts ar wlcom. Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial of A to b th matrix A A k I + A + k!

More information

The Fourier Transform

The Fourier Transform /9/ Th ourr Transform Jan Baptst Josph ourr 768-83 Effcnt Data Rprsntaton Data can b rprsntd n many ways. Advantag usng an approprat rprsntaton. Eampls: osy ponts along a ln Color spac rd/grn/blu v.s.

More information

Solution of Assignment #2

Solution of Assignment #2 olution of Assignmnt #2 Instructor: Alirza imchi Qustion #: For simplicity, assum that th distribution function of T is continuous. Th distribution function of R is: F R ( r = P( R r = P( log ( T r = P(log

More information

NON-SYMMETRY POWER IN THREE-PHASE SYSTEMS

NON-SYMMETRY POWER IN THREE-PHASE SYSTEMS O-YMMETRY OWER THREE-HAE YTEM Llana Marlna MATCA nvrsty of Orada, nvrstat str., no., 487, Orada; lmatca@uorada.ro Abstract. For thr-phas lctrcal systms, n non-symmtrcal stuaton, an analyz mthod costs on

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

Function Spaces. a x 3. (Letting x = 1 =)) a(0) + b + c (1) = 0. Row reducing the matrix. b 1. e 4 3. e 9. >: (x = 1 =)) a(0) + b + c (1) = 0

Function Spaces. a x 3. (Letting x = 1 =)) a(0) + b + c (1) = 0. Row reducing the matrix. b 1. e 4 3. e 9. >: (x = 1 =)) a(0) + b + c (1) = 0 unction Spacs Prrquisit: Sction 4.7, Coordinatization n this sction, w apply th tchniqus of Chaptr 4 to vctor spacs whos lmnts ar functions. Th vctor spacs P n and P ar familiar xampls of such spacs. Othr

More information

Unit 7 Introduction to Analysis of Variance

Unit 7 Introduction to Analysis of Variance PubHlth 640 Sprng 04 7. Introducton to Analyss of Varanc Pag of 8 Unt 7 Introducton to Analyss of Varanc Always graph rsults of an analyss of varanc - Grald van Bll. Analyss of varanc s a spcal cas of

More information

The Matrix Exponential

The Matrix Exponential Th Matrix Exponntial (with xrciss) by Dan Klain Vrsion 28928 Corrctions and commnts ar wlcom Th Matrix Exponntial For ach n n complx matrix A, dfin th xponntial of A to b th matrix () A A k I + A + k!

More information

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time.

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time. Elctroncphalography EEG Dynamc Causal Modllng for M/EEG ampltud μv tm ms tral typ 1 tm channls channls tral typ 2 C. Phllps, Cntr d Rchrchs du Cyclotron, ULg, Blgum Basd on slds from: S. Kbl M/EEG analyss

More information

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION

CHAPTER 7d. DIFFERENTIATION AND INTEGRATION CHAPTER 7d. DIFFERENTIATION AND INTEGRATION A. J. Clark School o Engnrng Dpartmnt o Cvl and Envronmntal Engnrng by Dr. Ibrahm A. Assakka Sprng ENCE - Computaton Mthods n Cvl Engnrng II Dpartmnt o Cvl and

More information

Application of Local Influence Diagnostics to the Linear Logistic Regression Models

Application of Local Influence Diagnostics to the Linear Logistic Regression Models Dhaka Unv. J. Sc., 5(): 6978 003(July) Applcaton of Local Influnc Dagnostcs to th Lnar Logstc Rgrsson Modls Monzur Hossan * and M. Ataharul Islam Dpartmnt of Statstcs, Unvrsty of Dhaka Rcvd on 5.0.00.

More information

dr Bartłomiej Rokicki Chair of Macroeconomics and International Trade Theory Faculty of Economic Sciences, University of Warsaw

dr Bartłomiej Rokicki Chair of Macroeconomics and International Trade Theory Faculty of Economic Sciences, University of Warsaw dr Bartłomij Rokicki Chair of Macroconomics and Intrnational Trad Thory Faculty of Economic Scincs, Univrsity of Warsaw dr Bartłomij Rokicki Opn Economy Macroconomics Small opn conomy. Main assumptions

More information

Hostel Occupancy Survey (YHOS) Methodology

Hostel Occupancy Survey (YHOS) Methodology Hostl Occupancy Survy (HOS) Mthodology March 205 Indx rsntaton 3 2 Obctvs 4 3 Statstcal unt 5 4 Survy scop 6 5 fnton of varabls 7 6 Survy frawork and sapl dsgn 9 7 Estators 0 8 Inforaton collcton 3 9 Coffcnts

More information

HORIZONTAL IMPEDANCE FUNCTION OF SINGLE PILE IN SOIL LAYER WITH VARIABLE PROPERTIES

HORIZONTAL IMPEDANCE FUNCTION OF SINGLE PILE IN SOIL LAYER WITH VARIABLE PROPERTIES 13 th World Confrnc on Earthquak Engnrng Vancouvr, B.C., Canada August 1-6, 4 Papr No. 485 ORIZONTAL IMPEDANCE FUNCTION OF SINGLE PILE IN SOIL LAYER WIT VARIABLE PROPERTIES Mngln Lou 1 and Wnan Wang Abstract:

More information

Higher order derivatives

Higher order derivatives Robrto s Nots on Diffrntial Calculus Chaptr 4: Basic diffrntiation ruls Sction 7 Highr ordr drivativs What you nd to know alrady: Basic diffrntiation ruls. What you can larn hr: How to rpat th procss of

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

BLOCKS REPLICATION EXPERIMENTAL UNITS RANDOM VERSUS FIXED EFFECTS

BLOCKS REPLICATION EXPERIMENTAL UNITS RANDOM VERSUS FIXED EFFECTS DESIGN CONCEPTS: BLOCKS REPLICATION EXPERIMENTAL UNITS RANDOM VERSUS FIXED EFFECTS TREATMENT DESIGNS (PLANS) VS. EXPERIMENTAL DESIGNS Outln: Blockd dsgns Random Block Effcts REML analyss Incomplt Blocks

More information

Evaluating Farm-Level Crop Insurance Demand in China: A Double-Bounded Dichotomous Approach

Evaluating Farm-Level Crop Insurance Demand in China: A Double-Bounded Dichotomous Approach Journal of Agrcultural Scnc; Vol. 8, No. 3; 2016 ISSN 1916-9752 E-ISSN 1916-9760 Publshd by Canadan Cntr of Scnc and Educaton Evaluatng Farm-Lvl Crop Insuranc Dmand n Chna: A Doubl-Boundd Dchotomous Approach

More information

CS 361 Meeting 12 10/3/18

CS 361 Meeting 12 10/3/18 CS 36 Mting 2 /3/8 Announcmnts. Homwork 4 is du Friday. If Friday is Mountain Day, homwork should b turnd in at my offic or th dpartmnt offic bfor 4. 2. Homwork 5 will b availabl ovr th wknd. 3. Our midtrm

More information