Demand Systems Based on Regular Ratio Indirect Utility Functions
|
|
- Miranda Goodwin
- 5 years ago
- Views:
Transcription
1 Demand Systems Based on Regular Rato Indret Utlty Funtons by Russel J. Cooper Department of Eonoms Dvson of Eonom and Fnanal Studes Maquare Unversty NSW 2109 Australa Emal: and Keth R. MLaren Department of Eonometrs and Busness Statsts Monash Unversty Clayton, Vtora 3800 Australa Emal: June 2006
2 Abstrat Empral demand analyss s usually onduted n the ontext of the perennal tradeoff between regularty and flexblty. The lass of known globally regular demand systems s qute small and omes at the pre of nflexblty. At the other extreme are demand systems suh as Translog or the Almost Ideal Demand Systems desgnated as loally flexble, n that they do not put any pror restrtons on slopes (or elasttes, other than those mposed by the regularty ondtons, at the pont of approxmaton. The ost of ths flexblty at a pont s that these systems usually exhbt only small regons of regularty about the pont of approxmaton. A onvenent ompromse between these two extremes s the lass of effetvely globally regular demand systems, whh preserve regularty n an nterestng unbounded regon. Lewbel extended Gorman s defnton of rank to non-aggregable systems, and also showed that rank s equvalent to the mnmum number of pre ndes n the ndret utlty funton. The purpose of ths paper s to ntrodue a new demand system that s effetvely globally regular, potentally loally flexble, and of potentally arbtrary rank. 2
3 1 Introduton Empral demand analyss s usually onduted n the ontext of the perennal tradeoff between regularty and flexblty. A demand system s sad to be regular f t satsfes the restrtons mposed by the paradgm of ratonal onsumer hoe.e. f t s onsstent wth the paradgm of maxmzng utlty subjet to a budget onstrant. In the ontext of Marshallan (ordnary demand systems, ths means that the demand systems expressng quanttes demanded as funtons of expendture and pres satsfy the propertes of non-negatvty, homogenety, Engel aggregaton, Cournot aggregaton, and the symmetry and non-negatve defnteness of the Slutsky matrx. A system s sad to be globally regular f t s regular for all non-negatve values of expendture and pres. The lass of known globally regular demand systems s qute small (Cobb-Douglas, CES, Indret Addlog,? and omes at the pre of nflexblty. For example, n a Cobb-Douglas system nome, own-pre and rosspre elasttes are a pror onstraned to be +1, -1 and zero, respetvely. At the other extreme are demand systems suh as the Translog (see Chrstensen et al (1975 or the Almost Ideal Demand Systems (AIDS (see Deaton and Muellbauer (1980 desgnated as loally flexble, n that they do not put any pror restrtons on slopes (or elasttes, other than those mposed by the regularty ondtons, at the pont of approxmaton. That s, they possess just enough free parameters to approxmate any theoretally possble elastty at the gven pont. The ost of ths flexblty at a pont s that these systems usually exhbt small regons of regularty about the pont of approxmaton. A onvenent ompromse between these two extremes s the lass of effetvely globally regular demand systems, n the sense of Cooper and MLaren (1996. By effetvely globally regular s meant that there exsts a pre ndex P( p suh that the regularty propertes are satsfed for all expendture-pre ombnatons satsfyng P ( p. Thus the regularty regon s an unbounded regon n pre-expendture spae, potentally nludng all ponts n the sample, and all ponts orrespondng to hgher levels of real nome. A well-known example of suh a system s the Lnear Expendture system, whh s regular over an unbounded regon but not flexble at a pont. Suh systems an also be flexble at a pont, suh as the orgnal MAIDS of Cooper and MLaren (1992. Another nterestng haraterst of demand systems s ther rank. Gorman (1981 defned the rank of a demand system as the dmenson of the spae spanned by ts Engel urves. For example a system has rank one f and only f t s homothet, n whh ase all nome elasttes are unty. Gorman showed that exatly aggregable regular demand systems have maxmum rank of three. Lewbel (1991 extended the defnton of rank to non-aggregable systems, and also showed that rank s equvalent to the mnmum number of pre ndes n the ndret utlty funton. Thus the Cobb-Douglas system s rank one, whle the LES, AIDS and MAIDS are rank two. Nonparametr tests suh as Banks, Blundell and Lewbel (1997 suggest that rank should be at least three, and ths led them to generalse AIDS to the Quadrat Almost Ideal Demand System (QUAIDS. Lewbel (2003 extends QUAIDS to a rank four demand system. But lke AIDS, nether QUAIDS nor Lewbel s rank four system s effetvely globally regular. 3
4 The purpose of ths paper s to ntrodue a new demand system that s effetvely globally regular, potentally loally flexble, and of potentally arbtrary rank. 2 The Rank of Demand Systems Let > 0 denote a level of expendture (ost and let p be an n-vetor of non-negatve pres. Let S(, p denote an n-vetor of shares. Then followng Lewbel (2003 a demand system an be wrtten n the form m k k S(, p = A ( p f (, p k = 1 (2.1 for some and the k m n, where the f (, p are salar funtons of expendture and pres, k A ( p are n-vetors of funtons of pres. Defne the n m matrx 1 2 m Ap = A( p, A( p,, A( p. Generalzng Gorman (1981, Lewbel (1991 defnes the rank of a demand system to be the maxmum rank, over all possble pre vetors p, of the matrx Ap. Lewbel (1991 then shows that f the system (2.1 s onsstent wth utlty maxmzaton, then the rank m wll also be the mnmum number of salar funtons (pre ndes Ak ( p suh that the ndret utlty funton an be wrtten n the form [ ] U, p = U, A( p,, A ( p. (2.2 Gorman (1981 was onerned wth the ase where (2.1 an be wrtten n the restrted form 1 m m k k S, p = A ( p g (. k = 1 (2.3 He defned suh systems to be exatly aggregable, and proved that homogenety mples that suh systems have maxmum rank of three. There s no suh restrton on the rank defned aordng to (2.1. However t s the ase that a rank of m<n mposes restrtons on a demand system. 3 The Class of Regular Rato Indret Utlty Funtons A system of Marshallan demand equatons wll be regular over the regon Ω, an n+1 dmensonal subset of expendture-pre spae, f the orrespondng ndret utlty funton satsfes the followng ondtons n the regon Ω : (, p RIU:V s non-dereasng n expendture ; non-nreasng n pres p; homogeneous of degree zero n p and ; quas-onvex n p. 4
5 The lass of Regular Rato 1 Indret Utlty funtons s spefed as havng the general form of (, U p (, (, V p = (3.1 W p where the omponent funtons have the followng regularty propertes n a regon Ω of the n+1 dmensonal postve orthant: (, p RV:V s postve; non-dereasng n expendture ; non-nreasng n pres p; homogeneous of degree zero n p and ; onvex n p. RW: W (, p s postve; non-nreasng n ; non-dereasng n p; homogeneous of degree zero n p and ; onave n p. Struture (3.1 has an attratve ntutve appeal. Roy s Identty appled to any ndret utlty funton wll n general generate demand equatons or shares as rato forms (ratos of partal dervatves of U, exept n the speal ases when the denomnator ollapses to a onstant. Roy s Identty appled to (3.1 also generates demand equatons or shares as rato forms (ratos of ombnatons of partal dervatves of V and W. Our man result s the followng. Theorem 1: Provded the two omponent funtons V and W satsfy these propertes RV and RW, respetvely, then, n the regon Ω, the orrespondng ndret utlty funton U defned by (3.1 wll satsfy the regularty propertes RIU of an ndret utlty funton. In addton, U wll be postve n the regon Ω. Proof: Homogenety of degree zero follows by onstruton. The monotonty property follows by straghtforward dfferentaton and from the postvty and V, p and W, p. The urvature property follows monotonty propertes of from the fat that the rato of a postve onvex funton to a postve onave funton s quas-onvex, a result that s dsussed more fully and proved n Lemma 1 n the appendx. The power of the above onstruton follows from the followng well-known propertes: postve lnear ombnatons of postve non-nreasng onvex funtons are postve non-nreasng onvex funtons; postve lnear ombnatons of postve non-dereasng onave funtons are postve non-dereasng onave funtons. j Thus f V, p, 1,, n satsfy propertes RV, f W, p, j = 1, m satsfy RW, = 1 In earler versons, the adjetve ratonal was used n plae of rato, and had a ertan appeal. However, tehnally, a ratonal funton s a rato of polynomals, and we do not want to neessarly restrt the onsttuent funtons to be polynomals. Further, n the demand systems lterature the adjetve ratonal s also used n an alternatve sense to desrbe ratonal onsumer hoe, as above, and hene would ause unneessary onfuson. It s also worth notng that when the word ratonal s used n ths sense n the onsumer demand lterature, t often appears to mean onssteny wth the addng up and homogenety ondtons only, gnorng the other mplatons of utlty maxmzaton. 5
6 and f the onstants θ, = 1,, n, and π, j = 1,, m satsfy 0 θ 1, 0 π 1 then j n = 1 (, = U p m j= 1 θ V j π W (, p j (, p (3.2 satsfes propertes RIU. Thus the spefaton (3.1 defnes a whole lass of regular ndret utlty funtons, and the above result mples that t s possble to onstrut demand systems wth arbtrary rank, n the sense of Lewbel (1991. Agan, the ntuton s nformatve. Use of lnear ombnatons of funtons s a onvenent and smple way to aheve nreased rank. But sne quas-onvexty s not preserved when takng lnear ombnatons, t s approprate to take lnear ombnatons of omponent funtons whose (more restrtve propertes are preserved under postve lnear ombnatons. In addton, t should be relatvely easy to hoose funtonal forms n suh a way that the resultng ndret utlty funton s effetvely globally regular, n the sense of Cooper and MLaren (1996. By effetvely globally regular s meant that there exsts a pre ndex P( p suh that the regularty propertes are satsfed for all expendture-pre ombnatons satsfyng P( p. Provded ths ondton s 0 0 satsfed at some base expendture-pre ombnaton, say P( p, then the regularty ondtons wll be satsfed for the entre sample and for typal post-sample analyss whh usually orresponds to evaluaton at hgher levels of real expendture, n the above sense. Thus, by onstruton, the regular regon Ω exludes only unnterestng expendture-pre ombnatons. As an llustraton of the power of ths result, the Lnear Expendture System s an example of a demand system that s effetvely globally regular. In the usual notaton of the lnear expendture system, P( p = γp and the regular regon s the set of expendtures and pres that satsfy > γ p. A onvenent and ntutvely appealng way to onstrut funtons to use as buldng bloks n (3.2 s to buld them up from funtons of varous defntons of real nome, defned as, where the funtons P( p are defned for all nonnegatve pres, and satsfy the regularty propertes of a unt ost funton: non- P ( p negatvty; non-dereasng n pres; homogeneous of degree one n pres; onave n pres. In addton, these funtons are typally normalzed to unty at base perod pres: P (1 = 1. Ths proedure wll be llustrated n the next seton. Alternatvely, t s sometmes onvenent to explot the homogenety propertes, and defne funtons n terms of the n-dmensonal spae of normalzed pres. For example, by homogenety of degree zero, V (, p ( 1, p ˆ ˆ ( p = V V V ( r = =,say. 6
7 Usng analogous defntons, the orrespondng result s: f Vˆ ( r s postve, nonnreasng and onvex n normalzed pres r, and f Wˆ ( r s postve, non-dereasng and onave n normalzed pres r, f Uˆ r Vˆ r = (3.3 Wˆ r then Uˆ ( r s postve, non-nreasng and quas-onvex n normalzed pres r. For example, the Lnear Expendture System orresponds to an ndret utlty funton of the form κ A( p B p, κ > 0 (3.4 where A s lnear and B s Cobb-Douglas. (In the usual notaton κ = γ s a normalzng onstant that allows the pre ndex Ap to have the same base as the omponent pres. A natural queston s whether ths an be generalzed to the ase n whh A and B are arbtrary unt ost funtons. Now (3.4 s not mmedately n the form of (3.1. However, normalzng by n numerator and denomnator, then (3.4 above an be wrtten as 1 κar whh s n the form of (3.3, leadng to an affrmatve answer, provded the >κ A p, the ondton for effetve global B r numerator s postve.e. provded regularty. Another alternatve way to onstrut regular ndret utlty funtons from (two omponent funtons s the followng. Theorem 2. Let 1 (, p and V 2 (, p utlty funton onstruted as V satsfy propertes RV. Then the ndret 1 2 (, = (, (, U p V p V p (3.5 s postve and satsfes the regularty ondtons RIU. Proof: Defne W (, p Theorem 1 apples. = 1 V 2 (, p. Then by Lemma 2 W satsfes RW and 7
8 4 An Example of a Regular Rato Indret Utlty Funton Pursung the noton that a onvenent way to onstrut regular ndret utlty funtons s to buld them up from funtons of varous defntons of real nome, defned as nome deflated by pre ndes that satsfy the regularty propertes of a unt ost funton, onsder the followng onstruton: (, U p θ + (1 θ ln G( p = B( p ( Ap η (4.1 where the pre ndes, and A p B p G p are anddate pre ndes P( p. Ths system wll be of rank 3 n Lewbel s termnology. 4.1 Relaton to Prevous Spefatons The spefaton (4.1 s presented as a more regular alternatve to the Quadrat Almost Ideal Demands System (QUAIDS, a rank 3 demand system that orresponds to an Indret Utlty funton spefed as (, U p ln ( Ap + ln = B p G p A p whh s usually wrtten n Cost Funton form (on nverson n as (4.2 * B p u ln C( u, p = A ( p +. (4.3 1 G p u and where B( p and G( p are homogeneous of degree zero (and thus not monoton. Condtons for the requred urvature (and monotonty propertes of ether (4.2 or (4.3 are not obvous. The spefaton (4.1 also ontans MAIDS as a speal nested ase when θ= 0 and η= 1 (, ( = ln Ap ( ( B( p = U p B p ln A p. (4.4 Fnally, (4.1 nests regular homothet demands (suh as Cobb-Douglas or CES when η= 0 and when θ= 0 or The Share Equatons The share equatons an be derved as follows. Correspondng to (4.1, 8
9 η B θ (1 θ B B + +η θ + (1 θ ln 2 ( A G G U = 2η B η 1 (4.5 and η η 1 B θgp (1 θ A p B Bp (1 ln 2 ( A + G A +η θ + θ G U p = 2η B (4.6 where A = ln A( p ln p, B = ln B( p ln p, and G G( p Therefore by Roy s Identty the Marshallan share equatons are: = ln ln p. (1 θ A +θ G +η B +ηb(1 θ ln A S( p, = px( p, = G (1 θ +θ ( 1 +η +η(1 θ ln ( A G (4.7 gvng shares as weghted averages of two measures of real nome. 4.3 Regularty Propertes Homogenety Sne the pre ndes A( p, B( p and G( p ndret utlty funton as deflators of expendture, then the ndret utlty funton wll be homogeneous of degree zero. The HD1 propertes also mply A = 1, B = 1 and G = 1 and hene S 1. = Monotonty. Sne the pre ndes A( p, B( p and G( p are all HD1, and they enter the are all nondereasng, the numerator of (4.1 s nonnreasng. Hene a suffent ondton for the monotonty of the ndret utlty funton s that 0 1 and 0 ln A p 0. Ths θ η>, and that ( latter ondton wll be satsfed for all pre and expendture values that satsfy the ondton A p (4.8 whh s a one n pre-expendture spae. At least for these pre and expendture values the monotonty ondton together wth the homogenety ondton wll be 9
10 suffent to guarantee that the shares wll be bounded to the unt nterval 0 S p, Curvature. The pre ndes A( p, B( p and G( p are all onave. Now ln ( A( p s the negatve of a onave funton (an nreasng onave funton of a onave funton, and thus onvex. Not so well known s that the reproal of a postve, nreasng and onave funton s onvex, and ths property s addressed n Lemma 2. Thus by Lemma 1 a suffent ondton for the quas-onvexty of the ndret utlty funton s that 0 1, 0 1 and ln A p 0. Ths latter ondton θ <η ( wll be satsfed for all pre and expendture values that satsfy the ondton (4.8. Thus the suffent ondtons for urvature ft well wth the suffent ondtons for monotonty. 5 An Empral Applaton MAIDS an be generalzed by replang the natural logarthm of real nome by a Box-Cox transformaton, resultng n the Generalzed Exponental Form (GEF of Cooper and MLaren (1996, n whh ase the LES an be nluded as a nested ase. Sne the LES s the arhetypal example of an effetvely globally regular system, a smlar generalzaton wll be used for the empral applaton, to gve an effetvely globally regular system that nests both LES and MAIDS. 5.1 The Empral Spefaton Thus onsder the spefaton (, U p θ + (1 θ G( p = B p ( κa( p η µ µ 1 (5.1 subjet to the restrtons µ 1,0 θ 1 and η> 0. Spefaton (5.1 nests LES when θ= 0, η= 1, µ= 1, Ap lnear ( κ Ap = γ and B( p Cobb-Douglas ( = p β. When θ= 0, η= 1, µ= 1, and A and B are arbtrary (unt ost funtons the resultng spefaton s the Gorman Polar Form. When θ = 0, 0 η 1, µ= 0 the gener form of MAIDS results, and θ = 0, 0 η 1, µ 1 gves the gener form of GEF. Fnally, when 0 θ 1 eah of these models s generalzed to a rank 3 form. Correspondng to (5.1 p 10
11 η η 1 µ µ B θ (1 θ 1 B B + ( κ A( p +η θ + (1 θ 2 ( κa( p G A G U = 2η B ( κ η µ 1 B Gp (1 A p A η θ θ p B Bp µ + +η (1 2 2 θ + θ ( κa( p G A G U p = 2η B gvng the share equatons as S µ (1 θ A( κ A( p +θ ( G +η B +ηb(1 θ G = µ (1 θ ( κ A( p +θ ( 1 +η +η(1 θ G ( κa( p ( κa( p µ µ 1 µ. (5.2 µ Data As an llustratve example, we use aggregate data from the Australan Natonal Aounts, Prvate Fnal Consumpton Expendture. Quarterly data for the perod 1959:3 to 2005:2 was aggregated to annual (fnanal year data, to avod seasonal omplatons, and the possbly spurous alloaton of annual fgures to ther quarterly omponents. Aggregate date was onverted to per apta unts by dvdng by populaton. Pres were represented by mplt pre deflators, alulated as the rato of a nomnal seres to the real (han volume measure. Intally nne ategores were onstruted, but ths level of dsaggregaton would not allow estmaton of even the LES. Ths appeared to be due to the hgh orrelaton among the pre seres. Calulaton of parwse orrelatons revealed qute hgh orrelatons between many pars, wth one orrelaton as hgh as Usng the szes of these orrelatons as a gude, and estmaton of LES as a prerequste, the nne ategores were further amalgamated down to fve, for whh the LES estmates were well behaved. The resultng fve ategores are: Food; Cgarettes & tobao plus Alohol beverages plus Hotels, afes & restaurants; Clothng & footwear plus Furnshngs & h'hold equpment; Rent & other dwellng serves plus Eletrty, gas & other fuel; Other. 5.3 Results Wth the varous nestngs, and the varous hoes avalable for the pre ndes A, B and G, there s learly a large number of models that ould be estmated, espeally when allowng for the mposton of the suffeny ondtons for regularty. Sne the theme of the paper s regularty, only suh regular models wll be reported. As 11
12 usual, the LES provdes a remarkably good ft, and wll be taken as the benhmark effetvely globally regular model. In addton, two generalsatons wll be presented. Model 1 s LES. Model 2 allows η and µ to be freely estmated ondtonal on the LES pre ndes. Fnally, Model 3 allows θ to be freely estmated, and ntrodues a Cobb-Douglas pre ndex for G, agan ondtonal on the LES pre ndes. Log lkelhoods are summarzed n Table 1. η µ θ Log Lkelhood Model Model Model Table 1: Log-lkelhoods for 3 nested models As noted, the LES fts the data remarkably well. One mght have thought that freeng up η and µ would add sgnfant Engel flexblty, as well as (presumably removng the pror restrtons between nome elasttes and pre elasttes mposed by an addtve dret utlty funton, leadng to sgnfantly mproved explanatory power, but ths appears not to be the ase. On the other hand, the generalsaton to Model 3, whh mproves Engel flexblty by movng from a rank 2 to a rank 3 system, does appear to mprove ft sgnfantly. Some other nterestng summary statsts are presented n Table 2. Category R Model Model DW Model Model Table 2: Comparson of R 2 and Durbn-Watson statsts 6 Conluson The onept of rank s an nsghtful haraterst of demand systems. Ths paper has extended the lass of effetvely globally regular demand systems to ranks three and hgher. 12
13 7 Appendx The followng two results appear to be well-known, although t has been surprsngly dffult to fnd an aessble referene to an explt proof. The frst result relates to quas urvature propertes of ratos of onave/onvex funtons. The most aessble referene s probably Mangasaran (1969 who sets the result as a problem. Hs Problem 6.1.( states: Let Γ be onvex. Show that f=g/h (our notaton s quas-onvex on Γ f [{g s onvex on Γ, h>0 on Γ } or {g s onave on Γ, h<0 on Γ }] and [{g s lnear on g 0 on Γ }]. n R } or {h s onvex on Γ, 0 g on Γ } or {h s onave on Γ, Ths result s also quoted n Greenberg and Perskalla (1971, who attrbute t to Mangasaran and to three less aessble papers datng bak to 1964 and Greenberg and Perskalla was the referene used by Cooper and MLaren to prove the quas-onvexty of MAIDS (1992, 1994 and GEF (1996 demand systems. Avrel, Dewert, Shable and Zemba (1981 quote, wthout proof, a smlar result. For our purposes we need the followng. gx Lemma 1: Defne f( x = where gx s onvex and postve on Γ and hx s hx onave and postve on Γ. Then f ( x s quas-onvex and postve on Γ. Proof: Assume (a f( x1 α and (b f( x2 α. By postvty of hx, f x1 α g x1 α h x 1 and f x2 α g x2 α h x 2 and hene (a and (b mply Now ( λ gx + gx [ hx hx ] (1 λ α λ + (1 λ 0 λ ( λ + (1 λ λ + (1 λ α[ λ + (1 λ ] α ( λ + (1 λ g x1 x2 g x1 g x2 h x1 h x2 h x1 x2 where the frst nequalty follows by the onvexty of g, the seond nequalty follows by (, and the thrd nequalty follows by the onavty of h and postvty of f. Usng the two extreme nequaltes 13
14 ( (1 ( (1 whh mples ( (1 g λ x1+ λ x2 α h λ x1+ λ x2 f λ x1+ λ x2 α and hene provng the quas-onvexty of f f( x αand f( x α f λ x + (1 λ x α 2, The seond result relates to the reproal of a onave funton. Whle t s obvous that the reproal of a quas-onave funton s quas-onvex (and ve-versa, leadng to the ommon use of the reproal ndret utlty funton n dualty results, a orrespondng type of result for onave funtons appears not so well known. Agan, n Mangasaran Problem 6.4.( he states Hnt: Show frst that the reproal of a postve onave funton s a postve onvex funton, and that the reproal of a negatve onvex funton s a negatve onave funton, Ths result s then used by Mangasaran, together wth Lemma 1, to prove analogous results to Lemma 1 for the produts of onave/onvex funtons. Lemma 2: Let gx be a postve onave funton. Then f( x = 1 gx s a postve onvex funton. Proof: The j th element of the Hessan matrx of f, H f s f xx j 2 ggxx 2ggg j x xj = so 4 g f 1 g 2 H = H + g 2 3 x g x. g g 1 2 g xh x= xh x+ xg x sne xh x 0 and g 0. g g f g Therefore ( 2 Alternatve Proof: Sne g s onave Now g λ x1+ (1 λ x2 λ g( x1 + (1 λ g( x2. 14
15 1 f ( λ x1+ (1 λ x2 = g x x ( λ + (1 λ λ g x + λ (1 g( x λ + (1 λ gx gx 1 2 =λ f x + (1 λ f x. 1 2 The frst nequalty follows from reproatng the nequalty mpled by the onavty of g, and the seond nequalty from the fat that the reproal s a onvex funton when onstraned to the postve orthant. Referenes Avrel, M., W.E. Dewert, S. Shable and W.T. Zemba, Introduton to Conave and Generalzed Conave Funtons, n S. Shable and W. Zemba ed. Generalzed Conavty n Optmzaton and Eonoms Aadem Press, New York, 1981 Banks J., R. Blundell and A. Lewbel, Quadrat Engel Curves and Consumer Demand, The Revew of Eonoms and Statsts 79, 1997, pp Barnett, Wllam A. and Ousmane Sek, Rotterdam vs Almost Ideal Models: Wll the Best Demand Spefaton Please Stand Up?, Unversty of Kansas, February 12, Chrstensen, L.R., D. Jorgenson and L.J. Lau, Transendental Logarthm Utlty Funtons, Ameran Eonom Revew, 65, 1975, pp Cooper, Russel J. and Keth R. MLaren, An Emprally Orented Demand System wth Improved Regularty Propertes, Canadan Journal of Eonoms, XXV, No.3, 1992, pp Cooper, Russel J., Keth R. MLaren and Prya Parameswaran, A System of Demand Equatons Satsfyng Effetvely Global Curvature Condtons, Eonom Reord, Vol. 70, No. 208, Marh 1994, pp Cooper, Russel J. and Keth R. MLaren, A System of Demand Equatons Satsfyng Effetvely Global Regularty Condtons Revew of Eonoms and Statsts, Vol. 78, No 2, May 1996, pp Deaton, A.S. and J. Muellbauer, An Almost Ideal Demand System, Ameran Eonom Revew, 70, 1980, pp Gorman, W. M., Some Engel Curves, n Essays n the Theory and Measurement of Consumer Behavour n Honour of Sr Rhard Stone, ed. by Angus Deaton. Cambrdge: Cambrdge Unversty Press,
16 Greenberg, Harvey J. and Wllam P. Perskalla, A Revew of Quas-onvex Funtons, Operatons Researh 1971 pp Lewbel, Arthur, The Rank of Demand Systems: Theory and Nonparametr Estmaton, Eonometra 59, 1991, pp Lewbel, Arthur, A Ratonal Rank Four Demand System, Journal of Appled Eonometrs 18, 2003, pp Mangasaran, Olv L., Nonlnear Programmng. MGraw-Hll, New York,
The corresponding link function is the complementary log-log link The logistic model is comparable with the probit model if
SK300 and SK400 Lnk funtons for bnomal GLMs Autumn 08 We motvate the dsusson by the beetle eample GLMs for bnomal and multnomal data Covers the followng materal from hapters 5 and 6: Seton 5.6., 5.6.3,
More informationThe Order Relation and Trace Inequalities for. Hermitian Operators
Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence
More informationEC3075 Mathematical Approaches to Economics
EC3075 Mathematal Aroahes to Eonoms etures 7-8: Dualt and Constraned Otmsaton Pemberton & Rau haters 7-8 Dr Gaa Garno [Astle Clarke Room 4 emal: gg44] Dualt n onsumer theor We wll exose the rmal otmsaton
More informationLectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix
Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could
More informationAffine transformations and convexity
Affne transformatons and convexty The purpose of ths document s to prove some basc propertes of affne transformatons nvolvng convex sets. Here are a few onlne references for background nformaton: http://math.ucr.edu/
More informationA NOTE ON CES FUNCTIONS Drago Bergholt, BI Norwegian Business School 2011
A NOTE ON CES FUNCTIONS Drago Bergholt, BI Norwegan Busness School 2011 Functons featurng constant elastcty of substtuton CES are wdely used n appled economcs and fnance. In ths note, I do two thngs. Frst,
More informationMore metrics on cartesian products
More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of
More informationAPPENDIX A Some Linear Algebra
APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,
More informationprinceton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg
prnceton unv. F 17 cos 521: Advanced Algorthm Desgn Lecture 7: LP Dualty Lecturer: Matt Wenberg Scrbe: LP Dualty s an extremely useful tool for analyzng structural propertes of lnear programs. Whle there
More information6) Derivatives, gradients and Hessian matrices
30C00300 Mathematcal Methods for Economsts (6 cr) 6) Dervatves, gradents and Hessan matrces Smon & Blume chapters: 14, 15 Sldes by: Tmo Kuosmanen 1 Outlne Defnton of dervatve functon Dervatve notatons
More informationController Design for Networked Control Systems in Multiple-packet Transmission with Random Delays
Appled Mehans and Materals Onlne: 03-0- ISSN: 66-748, Vols. 78-80, pp 60-604 do:0.408/www.sentf.net/amm.78-80.60 03 rans eh Publatons, Swtzerland H Controller Desgn for Networed Control Systems n Multple-paet
More informationHomework Math 180: Introduction to GR Temple-Winter (3) Summarize the article:
Homework Math 80: Introduton to GR Temple-Wnter 208 (3) Summarze the artle: https://www.udas.edu/news/dongwthout-dark-energy/ (4) Assume only the transformaton laws for etors. Let X P = a = a α y = Y α
More informationInterval Valued Neutrosophic Soft Topological Spaces
8 Interval Valued Neutrosoph Soft Topologal njan Mukherjee Mthun Datta Florentn Smarandah Department of Mathemats Trpura Unversty Suryamannagar gartala-7990 Trpura Indamal: anjan00_m@yahooon Department
More informationJSM Survey Research Methods Section. Is it MAR or NMAR? Michail Sverchkov
JSM 2013 - Survey Researh Methods Seton Is t MAR or NMAR? Mhal Sverhkov Bureau of Labor Statsts 2 Massahusetts Avenue, NE, Sute 1950, Washngton, DC. 20212, Sverhkov.Mhael@bls.gov Abstrat Most methods that
More information,, MRTS is the marginal rate of technical substitution
Mscellaneous Notes on roducton Economcs ompled by eter F Orazem September 9, 00 I Implcatons of conve soquants Two nput case, along an soquant 0 along an soquant Slope of the soquant,, MRTS s the margnal
More informationSome Results on the Counterfeit Coins Problem. Li An-Ping. Beijing , P.R.China Abstract
Some Results on the Counterfet Cons Problem L An-Png Bejng 100085, P.R.Chna apl0001@sna.om Abstrat We wll present some results on the ounterfet ons problem n the ase of mult-sets. Keywords: ombnatoral
More informationUniform bounds on the 1-norm of the inverse of lower triangular Toeplitz matrices
Unform bounds on the -norm of the nverse of lower trangular Toepltz matres X Lu S MKee J Y Yuan X Y Yuan Aprl 2, 2 Abstrat A unform bound of the norm s gven for the nverse of lower trangular Toepltz matres
More informationDifference Equations
Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1
More informationn α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0
MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector
More informationPerfect Competition and the Nash Bargaining Solution
Perfect Competton and the Nash Barganng Soluton Renhard John Department of Economcs Unversty of Bonn Adenauerallee 24-42 53113 Bonn, Germany emal: rohn@un-bonn.de May 2005 Abstract For a lnear exchange
More informationON DUALITY FOR NONSMOOTH LIPSCHITZ OPTIMIZATION PROBLEMS
Yugoslav Journal of Oeratons Researh Vol 9 (2009), Nuber, 4-47 DOI: 0.2298/YUJOR09004P ON DUALITY FOR NONSMOOTH LIPSCHITZ OPTIMIZATION PROBLEMS Vasle PREDA Unversty of Buharest, Buharest reda@f.unbu.ro
More informationLecture 10 Support Vector Machines II
Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed
More informationComplement of an Extended Fuzzy Set
Internatonal Journal of Computer pplatons (0975 8887) Complement of an Extended Fuzzy Set Trdv Jyot Neog Researh Sholar epartment of Mathemats CMJ Unversty, Shllong, Meghalaya usmanta Kumar Sut ssstant
More informationLinear Approximation with Regularization and Moving Least Squares
Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...
More informationErrors for Linear Systems
Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch
More informationPOWER ON DIGRAPHS. 1. Introduction
O P E R A T I O N S R E S E A R H A N D D E I S I O N S No. 2 216 DOI: 1.5277/ord1627 Hans PETERS 1 Judth TIMMER 2 Rene VAN DEN BRINK 3 POWER ON DIGRAPHS It s assumed that relatons between n players are
More informationU.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017
U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that
More informationFirst day August 1, Problems and Solutions
FOURTH INTERNATIONAL COMPETITION FOR UNIVERSITY STUDENTS IN MATHEMATICS July 30 August 4, 997, Plovdv, BULGARIA Frst day August, 997 Problems and Solutons Problem. Let {ε n } n= be a sequence of postve
More informationThe Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction
ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also
More informationU.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016
U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and
More informationSalmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2
Salmon: Lectures on partal dfferental equatons 5. Classfcaton of second-order equatons There are general methods for classfyng hgher-order partal dfferental equatons. One s very general (applyng even to
More informationBOUNDEDNESS OF THE RIESZ TRANSFORM WITH MATRIX A 2 WEIGHTS
BOUNDEDNESS OF THE IESZ TANSFOM WITH MATIX A WEIGHTS Introducton Let L = L ( n, be the functon space wth norm (ˆ f L = f(x C dx d < For a d d matrx valued functon W : wth W (x postve sem-defnte for all
More informationHorizontal Mergers for Buyer Power
Horzontal Mergers for Buyer Power Lluís Bru a and Ramon Faulí-Oller b* Marh, 004 Abstrat: Salant et al. (1983) showed n a Cournot settng that horzontal mergers are unproftable beause outsders reat by nreasng
More informationBézier curves. Michael S. Floater. September 10, These notes provide an introduction to Bézier curves. i=0
Bézer curves Mchael S. Floater September 1, 215 These notes provde an ntroducton to Bézer curves. 1 Bernsten polynomals Recall that a real polynomal of a real varable x R, wth degree n, s a functon of
More informationEconomics 101. Lecture 4 - Equilibrium and Efficiency
Economcs 0 Lecture 4 - Equlbrum and Effcency Intro As dscussed n the prevous lecture, we wll now move from an envronment where we looed at consumers mang decsons n solaton to analyzng economes full of
More informationC/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1
C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned
More informationWelfare Properties of General Equilibrium. What can be said about optimality properties of resource allocation implied by general equilibrium?
APPLIED WELFARE ECONOMICS AND POLICY ANALYSIS Welfare Propertes of General Equlbrum What can be sad about optmalty propertes of resource allocaton mpled by general equlbrum? Any crteron used to compare
More informationCOMPLEX NUMBERS AND QUADRATIC EQUATIONS
COMPLEX NUMBERS AND QUADRATIC EQUATIONS INTRODUCTION We know that x 0 for all x R e the square of a real number (whether postve, negatve or ero) s non-negatve Hence the equatons x, x, x + 7 0 etc are not
More information1. relation between exp. function and IUF
Dualty Dualty n consumer theory II. relaton between exp. functon and IUF - straghtforward: have m( p, u mn'd value of expendture requred to attan a gven level of utlty, gven a prce vector; u ( p, M max'd
More informationBrander and Lewis (1986) Link the relationship between financial and product sides of a firm.
Brander and Lews (1986) Lnk the relatonshp between fnanal and produt sdes of a frm. The way a frm fnanes ts nvestment: (1) Debt: Borrowng from banks, n bond market, et. Debt holders have prorty over a
More informationAn (almost) unbiased estimator for the S-Gini index
An (almost unbased estmator for the S-Gn ndex Thomas Demuynck February 25, 2009 Abstract Ths note provdes an unbased estmator for the absolute S-Gn and an almost unbased estmator for the relatve S-Gn for
More informationNumerical Heat and Mass Transfer
Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and
More information(1 ) (1 ) 0 (1 ) (1 ) 0
Appendx A Appendx A contans proofs for resubmsson "Contractng Informaton Securty n the Presence of Double oral Hazard" Proof of Lemma 1: Assume that, to the contrary, BS efforts are achevable under a blateral
More informationThe Geometry of Logit and Probit
The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.
More informationChapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems
Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationHorizontal mergers for buyer power. Abstract
Horzontal mergers for buyer power Ramon Faul-Oller Unverstat d'alaant Llus Bru Unverstat de les Illes Balears Abstrat Salant et al. (1983) showed n a Cournot settng that horzontal mergers are unproftable
More informationFAULT DETECTION AND IDENTIFICATION BASED ON FULLY-DECOUPLED PARITY EQUATION
Control 4, Unversty of Bath, UK, September 4 FAUL DEECION AND IDENIFICAION BASED ON FULLY-DECOUPLED PARIY EQUAION C. W. Chan, Hua Song, and Hong-Yue Zhang he Unversty of Hong Kong, Hong Kong, Chna, Emal:
More informationModule 9. Lecture 6. Duality in Assignment Problems
Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept
More informationComparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method
Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method
More informationMaximizing the number of nonnegative subsets
Maxmzng the number of nonnegatve subsets Noga Alon Hao Huang December 1, 213 Abstract Gven a set of n real numbers, f the sum of elements of every subset of sze larger than k s negatve, what s the maxmum
More informationPerron Vectors of an Irreducible Nonnegative Interval Matrix
Perron Vectors of an Irreducble Nonnegatve Interval Matrx Jr Rohn August 4 2005 Abstract As s well known an rreducble nonnegatve matrx possesses a unquely determned Perron vector. As the man result of
More informationMaximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models
ECO 452 -- OE 4: Probt and Logt Models ECO 452 -- OE 4 Maxmum Lkelhood Estmaton of Bnary Dependent Varables Models: Probt and Logt hs note demonstrates how to formulate bnary dependent varables models
More informationUsing the estimated penetrances to determine the range of the underlying genetic model in casecontrol
Georgetown Unversty From the SelectedWorks of Mark J Meyer 8 Usng the estmated penetrances to determne the range of the underlyng genetc model n casecontrol desgn Mark J Meyer Neal Jeffres Gang Zheng Avalable
More informationLinear Regression Analysis: Terminology and Notation
ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented
More informationMachine Learning: and 15781, 2003 Assignment 4
ahne Learnng: 070 and 578, 003 Assgnment 4. VC Dmenson 30 onts Consder the spae of nstane X orrespondng to all ponts n the D x, plane. Gve the VC dmenson of the followng hpothess spaes. No explanaton requred.
More informationBezier curves. Michael S. Floater. August 25, These notes provide an introduction to Bezier curves. i=0
Bezer curves Mchael S. Floater August 25, 211 These notes provde an ntroducton to Bezer curves. 1 Bernsten polynomals Recall that a real polynomal of a real varable x R, wth degree n, s a functon of the
More informationSection 8.3 Polar Form of Complex Numbers
80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the
More informationMMA and GCMMA two methods for nonlinear optimization
MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons
More informationStanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011
Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected
More informationCollege of Computer & Information Science Fall 2009 Northeastern University 20 October 2009
College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:
More informationSingular Value Decomposition: Theory and Applications
Sngular Value Decomposton: Theory and Applcatons Danel Khashab Sprng 2015 Last Update: March 2, 2015 1 Introducton A = UDV where columns of U and V are orthonormal and matrx D s dagonal wth postve real
More informationThe Similar Structure Method for Solving Boundary Value Problems of a Three Region Composite Bessel Equation
The Smlar Struture Method for Solvng Boundary Value Problems of a Three Regon Composte Bessel Equaton Mngmng Kong,Xaou Dong Center for Rado Admnstraton & Tehnology Development, Xhua Unversty, Chengdu 69,
More informationEEE 241: Linear Systems
EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they
More informationReport on Image warping
Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.
More informationGeneral Nonlinear Programming (NLP) Software
General Nonlnear Programmng NLP Software CAS 737 / CES 735 Krstn Daves Hamd Ghaffar Alberto Olvera-Salazar Vou Chs January 2 26 Outlne Intro to NLP Eamnaton of: IPOPT PENNON CONOPT LOQO KNITRO Comparson
More informationHow Strong Are Weak Patents? Joseph Farrell and Carl Shapiro. Supplementary Material Licensing Probabilistic Patents to Cournot Oligopolists *
How Strong Are Weak Patents? Joseph Farrell and Carl Shapro Supplementary Materal Lcensng Probablstc Patents to Cournot Olgopolsts * September 007 We study here the specal case n whch downstream competton
More information5 The Rational Canonical Form
5 The Ratonal Canoncal Form Here p s a monc rreducble factor of the mnmum polynomal m T and s not necessarly of degree one Let F p denote the feld constructed earler n the course, consstng of all matrces
More informationUsing T.O.M to Estimate Parameter of distributions that have not Single Exponential Family
IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran
More informatione a = 12.4 i a = 13.5i h a = xi + yj 3 a Let r a = 25cos(20) i + 25sin(20) j b = 15cos(55) i + 15sin(55) j
Vetors MC Qld-3 49 Chapter 3 Vetors Exerse 3A Revew of vetors a d e f e a x + y omponent: x a os(θ 6 os(80 + 39 6 os(9.4 omponent: y a sn(θ 6 sn(9 0. a.4 0. f a x + y omponent: x a os(θ 5 os( 5 3.6 omponent:
More information1 Matrix representations of canonical matrices
1 Matrx representatons of canoncal matrces 2-d rotaton around the orgn: ( ) cos θ sn θ R 0 = sn θ cos θ 3-d rotaton around the x-axs: R x = 1 0 0 0 cos θ sn θ 0 sn θ cos θ 3-d rotaton around the y-axs:
More informationInstance-Based Learning and Clustering
Instane-Based Learnng and Clusterng R&N 04, a bt of 03 Dfferent knds of Indutve Learnng Supervsed learnng Bas dea: Learn an approxmaton for a funton y=f(x based on labelled examples { (x,y, (x,y,, (x n,y
More informationAssortment Optimization under MNL
Assortment Optmzaton under MNL Haotan Song Aprl 30, 2017 1 Introducton The assortment optmzaton problem ams to fnd the revenue-maxmzng assortment of products to offer when the prces of products are fxed.
More informationCOS 521: Advanced Algorithms Game Theory and Linear Programming
COS 521: Advanced Algorthms Game Theory and Lnear Programmng Moses Charkar February 27, 2013 In these notes, we ntroduce some basc concepts n game theory and lnear programmng (LP). We show a connecton
More informationGeneralized Linear Methods
Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set
More informationDigital Signal Processing
Dgtal Sgnal Processng Dscrete-tme System Analyss Manar Mohasen Offce: F8 Emal: manar.subh@ut.ac.r School of IT Engneerng Revew of Precedent Class Contnuous Sgnal The value of the sgnal s avalable over
More informationCommunication Complexity 16:198: February Lecture 4. x ij y ij
Communcaton Complexty 16:198:671 09 February 2010 Lecture 4 Lecturer: Troy Lee Scrbe: Rajat Mttal 1 Homework problem : Trbes We wll solve the thrd queston n the homework. The goal s to show that the nondetermnstc
More informationLet p z be the price of z and p 1 and p 2 be the prices of the goods making up y. In general there is no problem in grouping goods.
Economcs 90 Prce Theory ON THE QUESTION OF SEPARABILITY What we would lke to be able to do s estmate demand curves by segmentng consumers purchases nto groups. In one applcaton, we aggregate purchases
More informationLecture 20: Lift and Project, SDP Duality. Today we will study the Lift and Project method. Then we will prove the SDP duality theorem.
prnceton u. sp 02 cos 598B: algorthms and complexty Lecture 20: Lft and Project, SDP Dualty Lecturer: Sanjeev Arora Scrbe:Yury Makarychev Today we wll study the Lft and Project method. Then we wll prove
More informationA note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights
ACTA ET COMMENTATIONES UNIVERSITATIS TARTUENSIS DE MATHEMATICA Volume 7, Number 2, December 203 Avalable onlne at http://acutm.math.ut.ee A note on almost sure behavor of randomly weghted sums of φ-mxng
More informationEcon107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)
I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes
More informationUniversity of California, Davis Date: June 22, 2009 Department of Agricultural and Resource Economics. PRELIMINARY EXAMINATION FOR THE Ph.D.
Unversty of Calforna, Davs Date: June 22, 29 Department of Agrcultural and Resource Economcs Department of Economcs Tme: 5 hours Mcroeconomcs Readng Tme: 2 mnutes PRELIMIARY EXAMIATIO FOR THE Ph.D. DEGREE
More informationMath 217 Fall 2013 Homework 2 Solutions
Math 17 Fall 013 Homework Solutons Due Thursday Sept. 6, 013 5pm Ths homework conssts of 6 problems of 5 ponts each. The total s 30. You need to fully justfy your answer prove that your functon ndeed has
More informationProjective change between two Special (α, β)- Finsler Metrics
Internatonal Journal of Trend n Research and Development, Volume 2(6), ISSN 2394-9333 www.jtrd.com Projectve change between two Specal (, β)- Fnsler Metrcs Gayathr.K 1 and Narasmhamurthy.S.K 2 1 Assstant
More informationMixed Taxation and Production Efficiency
Floran Scheuer 2/23/2016 Mxed Taxaton and Producton Effcency 1 Overvew 1. Unform commodty taxaton under non-lnear ncome taxaton Atknson-Stgltz (JPubE 1976) Theorem Applcaton to captal taxaton 2. Unform
More informationLINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity
LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have
More informationSolutions to exam in SF1811 Optimization, Jan 14, 2015
Solutons to exam n SF8 Optmzaton, Jan 4, 25 3 3 O------O -4 \ / \ / The network: \/ where all lnks go from left to rght. /\ / \ / \ 6 O------O -5 2 4.(a) Let x = ( x 3, x 4, x 23, x 24 ) T, where the varable
More informationA CHARACTERIZATION OF ADDITIVE DERIVATIONS ON VON NEUMANN ALGEBRAS
Journal of Mathematcal Scences: Advances and Applcatons Volume 25, 2014, Pages 1-12 A CHARACTERIZATION OF ADDITIVE DERIVATIONS ON VON NEUMANN ALGEBRAS JIA JI, WEN ZHANG and XIAOFEI QI Department of Mathematcs
More informationPsychology 282 Lecture #24 Outline Regression Diagnostics: Outliers
Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.
More informationPHYSICS 212 MIDTERM II 19 February 2003
PHYSICS 1 MIDERM II 19 Feruary 003 Exam s losed ook, losed notes. Use only your formula sheet. Wrte all work and answers n exam ooklets. he aks of pages wll not e graded unless you so request on the front
More informationThe Second Anti-Mathima on Game Theory
The Second Ant-Mathma on Game Theory Ath. Kehagas December 1 2006 1 Introducton In ths note we wll examne the noton of game equlbrum for three types of games 1. 2-player 2-acton zero-sum games 2. 2-player
More informationTradable Emission Permits Regulations: The Role of Product Differentiation
Internatonal Journal of Busness and Eonoms, 005, Vol. 4, No. 3, 49-6 radable Emsson Permts Regulatons: he Role of Produt Dfferentaton Sang-Ho Lee * Department of Eonoms, Chonnam Natonal Unversty, Korea
More informationStructure and Drive Paul A. Jensen Copyright July 20, 2003
Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.
More informationClustering through Mixture Models
lusterng through Mxture Models General referenes: Lndsay B.G. 995 Mxture models: theory geometry and applatons FS- BMS Regonal onferene Seres n Probablty and Statsts. MLahlan G.J. Basford K.E. 988 Mxture
More informationAn efficient algorithm for multivariate Maclaurin Newton transformation
Annales UMCS Informatca AI VIII, 2 2008) 5 14 DOI: 10.2478/v10065-008-0020-6 An effcent algorthm for multvarate Maclaurn Newton transformaton Joanna Kapusta Insttute of Mathematcs and Computer Scence,
More informationSimulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests
Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth
More informationBeyond Zudilin s Conjectured q-analog of Schmidt s problem
Beyond Zudln s Conectured q-analog of Schmdt s problem Thotsaporn Ae Thanatpanonda thotsaporn@gmalcom Mathematcs Subect Classfcaton: 11B65 33B99 Abstract Usng the methodology of (rgorous expermental mathematcs
More informationAPPROXIMATE OPTIMAL CONTROL OF LINEAR TIME-DELAY SYSTEMS VIA HAAR WAVELETS
Journal o Engneerng Sene and ehnology Vol., No. (6) 486-498 Shool o Engneerng, aylor s Unversty APPROIAE OPIAL CONROL OF LINEAR IE-DELAY SYSES VIA HAAR WAVELES AKBAR H. BORZABADI*, SOLAYAN ASADI Shool
More informationComposite Hypotheses testing
Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter
More informationCharged Particle in a Magnetic Field
Charged Partle n a Magnet Feld Mhael Fowler 1/16/08 Introduton Classall, the fore on a harged partle n eletr and magnet felds s gven b the Lorentz fore law: v B F = q E+ Ths velot-dependent fore s qute
More information