Sources of Inflation Uncertainty and Real Economic Activity

Size: px
Start display at page:

Download "Sources of Inflation Uncertainty and Real Economic Activity"

Transcription

1 Sources of Inflaion Uncerain and Real Economic Acivi Jh-Lin Wu Deparmen of Economics Naional Chung Cheng Universi Chia-Yi 6, Taiwan and Show-Lin Chen Deparmen of Economics Fu-jen Caholic Universi Taipei 4, Taiwan and Hsiu-Yun Lee Deparmen of Economics Naional Chung Cheng Universi Chia-Yi 6, Taiwan Absrac This paper examines he effecs of inflaion uncerainies on real GDP. We argue ha differen sources of inflaion uncerain have differen impacs on real GDP. The empirical evidence poins ou ha uncerain arising from changing regression coefficiens has negaive impacs on real GDP. However, he effec of uncerain due o heeroscedasici in disurbances on real GDP is insignifican. Moreover, we find ha he surve-based measure of inflaion uncerain appears o be more associaed wih he uncerain ha arises from changing regression coefficiens. Corresponding auhor. address: ecdjlw@ccunix.ccu.edu.w.

2 . Inroducion In his Nobel Prize lecure, Milon Friedman (977) poins ou he poenial for increased inflaion o creae nominal uncerain ha hinders he efficien allocaion of resources and reduces real oupu. Ever since hen, here have been several heoreical and empirical discussions on he relaionship beween inflaion and inflaion uncerain (Ball, 99; Cukierman and Melzer, 986; Evans, 99 and Evans and Wachel, 993; Davis and Kanago, 000). In addiion, here are also several aricles ha empiricall examine he impacs of inflaion uncerain on real economic acivi, which can be caegorized ino wo differen approaches ha differ in heir prox for inflaion uncerain. The firs one is he surve-based approach, in which inflaion uncerain is proxied b he variabili across he individual forecass. In oher words, he sandard deviaion of inflaion forecass is used o measure inflaion uncerain. The inflaion uncerain obained from surve daa ends o negaivel correlae wih real economic aciviies (Hafer, 986 and Davis and Kanago, 996). The alernaive approach measures uncerain b esimaing he ime-varing condiional variance of a variable s unpredicable innovaion. In his approach he auoregressive condiional heeroscedasici (ARCH) model provided b Engle (98) or he generalized ARCH (GARCH) model provided b Bollerslev (986) is applied o esimae a ime-varing condiional residual variance. Based on an ARCH model, Coulson and Robins (985) find ha uncerain is posiivel correlaed wih real economic acivi, bu heir correlaion relaionship is no significan. Appling a sae-dependen condiional heeroscedasici model provided b Brunner and Hess (993), Lee and Ni (995) find ha inflaion uncerain is negaivel correlaed wih real economic aciviies. Adoping a bivariae GARCH model, Grier and Perr

3 (000) find ha inflaion uncerain significanl lowers real oupu growh. ARCH- or GARCH-pe models provide esimaes of how he condiional variance of inflaion varies over ime wihin a given srucure and herefore he ignore he possibili of srucural changes caused b changing regimes. Evans (99) poins ou ha here are man aspecs o inflaion uncerain and applies a ime-varing parameer model wih an ARCH specificaion for he shocks o inflaion in order o measure differen pes of uncerain. Evans and Wachel (993) accoun for he prospecs of changing inflaion regimes b appling a Markov-swiching model. The find ha uncerain abou he inflaion process conribues significanl o he overall degree of inflaion uncerain as measured b he condiional variance of fuure inflaion. Furhermore, he show ha heir measure of inflaion uncerain has a significan influence on unemplomen based on a vecor auoregressive esimaion. According o he previous discussion, we find ha here are wo pes of uncerain wihin a regression conex. These uncerainies arise due o heeroscedasici in he disurbance erms and he unknown or changing regression coefficiens, respecivel. However, he previousl-menioned lieraure fail o consider ha inflaion uncerain from differen sources ma have differen effecs on economic agens decision-making and hus on economic acivi. Mos of he previousl-menioned lieraures appl a convenional wo-sep approach in analzing he impacs of uncerainies on real economic acivi, excep for he paper b Grier and Perr (000). Pagan (984) criicizes he inappropriaeness of he wo-sep approach b showing ha he esimaes or sandard errors from he second sep are biased. The purpose of his paper is wo-fold. Firs, we examine he effecs of differen

4 3 sources of inflaion uncerain on real economic acivi measured b real GDP. Kim (993) firs applies a ime-varing parameer model wih Markov-swiching heeroscedasici o measure monear growh uncerain ha arises from heeroscedasici in disurbances and from changing regression coefficiens, respecivel. We herefore appl Kim s (993) model o calculae differen sources of inflaion uncerain and hen examine heir impacs on real GDP. To avoid Pagan s criicism of his approach, we joinl esimae he oupu equaion and he inflaion equaion wih he maximum likelihood esimaion mehod. Second, we examine he informaion conen embeded in he surve-based measure of inflaion uncerain b comparing he dnamic paern of he surve-based measure wih ha of model-based measures. Findings from our empirical invesigaion indicae ha uncerain due o changing regression coefficiens has a negaive impac on real GDP, bu he impac of uncerain arising from heeroscedasici in disurbances is negligible. Moreover, we discover ha he surve-based measure of inflaion uncerain appears more closel associaed wih he uncerain ha arises from changing regression coefficiens han wih he uncerain arising from heeroscedasici in disurbances. The remainder of he paper is organized as follows. Secion describes he inflaion s ime series model, in which parameers are ime varing and he condiional variance of disurbances is Markov-swiching. We hen discuss our measure of inflaion uncerainies based on he presened model. Secion 3 begins b discussing he daa before presening he model esimaes. The effecs of differen pes of inflaion uncerain on real GDP are hen examined empiricall. We hen decompose he oal inflaion uncerain ino wo differen sources of inflaion uncerain, and hen examine heir conribuion o oal uncerain. In addiion, we

5 4 also examine he linkage beween a surve-based measure of inflaion uncerain wih he model-based measures. Finall, conclusions are summarized in he las secion.. Measuring Inflaion Uncerainies There are wo differen pes of uncerain in convenional regression analsis. Over ime, i is highl likel ha changes in privae secor behavior, economic polic, and/or insiuions induce significan variaions in he srucure of he inflaion process. Under he assumpion of raional expecaion, i is no reasonable o assume ha coefficiens in inflaion regression are ime invarian since he econom s srucure ma change over ime. Economic agens will learn abou he polic regime changes and respond accordingl if here are frequen polic shifs (Lucas, 976). Talor (980) poins ou ha he dnamics of inflaion depend on he naure of wage conracs and he form of monear polic rules. Therefore, ime-varing coefficiens in an inflaion equaion is one source of inflaion uncerain. Second, mos exising lieraure esimae inflaion uncerain b assuming ha uncerain is due o shocks o he inflaion process, and hence he measure inflaion uncerain b using he condiional variance of inflaion. An ARCH specificaion on disurbances fails o ake ino accoun he influence on uncerain of possible fuure regime changes. Therefore, he second source of inflaion uncerain is measured b he condiional variance of inflaion ha will be affeced b he possibili of swiches in he inflaion process. To decompose he inflaion uncerain ino wo disinc sources menioned previousl, we appl a ime-varing-parameer model wih Markov-swiching heeroscedasici o describe he inflaion process:

6 5,,, T 0 X k i i, i ~ N(0, h ), h 0 ( 0 )S, () ~ iidn(0, ), (), wih Pr[S Pr[S S 0S ] p 0] p ; 00 ; Pr[S Pr[S 0S S ] p 0] p, 00, (3) where X (... ) is a (k+) vecor of explanaor variables; k is a (k+) vecor of ime-varing regression coefficiens; is a posiive definie marix; and h is he condiional variance of. The sae ( S ) is given b he wo-sae, firs-order Markov process wih he ransiion probabiliies described in (3). The ransiion probabili b sae i. p ii represens he probabili ha sae i will be followed Equaion () is he measuremen equaion, which describes he relaionship beween he unobserved sae and a T vecor of measuremen. Equaion () is he ransiion equaion, which describes he evoluion of coefficien. A random walk specificaion is used for he ime-varing coefficiens following he specificaion of Kim and Nelson (989) and Kim (993). Markov-swiching heeroscedasici is refleced b he equaion of h, which saes ha he uncondiional variance iself is subjec o shifs (srucural changes). B considering Markov-swiching heeroscedasici, he change in uncondiional variance is acuall regarded as resuling from endogenous regime changes in he variance srucure. However, if 0, hen he model degeneraes o he convenional ime-varing parameer model. Equaions () and () are easil recognized as sae-space models,

7 6 which can be recursivel esimaed b he Kalman filer. A deailed discussion of he esimaion mehod can be found in Kim (993) and hence is no described here. The wo uncerain erms of differen sources can be calculaed from he forecas error s condiional variance in equaion () as: where U ~ U ~ U ~, U ~ V ~ ' U ~ X X, (5) 0 ( 0 ) Pr[S ], (6) V ~ i Pr[S i ]{V ( ~ i )( ~ i )'}. (7) - i0 (4) Here, ~ i i Pr[S i ; is he esimae of based on ] i0 S i V informaion up o ime -, given i ; is he covariance marix of i and is he informaion available a ime. Terms U ~ and U ~ are he condiional variances due o changing regression coefficiens and he heeroscedisici of he disurbance erms, respecivel. 3. Inflaion Uncerainies and Real Economic Acivi: An Empirical Invesigaion The purpose of his secion is o empiricall examine he effecs of differen pes of uncerain on real GDP. 3. Daa Descripion Quarerl daa of real GDP and he consumer price index (CPI) for he Unied Saes (US) are obained from IMF s Inernaional Financial Saisics. Inflaion raes are compued from he CPI. The sample period sars from 957Q and ends in 000Q3. The one-quarer forecass of he GDP deflaor and CPI inflaion rae are

8 7 obained from he Surve of Professional Forecasers published b he Federal Reserve Bank of Philadelphia. The forecass of he GDP deflaor sar from he las quarer of 968, while he forecass of CPI inflaion raes begin from he hird quarer of Model Esimaion We follow Hamilon (989) and Kim (993) b assuming ha real GDP is composed of wo unobserved componens. The firs componen follows a random walk wih drif, which evolves according o a wo-sae Markov process, and he second componen follows an auoregressive process. Therefore, he specificaion for real GDP is given as follows: C, (8) s U ~ U ~, (9) C ~ C C 3, ~ iidn(0, ), (0) S, S 0,, () s Pr Pr 0 S S q and PrS 0 S q, S 0 S 0 q and PrS S 0 q, 00 where is he log of real GDP a ime ; and are he rend and cclical C 00 () componens of real GDP, respecivel; s is he drif erm, which changes according ~ o a wo-sae Markov process; and is he condiional forecas error of inflaion. If Friedman s argumen is correc, hen we expec ha and/or be negaive. in (9) should I is worh noing ha Equaion (0) is derived b using a Phillips curve which relaes cclical unemplomen o he forecas error of inflaion and Okun's law ha relaes cclical oupu o cclical unemplomen. A Phillips curve presened in is

9 8 empirical forma is: u u n p n ~ l (u l u l ) 0, 0 0, (3) l in which is he unemplomen rae, u is is naural rae, and u n is a seriall- uncorrelaed error erm. Noe ha 0 measures he rade-off beween he unemplomen gap and inflaion surprise. Okun's law relaion, on he oher hand, is n (u u ), 0. (4) Equaion (0) is obained b combining (3) and (4) ogeher. Thus, 3 0 in equaion (0) should be posiive. In oher words, he rade-off beween inflaion raes and unemplomen raes is suppored if 3 is significanl posiive. Equaions ()-(3) and (8)-() can be esimaed b a convenional wo-sep mehod. As such, we can esimae U ~ and U ~ from ()-(3) in he firs sep and hen use hem o esimae parameers of (9) and (0) in he second sep. However, as we saed before, Pagan (984) poins ou ha we ma ge biased parameer esimaes or biased sandard errors from he second sep esimaion procedure. To avoid Pagan s criique, we herefore esimae he inflaion equaion and he oupu equaion joinl b emploing a maximum likelihood esimaion mehod. The lag order of inflaion equaion () needs o be deermined, and i is seleced b choosing he maximum value of he log likelihood funcion from he join esimaion. The lag order in he inflaion equaion () is hen se o 3. To jusif he model in equaions ()-(3) and (8)-(), i would seem appropriae o show ha he esimaed disurbances from equaion () b he ordinar leas square (OLS) mehod are seriall correlaed and condiionall heeroscedasic. We herefore esimae () b OLS and hen examine he serial correlaion of residuals and squared

10 9 residuals b using he Q saisic of Ljung and Box (978). We also appl he ARCH es provided b Engle (98) o examine he null hpohesis of no auoregressive condiional heeroscedasici in he esimaed residuals. Findings from Table indicae no significan serial correlaion since he Q-saisics are no significan a he 5% level for he residuals from OLS. However, he Q-saisics are significan a he % level for he squared residuals. The ARCH es also rejecs he null of no ARCH effecs in he residuals. Table furher repors he diagnosic checks for residuals from a convenional ime-varing-parameer model. Again, Q-saisics are insignifican for sandardized residuals, bu significan a he % level for squared residuals. The ARCH es reveals a significan ARCH effec a he 5% level in he esimaed residuals. Therefore, findings from Table impl ha some of he dnamics of condiional variance are no full capured b a consan parameer model or a convenional ime-varing parameer model. To ake ino accoun he heeroscedasici in disurbances, we assume ha he condiional variance of disurbances is Markov-swiching. The join esimaions of he generalized model in equaions ()-(3) and (8)-() are repored in Table, which indicaes ha boh pes of uncerain have a negaive impac on real GDP. Uncerain arising from changing parameers has significanl negaive impacs on real GDP, bu he effec of uncerain from heeroscedasici in disurbances is negligible. Therefore, he hpohesis ha uncerain due o changing coefficiens discourages economic acivi is srongl suppored. 3.3 Robusness of he empirical esimaes The period saring from 970 in general has higher average inflaion uncerain han he period of pre-970. To check he robusness of our finding, we re-examine he effecs of inflaion uncerain on real GDP growh based on he

11 0 period 970Q 000Q3. The specificaion of he inflaion equaion over he sub-sample period is similar o ha over he full sample period. Findings from he las column of Table indicae ha he effec of uncerain arising from changing parameers is significan a he 0% level, bu he size of he esimae is /4h of ha in he whole period. Again, he impac of he uncerain from heeroscedasici of disurbances is insignifican a convenional levels. The rade-off beween inflaion raes and unemplomen raes is suppored since he esimaed coefficien of posiive. 3 is The previous wo findings are he same as hose in he whole period s esimaion. As for he diagnosic ess, here is no serial correlaion in he residuals and squared residuals, and no ARCH effec in he residuals. Overall, our finding ha uncerain arising from changing parameers has a negaive impac on real GDP is suppored, and his finding is robus for differen sample periods. We also find ha uncerain due o heeroscedasici in disurbances is negligible in affecing real GDP, and ha here is a rade-off beween inflaion and unemplomen raes. These wo findings are robus o he sample periods. One ma argue ha our finding of a significan negaive impac of he regime uncerain componen of inflaion uncerain ma be due o our failure of conrolling oil price shocks. Following Hamilon (996), we herefore include nominal oil price changes and ne oil price changes in equaion (0), respecivel, and hen re-esimae he model simulaneousl. Empirical findings indicae ha he impac of oil price changes on real oupu is no significan a he convenional significance level. Alhough our findings from Table are no significanl affeced when we include eiher nominal oil price changes or ne oil price changes, he residual diagnosic checks reveal he exisence of ARCH effecs in esimaed residuals.

12 (resuls are no repored here, bu are available upon reques). Therefore, his indicaes ha our findings from Table are no due o our failure of conrolling oil price shocks. 3.4 Decomposiion of Inflaion Uncerain Equaion (4) indicaes ha he condiional variance of disurbances consiss of wo disinc erms. The are he condiional variance arising from changing regression coefficiens and he condiional variance due o he heeroscedasici of he disurbance erms. These wo differen sources of uncerain can be calculaed based on equaion (5) and (6), respecivel. The plo of he oal uncerain, U ~, and he decomposed uncerain, U ~ and U ~, based on (5) and (6), respecivel, is given in Figure. From Figure we observe ha he overall inflaion uncerain is higher during he periods of , , and han in oher periods, which reflecs basicall wo oil price shocks ha occurred in 973 and 979, financial deregulaion saring from 979, he swiching of he Fed s monear polic arge in 98, and he Plaza accord in 985. We also observe ha uncerain due o changing regression coefficiens is in general higher han ha arising from he heeroscedasici of disurbances during he period of The average level of inflaion uncerain during he period of is 0.46, approximael 58% of which is due o he uncerain arising from changing regression coefficiens. However, he average level of inflaion uncerain is 0.5 during he sample period of , approximael 65% of which is due o he heeroscedasici of residuals. Convenional lieraure consruc inflaion uncerain from surve forecass, in which he sandard deviaion of inflaion forecass is used o measure inflaion

13 uncerain. Based on his approach, Hafer (986) and Davis and Kanago (996) suppor Friedman s hpohesis. I is herefore quie ineresing o examine he dnamic paerns of our esimaed inflaion uncerain wih ha from a surve measure b ploing hem ogeher. Firs of all, we consruc he surve-based measure of inflaion uncerain from he GDP deflaor and CPI inflaion rae, respecivel. According o Figure, we find ha he size of he model-based measure of oal inflaion uncerain is in general higher han (wih several excepions) ha of surve-based measures, bu he flucuaion of he laer is much larger han ha of he former. The swiching of he Fed s monear polic arge in 98, he Plaza Accord in 985, and Iraq s invasion of Kuwai in 990 are he periods wih high uncerain according o he CPI surve-based measure of inflaion uncerain. 3 The wo oil price shocks in he 970s, he onse of he Iran-Iraq war in 980, he swiching of he Fed s monear polic arge in 98, and he Iraq s invasion of Kuwai in 990 are he periods wih high inflaion uncerain according o he surve-based measure from he GDP deflaor. The previousl-menioned periods wih high inflaion uncerain are also consisen wih hose from he model based measure of oal inflaion uncerain. In Figure 3, we compare he surve-based measure of inflaion uncerain from he GDP deflaor wih differen sources of model-based measures. We find ha he movemen of he surve-based measure of inflaion uncerain is more associaed wih he uncerain arising from changing regression coefficiens. The correlaion coefficien beween he surve-based measure of inflaion uncerain and he measure of uncerain arising from changing regression coefficiens is This is higher han he correlaion coefficien beween he surve-based measure of inflaion uncerain and he uncerain arising from he heeroscedasici of

14 3 disurbances which is Figure 4 plos he model-based measure of inflaion uncerain and he surve-based measure from he forecas of he CPI inflaion rae. Again, we find ha he surve-based measure of inflaion uncerain is more associaed wih ha arising from changing regression coefficiens. The correlaion coefficien beween he surve-based measure of inflaion uncerain and he uncerain arising from changing regression coefficiens is This is higher han he correlaion coefficien beween he surve-based measure of inflaion uncerain and he uncerain arising from he heeroscedasici of disurbances which is Findings from Figures o 4 are ineresing since he indicae ha he surve-based measure of inflaion uncerain is more associaed wih he uncerain from changing regression coefficiens which can be characerized b a model wih ime-varing-parameers in coefficiens and Markov-swiching in disurbances. 4. Conclusions Inflaion uncerain is convenionall measured as he condiional variance of inflaion in mos exising lieraure. The effecs of inflaion uncerain on real economic acivi are hen examined b he convenional wo-sep approach. In his paper we appl a ime-varing-parameer model wih Markov-Swiching variance o measure wo differen pes of uncerain. The effecs of uncerainies on real GDP are hen examined b a join esimaion of he maximum likelihood. Empiricall, our join esimaion mehod sideseps Pagan s criique on he convenional wo-sep approach. The empirical findings poin ou ha uncerain due o changing regression coefficiens has a significan influence on real GDP, bu he effecs of uncerain arising from heeroscedasici of disurbances are negligible. Moreover,

15 4 we also find ha he surve-based measure of inflaion uncerain appears more associaed wih he uncerain arising from changing regression coefficiens.

16 5 Endnoes d The surve-based inflaion uncerain is given b γ [ (π π) ]/n, where i π i is he forecas of he ih forecaser and π is he average forecas across n forecasers. The series of ne oil price increases is he difference beween he level of oil prices for quarer and he maximum price observed during he preceding four quarers, if his difference is posiive. Oherwise, he series of ne oil prices is defined o be zero. 3 The daa for he surve forecass of he CPI inflaion rae sar from he hird quarer of 98, and hence heir uncerain is available from he same ime.

17 6 Table. Diagnosic Checks of Residuals CPM TVPM Q().883 [0.46] 3.85 [0.3] Q(4) 8.76 [0.5] [0.8] Q () 4.3 [0.00] [0.00] Q (4) [0.00] [0.00] ARCH() [0.3].39 [0.4] ARCH(4) 3.79 [0.0].758 [0.00] Noes:. CPM and TVPM indicae he consan parameer model and he ime-varing parameer model, respecivel. The CPM is esimaed b he ordinar leas square mehod and he TVPM is esimaed b a Kalman filer.. Numbers in brackes are p-values. 3. Terms Q(p) and Q (p) are he Ljung-Box auocorrelaion es saisiics for residuals and residuals squared, respecivel, for up o ph-order auocorrelaions, which have a chi-square disribuion wih p degrees of freedom. ARCH(q) is he Lagrange muliplier es of Engle (99) for up o qh-order auoregressive condiional heeroscedasici, which has a chi-square disribuion wih q degrees of freedom.

18 7 Table. Join Esimaion Resuls wih Oupu and Inflaion Equaions C, s U ~ U ~ C ~, C C 3, ~ iidn(0, ), s 0 S, PrS 0 S 0 q 00, PrS S q, Pr S S 0 q 00, PrS 0 S q. 57Q~000Q3 70Q~000Q * -.5# (.7) (0.84) (.960) (.39).037*.46* (0.04) (0.085) * (0.0) (0.084) * 0.437* 3 (0.46) (0.45) 0.608* 0.638* (0.050) (0.045).596* 3.43* 0 (0.35) (0.45) -.409* -.47* (0.4) (0.43) q 0.97* 0.970* 00 (0.04) (0.07) q 0.57* 0.37* (0.7) (0.8) Q() [0.548] 0.90 [0.537] Q(4) [0.05].70 [0.537 ] Q () [0.559] 9.97 [0.63] Q (4) 5.69 [0.39] [0.83] ARCH() [0.97] [0.58] ARCH(4) [0.87] [0.400] Noe:. The oupu equaion is esimaed using 00 imes he log-difference in quarerl real GDP. Sandard errors are in parenhesis and numbers in he brackes are p-values.. * and # indicae significance a he 5% and 0% level, respecivel. 3. Terms Q(p) and Q (p) are he Ljung-Box auocorrelaion es saisiics for residuals and residuals squared, respecivel, for up o ph-order auocorrelaions, which have a chi-square disribuion wih p degrees of freedom. ARCH(q) is he Lagrange muliplier es of Engle (99) for up o qh-order auoregressive condiional heeroscedasici, which has a chi-square disribuion wih q degrees of freedom.

19 TOT MKV TVP Figure. Model-based Measures of Inflaion Uncerain. TOT: model-based oal uncerain. MKV: uncerain arising from heeroscedasici of disurbances. TVP: uncerain arising from changing regression coefficiens TOT SVY(GDP) SVY(CPI) Figure. Model-based vs. Surve-based Measures of Inflaion Uncerain. TOT: model-based oal uncerain. SVY(GDP): surve-based measure of inflaion uncerain from a GDP deflaor. SVY(CPI): surve-based measure of inflaion uncerain from CPI inflaion raes.

20 SVY(GDP) MKV TVP Figure 3. Surve-based vs. Model-based Measures of Inflaion Uncerain wih a GDP deflaor in he Surve. SVY(GDP): surve-based measure of inflaion uncerain from GDP deflaor. MKV: uncerain arising from heeroscedasici of disurbances. TVP: uncerain arising from changing regression coefficiens SVY(CPI) MKV TVP Figure 4. Model-based vs. Surve-based Inflaion Uncerain wih CPI Inflaion Rae in he Surve. SVY(CPI): surve-based measure of inflaion uncerain from CPI inflaion raes. MKV: uncerain arising from heeroscedasici of disurbances. TVP: uncerain arising from changing regression coefficiens.

21 0 References Ball, Laurence. "Wh Does High Inflaion Raise Inflaion Uncerain?" Journal of Monear Economics 9 (June 99): Brunner, Allen D., and Hess, Gregor D. "Are Higher Levels of Inflaion Less Predicable? A Sae Dependen Condiional Heeroscedasici Approach." Journal of Business and Economic Saisics (April 993): Coulson, Edward, and Russell Robins. "Aggregae Economic Acivi and he Variance of Inflaion: Anoher Look." Economic Leers 7 (November 985): Cukierman, Alex, and Allan Melzer. "A Theor of Ambigui, Credibili, and Inflaion under Discreion and Asmmeric Informaion." Economerica 54 (Sepember 986): Davis, George K., and Brce E. Kanago. "On Measuring he Effec of Inflaion Uncerain on Real GNP Growh." Oxford Economic Paper 48 (Januar 996): "The Level and Uncerain of Inflaion: Resuls from OECD Forecass." Economic Inquir 38 (Januar 000): Engle, Rober. "Auoregressive Condiional Heeroscedasici wih Esimaes of he Variance of Unied Kingdom Inflaion." Economerica 50 (Jul 98): Evans, Marin. "Discovering he Link beween Inflaion Raes and Inflaion Uncerain." Journal of Mone, Credi and Banking 3 (Ma 99): Evans, Marin, and Paul Wachel. "Inflaion Regimes and he Sources of Inflaion Uncerain." Journal of Mone, Credi and banking 5 (Augus 993):

22 Friedman, Milon. "Nobel Lecure: Inflaion and Unemplomen." Journal of Poliical Econom 85 (June 977): Grier, Kevin B. and Mark J. Perr. "The Effecs of Real and Nominal Uncerain on Inflaion and Oupu Growh: Some GARCH-M Evidence." Journal of Applied Economerics 5 (Januar-Februar 000): Hafer, R. W. "Inflaion Uncerain and a Tes of he Friedman Hpohesis." Journal of Macroeconomics 8 (Summer 986): Hamilon, James. "This is Wha Happened o he Oil Price-Macroeconomic Relaionship." Journal of Monear Economics 38 (Ocober 996): "A New Approach o he Economic Analsis of Nonsaionar Time Series and he Business Ccle." Economerica 57 (March 989): Kim, Chang-Jin. "Sources of Monear Growh Uncerain and Economic Acivi: The Time-Varing-Parameer Model wih Heeroscedasic Disurbances." Review of Economics and Saisics 75 (Augus 993): Kim, Chang-Jin and Charles Nelson. "The Time-Varing-Parameer Model for Modeling Changing Condiional Variance: The Case of he Lucas Hpohesis." Journal of Business and Economic Saisics 7 (Ocober 989): Lee, Kiseok and Shawn Ni. "Inflaion Uncerain and Real Economic Aciviies." Applied Economic Leers (November 995): Ljung, G. M. and G. E. Box. "On a Measure of Lack of Fi in Time Series Models." Biomerika 66 (978): Lucas, Rober Jr. "Economeric Polic Evaluaion: A Criique." Carnegie-Rocheser Conference Series on Public Polic (976): Pagan, Adrian. "Economeric Issues in he Analsis of Regressions wih Generaed

23 Regressors." Inernaional Economic Review 5 (Februar 984): -47. Talor, John B. "Aggregae Dnamics and Saggered Conracs." Journal of Poliical Econom 88 (Februar 980): -3.

R t. C t P t. + u t. C t = αp t + βr t + v t. + β + w t

R t. C t P t. + u t. C t = αp t + βr t + v t. + β + w t Exercise 7 C P = α + β R P + u C = αp + βr + v (a) (b) C R = α P R + β + w (c) Assumpions abou he disurbances u, v, w : Classical assumions on he disurbance of one of he equaions, eg. on (b): E(v v s P,

More information

Has the Business Cycle Changed? Evidence and Explanations. Appendix

Has the Business Cycle Changed? Evidence and Explanations. Appendix Has he Business Ccle Changed? Evidence and Explanaions Appendix Augus 2003 James H. Sock Deparmen of Economics, Harvard Universi and he Naional Bureau of Economic Research and Mark W. Wason* Woodrow Wilson

More information

ESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING

ESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING Inernaional Journal of Social Science and Economic Research Volume:02 Issue:0 ESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING Chung-ki Min Professor

More information

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H.

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H. ACE 564 Spring 2006 Lecure 7 Exensions of The Muliple Regression Model: Dumm Independen Variables b Professor Sco H. Irwin Readings: Griffihs, Hill and Judge. "Dumm Variables and Varing Coefficien Models

More information

Linear Dynamic Models

Linear Dynamic Models Linear Dnamic Models and Forecasing Reference aricle: Ineracions beween he muliplier analsis and he principle of acceleraion Ouline. The sae space ssem as an approach o working wih ssems of difference

More information

Chapter 5. Heterocedastic Models. Introduction to time series (2008) 1

Chapter 5. Heterocedastic Models. Introduction to time series (2008) 1 Chaper 5 Heerocedasic Models Inroducion o ime series (2008) 1 Chaper 5. Conens. 5.1. The ARCH model. 5.2. The GARCH model. 5.3. The exponenial GARCH model. 5.4. The CHARMA model. 5.5. Random coefficien

More information

Diebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles

Diebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles Diebold, Chaper 7 Francis X. Diebold, Elemens of Forecasing, 4h Ediion (Mason, Ohio: Cengage Learning, 006). Chaper 7. Characerizing Cycles Afer compleing his reading you should be able o: Define covariance

More information

Testing for a Single Factor Model in the Multivariate State Space Framework

Testing for a Single Factor Model in the Multivariate State Space Framework esing for a Single Facor Model in he Mulivariae Sae Space Framework Chen C.-Y. M. Chiba and M. Kobayashi Inernaional Graduae School of Social Sciences Yokohama Naional Universiy Japan Faculy of Economics

More information

Lecture 5. Time series: ECM. Bernardina Algieri Department Economics, Statistics and Finance

Lecture 5. Time series: ECM. Bernardina Algieri Department Economics, Statistics and Finance Lecure 5 Time series: ECM Bernardina Algieri Deparmen Economics, Saisics and Finance Conens Time Series Modelling Coinegraion Error Correcion Model Two Seps, Engle-Granger procedure Error Correcion Model

More information

DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND

DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND Asymmery and Leverage in Condiional Volailiy Models Michael McAleer WORKING PAPER

More information

Bias in Conditional and Unconditional Fixed Effects Logit Estimation: a Correction * Tom Coupé

Bias in Conditional and Unconditional Fixed Effects Logit Estimation: a Correction * Tom Coupé Bias in Condiional and Uncondiional Fixed Effecs Logi Esimaion: a Correcion * Tom Coupé Economics Educaion and Research Consorium, Naional Universiy of Kyiv Mohyla Academy Address: Vul Voloska 10, 04070

More information

Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1

Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1 Vecorauoregressive Model and Coinegraion Analysis Par V Time Series Analysis Dr. Sevap Kesel 1 Vecorauoregression Vecor auoregression (VAR) is an economeric model used o capure he evoluion and he inerdependencies

More information

A Dynamic Model of Economic Fluctuations

A Dynamic Model of Economic Fluctuations CHAPTER 15 A Dynamic Model of Economic Flucuaions Modified for ECON 2204 by Bob Murphy 2016 Worh Publishers, all righs reserved IN THIS CHAPTER, OU WILL LEARN: how o incorporae dynamics ino he AD-AS model

More information

Econ Autocorrelation. Sanjaya DeSilva

Econ Autocorrelation. Sanjaya DeSilva Econ 39 - Auocorrelaion Sanjaya DeSilva Ocober 3, 008 1 Definiion Auocorrelaion (or serial correlaion) occurs when he error erm of one observaion is correlaed wih he error erm of any oher observaion. This

More information

Solutions to Odd Number Exercises in Chapter 6

Solutions to Odd Number Exercises in Chapter 6 1 Soluions o Odd Number Exercises in 6.1 R y eˆ 1.7151 y 6.3 From eˆ ( T K) ˆ R 1 1 SST SST SST (1 R ) 55.36(1.7911) we have, ˆ 6.414 T K ( ) 6.5 y ye ye y e 1 1 Consider he erms e and xe b b x e y e b

More information

A Specification Test for Linear Dynamic Stochastic General Equilibrium Models

A Specification Test for Linear Dynamic Stochastic General Equilibrium Models Journal of Saisical and Economeric Mehods, vol.1, no.2, 2012, 65-70 ISSN: 2241-0384 (prin), 2241-0376 (online) Scienpress Ld, 2012 A Specificaion Tes for Linear Dynamic Sochasic General Equilibrium Models

More information

Financial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS

Financial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS Name SOLUTIONS Financial Economerics Jeffrey R. Russell Miderm Winer 009 SOLUTIONS You have 80 minues o complee he exam. Use can use a calculaor and noes. Try o fi all your work in he space provided. If

More information

Introduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate.

Introduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate. Inroducion Gordon Model (1962): D P = r g r = consan discoun rae, g = consan dividend growh rae. If raional expecaions of fuure discoun raes and dividend growh vary over ime, so should he D/P raio. Since

More information

Wednesday, November 7 Handout: Heteroskedasticity

Wednesday, November 7 Handout: Heteroskedasticity Amhers College Deparmen of Economics Economics 360 Fall 202 Wednesday, November 7 Handou: Heeroskedasiciy Preview Review o Regression Model o Sandard Ordinary Leas Squares (OLS) Premises o Esimaion Procedures

More information

ACE 562 Fall Lecture 8: The Simple Linear Regression Model: R 2, Reporting the Results and Prediction. by Professor Scott H.

ACE 562 Fall Lecture 8: The Simple Linear Regression Model: R 2, Reporting the Results and Prediction. by Professor Scott H. ACE 56 Fall 5 Lecure 8: The Simple Linear Regression Model: R, Reporing he Resuls and Predicion by Professor Sco H. Irwin Required Readings: Griffihs, Hill and Judge. "Explaining Variaion in he Dependen

More information

Econ107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8)

Econ107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8) I. Definiions and Problems A. Perfec Mulicollineariy Econ7 Applied Economerics Topic 7: Mulicollineariy (Sudenmund, Chaper 8) Definiion: Perfec mulicollineariy exiss in a following K-variable regression

More information

Tourism forecasting using conditional volatility models

Tourism forecasting using conditional volatility models Tourism forecasing using condiional volailiy models ABSTRACT Condiional volailiy models are used in ourism demand sudies o model he effecs of shocks on demand volailiy, which arise from changes in poliical,

More information

Ready for euro? Empirical study of the actual monetary policy independence in Poland VECM modelling

Ready for euro? Empirical study of the actual monetary policy independence in Poland VECM modelling Macroeconomerics Handou 2 Ready for euro? Empirical sudy of he acual moneary policy independence in Poland VECM modelling 1. Inroducion This classes are based on: Łukasz Goczek & Dagmara Mycielska, 2013.

More information

On Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature

On Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature On Measuring Pro-Poor Growh 1. On Various Ways of Measuring Pro-Poor Growh: A Shor eview of he Lieraure During he pas en years or so here have been various suggesions concerning he way one should check

More information

Distribution of Least Squares

Distribution of Least Squares Disribuion of Leas Squares In classic regression, if he errors are iid normal, and independen of he regressors, hen he leas squares esimaes have an exac normal disribuion, no jus asympoic his is no rue

More information

Unit Root Time Series. Univariate random walk

Unit Root Time Series. Univariate random walk Uni Roo ime Series Univariae random walk Consider he regression y y where ~ iid N 0, he leas squares esimae of is: ˆ yy y y yy Now wha if = If y y hen le y 0 =0 so ha y j j If ~ iid N 0, hen y ~ N 0, he

More information

Time series Decomposition method

Time series Decomposition method Time series Decomposiion mehod A ime series is described using a mulifacor model such as = f (rend, cyclical, seasonal, error) = f (T, C, S, e) Long- Iner-mediaed Seasonal Irregular erm erm effec, effec,

More information

Outline. lse-logo. Outline. Outline. 1 Wald Test. 2 The Likelihood Ratio Test. 3 Lagrange Multiplier Tests

Outline. lse-logo. Outline. Outline. 1 Wald Test. 2 The Likelihood Ratio Test. 3 Lagrange Multiplier Tests Ouline Ouline Hypohesis Tes wihin he Maximum Likelihood Framework There are hree main frequenis approaches o inference wihin he Maximum Likelihood framework: he Wald es, he Likelihood Raio es and he Lagrange

More information

Granger Causality Among Pre-Crisis East Asian Exchange Rates. (Running Title: Granger Causality Among Pre-Crisis East Asian Exchange Rates)

Granger Causality Among Pre-Crisis East Asian Exchange Rates. (Running Title: Granger Causality Among Pre-Crisis East Asian Exchange Rates) Granger Causaliy Among PreCrisis Eas Asian Exchange Raes (Running Tile: Granger Causaliy Among PreCrisis Eas Asian Exchange Raes) Joseph D. ALBA and Donghyun PARK *, School of Humaniies and Social Sciences

More information

Department of Economics East Carolina University Greenville, NC Phone: Fax:

Department of Economics East Carolina University Greenville, NC Phone: Fax: March 3, 999 Time Series Evidence on Wheher Adjusmen o Long-Run Equilibrium is Asymmeric Philip Rohman Eas Carolina Universiy Absrac The Enders and Granger (998) uni-roo es agains saionary alernaives wih

More information

Distribution of Estimates

Distribution of Estimates Disribuion of Esimaes From Economerics (40) Linear Regression Model Assume (y,x ) is iid and E(x e )0 Esimaion Consisency y α + βx + he esimaes approach he rue values as he sample size increases Esimaion

More information

Chapter 15. Time Series: Descriptive Analyses, Models, and Forecasting

Chapter 15. Time Series: Descriptive Analyses, Models, and Forecasting Chaper 15 Time Series: Descripive Analyses, Models, and Forecasing Descripive Analysis: Index Numbers Index Number a number ha measures he change in a variable over ime relaive o he value of he variable

More information

GMM - Generalized Method of Moments

GMM - Generalized Method of Moments GMM - Generalized Mehod of Momens Conens GMM esimaion, shor inroducion 2 GMM inuiion: Maching momens 2 3 General overview of GMM esimaion. 3 3. Weighing marix...........................................

More information

Forecasting optimally

Forecasting optimally I) ile: Forecas Evaluaion II) Conens: Evaluaing forecass, properies of opimal forecass, esing properies of opimal forecass, saisical comparison of forecas accuracy III) Documenaion: - Diebold, Francis

More information

ACE 562 Fall Lecture 5: The Simple Linear Regression Model: Sampling Properties of the Least Squares Estimators. by Professor Scott H.

ACE 562 Fall Lecture 5: The Simple Linear Regression Model: Sampling Properties of the Least Squares Estimators. by Professor Scott H. ACE 56 Fall 005 Lecure 5: he Simple Linear Regression Model: Sampling Properies of he Leas Squares Esimaors by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Inference in he Simple

More information

Wisconsin Unemployment Rate Forecast Revisited

Wisconsin Unemployment Rate Forecast Revisited Wisconsin Unemploymen Rae Forecas Revisied Forecas in Lecure Wisconsin unemploymen November 06 was 4.% Forecass Poin Forecas 50% Inerval 80% Inerval Forecas Forecas December 06 4.0% (4.0%, 4.0%) (3.95%,

More information

The Brock-Mirman Stochastic Growth Model

The Brock-Mirman Stochastic Growth Model c December 3, 208, Chrisopher D. Carroll BrockMirman The Brock-Mirman Sochasic Growh Model Brock and Mirman (972) provided he firs opimizing growh model wih unpredicable (sochasic) shocks. The social planner

More information

Modeling and Forecasting Volatility Autoregressive Conditional Heteroskedasticity Models. Economic Forecasting Anthony Tay Slide 1

Modeling and Forecasting Volatility Autoregressive Conditional Heteroskedasticity Models. Economic Forecasting Anthony Tay Slide 1 Modeling and Forecasing Volailiy Auoregressive Condiional Heeroskedasiciy Models Anhony Tay Slide 1 smpl @all line(m) sii dl_sii S TII D L _ S TII 4,000. 3,000.1.0,000 -.1 1,000 -. 0 86 88 90 9 94 96 98

More information

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time.

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time. Supplemenary Figure 1 Spike-coun auocorrelaions in ime. Normalized auocorrelaion marices are shown for each area in a daase. The marix shows he mean correlaion of he spike coun in each ime bin wih he spike

More information

Dynamic Econometric Models: Y t = + 0 X t + 1 X t X t k X t-k + e t. A. Autoregressive Model:

Dynamic Econometric Models: Y t = + 0 X t + 1 X t X t k X t-k + e t. A. Autoregressive Model: Dynamic Economeric Models: A. Auoregressive Model: Y = + 0 X 1 Y -1 + 2 Y -2 + k Y -k + e (Wih lagged dependen variable(s) on he RHS) B. Disribued-lag Model: Y = + 0 X + 1 X -1 + 2 X -2 + + k X -k + e

More information

Volatility. Many economic series, and most financial series, display conditional volatility

Volatility. Many economic series, and most financial series, display conditional volatility Volailiy Many economic series, and mos financial series, display condiional volailiy The condiional variance changes over ime There are periods of high volailiy When large changes frequenly occur And periods

More information

Asymmetry and Leverage in Conditional Volatility Models*

Asymmetry and Leverage in Conditional Volatility Models* Asymmery and Leverage in Condiional Volailiy Models* Micael McAleer Deparmen of Quaniaive Finance Naional Tsing Hua Universiy Taiwan and Economeric Insiue Erasmus Scool of Economics Erasmus Universiy Roerdam

More information

Regression with Time Series Data

Regression with Time Series Data Regression wih Time Series Daa y = β 0 + β 1 x 1 +...+ β k x k + u Serial Correlaion and Heeroskedasiciy Time Series - Serial Correlaion and Heeroskedasiciy 1 Serially Correlaed Errors: Consequences Wih

More information

Methodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size.

Methodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size. Mehodology. Uni Roo Tess A ime series is inegraed when i has a mean revering propery and a finie variance. I is only emporarily ou of equilibrium and is called saionary in I(0). However a ime series ha

More information

DEPARTMENT OF STATISTICS

DEPARTMENT OF STATISTICS A Tes for Mulivariae ARCH Effecs R. Sco Hacker and Abdulnasser Haemi-J 004: DEPARTMENT OF STATISTICS S-0 07 LUND SWEDEN A Tes for Mulivariae ARCH Effecs R. Sco Hacker Jönköping Inernaional Business School

More information

What Ties Return Volatilities to Price Valuations and Fundamentals? On-Line Appendix

What Ties Return Volatilities to Price Valuations and Fundamentals? On-Line Appendix Wha Ties Reurn Volailiies o Price Valuaions and Fundamenals? On-Line Appendix Alexander David Haskayne School of Business, Universiy of Calgary Piero Veronesi Universiy of Chicago Booh School of Business,

More information

Properties of Autocorrelated Processes Economics 30331

Properties of Autocorrelated Processes Economics 30331 Properies of Auocorrelaed Processes Economics 3033 Bill Evans Fall 05 Suppose we have ime series daa series labeled as where =,,3, T (he final period) Some examples are he dail closing price of he S&500,

More information

Vehicle Arrival Models : Headway

Vehicle Arrival Models : Headway Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where

More information

OBJECTIVES OF TIME SERIES ANALYSIS

OBJECTIVES OF TIME SERIES ANALYSIS OBJECTIVES OF TIME SERIES ANALYSIS Undersanding he dynamic or imedependen srucure of he observaions of a single series (univariae analysis) Forecasing of fuure observaions Asceraining he leading, lagging

More information

ECON 482 / WH Hong Time Series Data Analysis 1. The Nature of Time Series Data. Example of time series data (inflation and unemployment rates)

ECON 482 / WH Hong Time Series Data Analysis 1. The Nature of Time Series Data. Example of time series data (inflation and unemployment rates) ECON 48 / WH Hong Time Series Daa Analysis. The Naure of Time Series Daa Example of ime series daa (inflaion and unemploymen raes) ECON 48 / WH Hong Time Series Daa Analysis The naure of ime series daa

More information

1. Diagnostic (Misspeci cation) Tests: Testing the Assumptions

1. Diagnostic (Misspeci cation) Tests: Testing the Assumptions Business School, Brunel Universiy MSc. EC5501/5509 Modelling Financial Decisions and Markes/Inroducion o Quaniaive Mehods Prof. Menelaos Karanasos (Room SS269, el. 01895265284) Lecure Noes 6 1. Diagnosic

More information

Forward guidance. Fed funds target during /15/2017

Forward guidance. Fed funds target during /15/2017 Forward guidance Fed funds arge during 2004 A. A wo-dimensional characerizaion of moneary shocks (Gürkynak, Sack, and Swanson, 2005) B. Odyssean versus Delphic foreign guidance (Campbell e al., 2012) C.

More information

Financial Crisis, Taylor Rule and the Fed

Financial Crisis, Taylor Rule and the Fed Deparmen of Economics Working Paper Series Financial Crisis, Taylor Rule and he Fed Saen Kumar 2014/02 1 Financial Crisis, Taylor Rule and he Fed Saen Kumar * Deparmen of Economics, Auckland Universiy

More information

Policy regimes Theory

Policy regimes Theory Advanced Moneary Theory and Policy EPOS 2012/13 Policy regimes Theory Giovanni Di Barolomeo giovanni.dibarolomeo@uniroma1.i The moneary policy regime The simple model: x = - s (i - p e ) + x e + e D p

More information

Comparing Means: t-tests for One Sample & Two Related Samples

Comparing Means: t-tests for One Sample & Two Related Samples Comparing Means: -Tess for One Sample & Two Relaed Samples Using he z-tes: Assumpions -Tess for One Sample & Two Relaed Samples The z-es (of a sample mean agains a populaion mean) is based on he assumpion

More information

Solutions: Wednesday, November 14

Solutions: Wednesday, November 14 Amhers College Deparmen of Economics Economics 360 Fall 2012 Soluions: Wednesday, November 14 Judicial Daa: Cross secion daa of judicial and economic saisics for he fify saes in 2000. JudExp CrimesAll

More information

Robust estimation based on the first- and third-moment restrictions of the power transformation model

Robust estimation based on the first- and third-moment restrictions of the power transformation model h Inernaional Congress on Modelling and Simulaion, Adelaide, Ausralia, 6 December 3 www.mssanz.org.au/modsim3 Robus esimaion based on he firs- and hird-momen resricions of he power ransformaion Nawaa,

More information

References are appeared in the last slide. Last update: (1393/08/19)

References are appeared in the last slide. Last update: (1393/08/19) SYSEM IDEIFICAIO Ali Karimpour Associae Professor Ferdowsi Universi of Mashhad References are appeared in he las slide. Las updae: 0..204 393/08/9 Lecure 5 lecure 5 Parameer Esimaion Mehods opics o be

More information

Licenciatura de ADE y Licenciatura conjunta Derecho y ADE. Hoja de ejercicios 2 PARTE A

Licenciatura de ADE y Licenciatura conjunta Derecho y ADE. Hoja de ejercicios 2 PARTE A Licenciaura de ADE y Licenciaura conjuna Derecho y ADE Hoja de ejercicios PARTE A 1. Consider he following models Δy = 0.8 + ε (1 + 0.8L) Δ 1 y = ε where ε and ε are independen whie noise processes. In

More information

ACE 562 Fall Lecture 4: Simple Linear Regression Model: Specification and Estimation. by Professor Scott H. Irwin

ACE 562 Fall Lecture 4: Simple Linear Regression Model: Specification and Estimation. by Professor Scott H. Irwin ACE 56 Fall 005 Lecure 4: Simple Linear Regression Model: Specificaion and Esimaion by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Simple Regression: Economic and Saisical Model

More information

The Co-movement of Inflation and the Real Growth of Output *

The Co-movement of Inflation and the Real Growth of Output * THE JOURNAL OF THE KOREAN ECONOMY, Vol. 7, No. 2 (Winer 26), 23-229 The Co-movemen of Inflaion and he Real Growh of Oupu * Jae Ho Yoon ** In order o find ou wheher here really are co-movemens beween inflaion

More information

15. Which Rule for Monetary Policy?

15. Which Rule for Monetary Policy? 15. Which Rule for Moneary Policy? John B. Taylor, May 22, 2013 Sared Course wih a Big Policy Issue: Compeing Moneary Policies Fed Vice Chair Yellen described hese in her April 2012 paper, as discussed

More information

Chapter 16. Regression with Time Series Data

Chapter 16. Regression with Time Series Data Chaper 16 Regression wih Time Series Daa The analysis of ime series daa is of vial ineres o many groups, such as macroeconomiss sudying he behavior of naional and inernaional economies, finance economiss

More information

Estimation Uncertainty

Estimation Uncertainty Esimaion Uncerainy The sample mean is an esimae of β = E(y +h ) The esimaion error is = + = T h y T b ( ) = = + = + = = = T T h T h e T y T y T b β β β Esimaion Variance Under classical condiions, where

More information

Dynamic models for largedimensional. Yields on U.S. Treasury securities (3 months to 10 years) y t

Dynamic models for largedimensional. Yields on U.S. Treasury securities (3 months to 10 years) y t Dynamic models for largedimensional vecor sysems A. Principal componens analysis Suppose we have a large number of variables observed a dae Goal: can we summarize mos of he feaures of he daa using jus

More information

Generalized Least Squares

Generalized Least Squares Generalized Leas Squares Augus 006 1 Modified Model Original assumpions: 1 Specificaion: y = Xβ + ε (1) Eε =0 3 EX 0 ε =0 4 Eεε 0 = σ I In his secion, we consider relaxing assumpion (4) Insead, assume

More information

Types of Exponential Smoothing Methods. Simple Exponential Smoothing. Simple Exponential Smoothing

Types of Exponential Smoothing Methods. Simple Exponential Smoothing. Simple Exponential Smoothing M Business Forecasing Mehods Exponenial moohing Mehods ecurer : Dr Iris Yeung Room No : P79 Tel No : 788 8 Types of Exponenial moohing Mehods imple Exponenial moohing Double Exponenial moohing Brown s

More information

Georey E. Hinton. University oftoronto. Technical Report CRG-TR February 22, Abstract

Georey E. Hinton. University oftoronto.   Technical Report CRG-TR February 22, Abstract Parameer Esimaion for Linear Dynamical Sysems Zoubin Ghahramani Georey E. Hinon Deparmen of Compuer Science Universiy oftorono 6 King's College Road Torono, Canada M5S A4 Email: zoubin@cs.orono.edu Technical

More information

This paper reports the near term forecasting power of a large Global Vector

This paper reports the near term forecasting power of a large Global Vector Commen: Forecasing Economic and Financial Variables wih Global VARs by M. Hashem Pesaran, Till Schuermann and L. Venessa Smih. by Kajal Lahiri, Universiy a Albany, SUY, Albany, Y. klahiri@albany.edu This

More information

A New Unit Root Test against Asymmetric ESTAR Nonlinearity with Smooth Breaks

A New Unit Root Test against Asymmetric ESTAR Nonlinearity with Smooth Breaks Iran. Econ. Rev. Vol., No., 08. pp. 5-6 A New Uni Roo es agains Asymmeric ESAR Nonlineariy wih Smooh Breaks Omid Ranjbar*, sangyao Chang, Zahra (Mila) Elmi 3, Chien-Chiang Lee 4 Received: December 7, 06

More information

Problem Set 5. Graduate Macro II, Spring 2017 The University of Notre Dame Professor Sims

Problem Set 5. Graduate Macro II, Spring 2017 The University of Notre Dame Professor Sims Problem Se 5 Graduae Macro II, Spring 2017 The Universiy of Nore Dame Professor Sims Insrucions: You may consul wih oher members of he class, bu please make sure o urn in your own work. Where applicable,

More information

A note on spurious regressions between stationary series

A note on spurious regressions between stationary series A noe on spurious regressions beween saionary series Auhor Su, Jen-Je Published 008 Journal Tile Applied Economics Leers DOI hps://doi.org/10.1080/13504850601018106 Copyrigh Saemen 008 Rouledge. This is

More information

Smoothing. Backward smoother: At any give T, replace the observation yt by a combination of observations at & before T

Smoothing. Backward smoother: At any give T, replace the observation yt by a combination of observations at & before T Smoohing Consan process Separae signal & noise Smooh he daa: Backward smooher: A an give, replace he observaion b a combinaion of observaions a & before Simple smooher : replace he curren observaion wih

More information

Lecture 3: Exponential Smoothing

Lecture 3: Exponential Smoothing NATCOR: Forecasing & Predicive Analyics Lecure 3: Exponenial Smoohing John Boylan Lancaser Cenre for Forecasing Deparmen of Managemen Science Mehods and Models Forecasing Mehod A (numerical) procedure

More information

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle Chaper 2 Newonian Mechanics Single Paricle In his Chaper we will review wha Newon s laws of mechanics ell us abou he moion of a single paricle. Newon s laws are only valid in suiable reference frames,

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON4325 Moneary Policy Dae of exam: Tuesday, May 24, 206 Grades are given: June 4, 206 Time for exam: 2.30 p.m. 5.30 p.m. The problem se covers 5 pages

More information

GDP PER CAPITA IN EUROPE: TIME TRENDS AND PERSISTENCE

GDP PER CAPITA IN EUROPE: TIME TRENDS AND PERSISTENCE Economics and Finance Working Paper Series Deparmen of Economics and Finance Working Paper No. 17-18 Guglielmo Maria Caporale and Luis A. Gil-Alana GDP PER CAPITA IN EUROPE: TIME TRENDS AND PERSISTENCE

More information

Iterated Multi-Step Forecasting with Model Coefficients Changing Across Iterations. Michal Franta 1

Iterated Multi-Step Forecasting with Model Coefficients Changing Across Iterations. Michal Franta 1 Ieraed Muli-Sep Forecasing wih Model Coefficiens Changing Across Ieraions Michal Frana 1 Absrac: Ieraed muli-sep forecass are usually consruced assuming he same model in each forecasing ieraion. In his

More information

Modal identification of structures from roving input data by means of maximum likelihood estimation of the state space model

Modal identification of structures from roving input data by means of maximum likelihood estimation of the state space model Modal idenificaion of srucures from roving inpu daa by means of maximum likelihood esimaion of he sae space model J. Cara, J. Juan, E. Alarcón Absrac The usual way o perform a forced vibraion es is o fix

More information

How to Deal with Structural Breaks in Practical Cointegration Analysis

How to Deal with Structural Breaks in Practical Cointegration Analysis How o Deal wih Srucural Breaks in Pracical Coinegraion Analysis Roselyne Joyeux * School of Economic and Financial Sudies Macquarie Universiy December 00 ABSTRACT In his noe we consider he reamen of srucural

More information

On Multicomponent System Reliability with Microshocks - Microdamages Type of Components Interaction

On Multicomponent System Reliability with Microshocks - Microdamages Type of Components Interaction On Mulicomponen Sysem Reliabiliy wih Microshocks - Microdamages Type of Componens Ineracion Jerzy K. Filus, and Lidia Z. Filus Absrac Consider a wo componen parallel sysem. The defined new sochasic dependences

More information

The Simple Linear Regression Model: Reporting the Results and Choosing the Functional Form

The Simple Linear Regression Model: Reporting the Results and Choosing the Functional Form Chaper 6 The Simple Linear Regression Model: Reporing he Resuls and Choosing he Funcional Form To complee he analysis of he simple linear regression model, in his chaper we will consider how o measure

More information

Measurement of Potential Output for Turkey: Unobserved Components Model

Measurement of Potential Output for Turkey: Unobserved Components Model Firs Draf Measuremen of Poenial Oupu for Turkey: Unobserved Componens Model Fehi Öğünç, Dilara Ece * Cenral Bank of he Republic of Turkey Saisics Deparmen 06100 Ulus-Ankara Fehi.Ogunc@cmb.gov.r Phone:

More information

Air Traffic Forecast Empirical Research Based on the MCMC Method

Air Traffic Forecast Empirical Research Based on the MCMC Method Compuer and Informaion Science; Vol. 5, No. 5; 0 ISSN 93-8989 E-ISSN 93-8997 Published by Canadian Cener of Science and Educaion Air Traffic Forecas Empirical Research Based on he MCMC Mehod Jian-bo Wang,

More information

RELATIONSHIP BETWEEN INFLATION AND INFLATION UNCERTAINTY: THE CASE OF SERBIA

RELATIONSHIP BETWEEN INFLATION AND INFLATION UNCERTAINTY: THE CASE OF SERBIA Yugoslav Journal of Operaions Research Vol 19 (009), Number 1, 171-183 DOI:10.98/YUJOR0901171M RELATIONSHIP BETWEEN INFLATION AND INFLATION UNCERTAINTY: THE CASE OF SERBIA Zorica MLADENOVIĆ Faculy of Economics,

More information

State-Space Models. Initialization, Estimation and Smoothing of the Kalman Filter

State-Space Models. Initialization, Estimation and Smoothing of the Kalman Filter Sae-Space Models Iniializaion, Esimaion and Smoohing of he Kalman Filer Iniializaion of he Kalman Filer The Kalman filer shows how o updae pas predicors and he corresponding predicion error variances when

More information

The Effect of Nonzero Autocorrelation Coefficients on the Distributions of Durbin-Watson Test Estimator: Three Autoregressive Models

The Effect of Nonzero Autocorrelation Coefficients on the Distributions of Durbin-Watson Test Estimator: Three Autoregressive Models EJ Exper Journal of Economi c s ( 4 ), 85-9 9 4 Th e Au h or. Publi sh ed by Sp rin In v esify. ISS N 3 5 9-7 7 4 Econ omics.e xp erjou rn a ls.com The Effec of Nonzero Auocorrelaion Coefficiens on he

More information

Cointegration and Implications for Forecasting

Cointegration and Implications for Forecasting Coinegraion and Implicaions for Forecasing Two examples (A) Y Y 1 1 1 2 (B) Y 0.3 0.9 1 1 2 Example B: Coinegraion Y and coinegraed wih coinegraing vecor [1, 0.9] because Y 0.9 0.3 is a saionary process

More information

Vector autoregression VAR. Case 1

Vector autoregression VAR. Case 1 Vecor auoregression VAR So far we have focused mosl on models where deends onl on as. More generall we migh wan o consider oin models ha involve more han one variable. There are wo reasons: Firs, we migh

More information

Excel-Based Solution Method For The Optimal Policy Of The Hadley And Whittin s Exact Model With Arma Demand

Excel-Based Solution Method For The Optimal Policy Of The Hadley And Whittin s Exact Model With Arma Demand Excel-Based Soluion Mehod For The Opimal Policy Of The Hadley And Whiin s Exac Model Wih Arma Demand Kal Nami School of Business and Economics Winson Salem Sae Universiy Winson Salem, NC 27110 Phone: (336)750-2338

More information

Financial Econometrics Kalman Filter: some applications to Finance University of Evry - Master 2

Financial Econometrics Kalman Filter: some applications to Finance University of Evry - Master 2 Financial Economerics Kalman Filer: some applicaions o Finance Universiy of Evry - Maser 2 Eric Bouyé January 27, 2009 Conens 1 Sae-space models 2 2 The Scalar Kalman Filer 2 21 Presenaion 2 22 Summary

More information

STATE-SPACE MODELLING. A mass balance across the tank gives:

STATE-SPACE MODELLING. A mass balance across the tank gives: B. Lennox and N.F. Thornhill, 9, Sae Space Modelling, IChemE Process Managemen and Conrol Subjec Group Newsleer STE-SPACE MODELLING Inroducion: Over he pas decade or so here has been an ever increasing

More information

Choice of Spectral Density Estimator in Ng-Perron Test: A Comparative Analysis

Choice of Spectral Density Estimator in Ng-Perron Test: A Comparative Analysis Inernaional Economeric Review (IER) Choice of Specral Densiy Esimaor in Ng-Perron Tes: A Comparaive Analysis Muhammad Irfan Malik and Aiq-ur-Rehman Inernaional Islamic Universiy Islamabad and Inernaional

More information

Stationary Time Series

Stationary Time Series 3-Jul-3 Time Series Analysis Assoc. Prof. Dr. Sevap Kesel July 03 Saionary Time Series Sricly saionary process: If he oin dis. of is he same as he oin dis. of ( X,... X n) ( X h,... X nh) Weakly Saionary

More information

Y, where. 1 Estimate St.error

Y, where. 1 Estimate St.error 1 HG Feb 2014 ECON 5101 Exercises III - 24 Feb 2014 Exercise 1 In lecure noes 3 (LN3 page 11) we esimaed an ARMA(1,2) for daa) for he period, 1978q2-2013q2 Le Y ln BNP ln BNP (Norwegian Model: Y Y, where

More information

Robust critical values for unit root tests for series with conditional heteroscedasticity errors: An application of the simple NoVaS transformation

Robust critical values for unit root tests for series with conditional heteroscedasticity errors: An application of the simple NoVaS transformation WORKING PAPER 01: Robus criical values for uni roo ess for series wih condiional heeroscedasiciy errors: An applicaion of he simple NoVaS ransformaion Panagiois Manalos ECONOMETRICS AND STATISTICS ISSN

More information

Økonomisk Kandidateksamen 2005(II) Econometrics 2. Solution

Økonomisk Kandidateksamen 2005(II) Econometrics 2. Solution Økonomisk Kandidaeksamen 2005(II) Economerics 2 Soluion his is he proposed soluion for he exam in Economerics 2. For compleeness he soluion gives formal answers o mos of he quesions alhough his is no always

More information

Dynamic Models, Autocorrelation and Forecasting

Dynamic Models, Autocorrelation and Forecasting ECON 4551 Economerics II Memorial Universiy of Newfoundland Dynamic Models, Auocorrelaion and Forecasing Adaped from Vera Tabakova s noes 9.1 Inroducion 9.2 Lags in he Error Term: Auocorrelaion 9.3 Esimaing

More information

Semi-Competing Risks on A Trivariate Weibull Survival Model

Semi-Competing Risks on A Trivariate Weibull Survival Model Semi-Compeing Risks on A Trivariae Weibull Survival Model Jenq-Daw Lee Graduae Insiue of Poliical Economy Naional Cheng Kung Universiy Tainan Taiwan 70101 ROC Cheng K. Lee Loss Forecasing Home Loans &

More information

STRUCTURAL CHANGE IN TIME SERIES OF THE EXCHANGE RATES BETWEEN YEN-DOLLAR AND YEN-EURO IN

STRUCTURAL CHANGE IN TIME SERIES OF THE EXCHANGE RATES BETWEEN YEN-DOLLAR AND YEN-EURO IN Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.1-3(004) STRUCTURAL CHANGE IN TIME SERIES OF THE EXCHANGE RATES BETWEEN YEN-DOLLAR AND YEN-EURO IN 001-004 OBARA, Takashi * Absrac The

More information