Economics 311 Fall 2017 Reading on VARs

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1 Economics 311 Fall 2017 Reading on VARs Vecor auoregressions (VARs) have become ubiquious in economeric analses using ime-series daa. Alhough some of he conceps go beond he level of mahemaical and saisical analsis ha we have used in mos of his course, i is imporan ha each suden undersand he basic idea of a VAR (so ha ou know how o inerpre i in papers ou read). Those wih a lile more mah/sa background ma find i useful o look a VARs a bi more mahemaicall. You know ha an auoregression is a regression of Y on pas values of iself. Vecor auoregression eends his o more han one dependen variable. If we have wo variables Y and Z, hen a VAR consiss of wo regressions: one regressing Y on pas values of boh Y and Z and one regressing Z on lagged values of boh Y and Z. If we have hree variables, we would have hree regressions and lagged values of all hree variables on he righ-hand side of all hree regressions. There are wo readings appended in his documen. The firs is si pages from Chaper 16 of Inroducion o Economerics b James Sock and Mark Wason (3 rd ediion), which is a e similar o bu a bi more advanced han Sudenmund. This is quie inuiive, hough here is some algebra involved. I is also somewha limied in no covering some uses of VARs ha are quie imporan. The second documen, which is no required, is a chaper ha I wroe a few ears ago and ha I use in m 312 course. The mah/sa level in his reading is a bi more advanced. There are several conceps in his chaper ha ou can ignore because we have no or will no cover hem in his class. The analsis of he variances rel on formulas ha we have no sudied and on he concep of covariance, which is he basis of calculaing a correlaion coefficien. (The correlaion coefficien beween X and Y is he covariance beween hem divided b he square roo of he produc of heir variances.) I d love for ou o learn hese conceps, bu here jus isn ime in minue class periods. The oher concep ha we haven (e) sudied is saionari and coinegraion. We ll alk briefl abou hese opics soon, bu ou can ignore he secions on vecor error-correcion models. Take wha ou can from his reading.

2 CHAPTER 16 Addiional Topics in Time Series Regression This chaper akes up some furher opics in ime series regression, saring wih forecasing. Chaper 14 considered forecasing a single variable. In pracice, however, ou migh wan o forecas wo or more variables such as he rae of inflaion and he growh rae of he GDP. Secion 16.1 inroduces a model for forecasing muliple variables, vecor auoregressions NARs), in which lagged values of wo or more variables are used o forecas fuure values of hose variables. Chaper 14 also focused on making forecass one period (e.g., one quarer) ino he fuure, bu making forecass wo, hree, or more periods ino he fuure is imporan as well. Mehods for making muliperiod forecass are discussed in Secion Secions 16.3 and 16.4 reurn o he opic of Secion 14.6, sochasic rends. Secion 16.3 inroduces addiional models of sochasic rends and an alernaive es for a uni auoregressive roo. Secion 16.4 inroduces he concep of coinegraion, which arises when wo variables share a common sochasic rend, ha is, when each variable conains a sochasic rend, bu a weighed difference of he wo variables does no. In some ime series daa, especiall financial daa, he variance changes over ime: Someimes he series ehibis high volaili, while a oher imes he volaili is low, so he daa ehibi clusers of volaili. Secion 16.5 discusses volaili clusering and inroduces models in which he variance of he forecas error changes over ime, ha is, models in which he forecas error is condiionall heeroskedasic. Models of condiional heeroskedasici have several applicaions. One applicaion is compuing forecas inervals, where he widh of he inerval changes over ime o reflec periods of high or low uncerain. Anoher applicaion is forecasing he uncerain of reurns on an asse, such as a sock, which in urn can be useful in assessing he risk of owning ha asse Vecor Auoregressions Chaper 14 focused on forecasing he rae of inflaion, bu in reali economic forecasers are in he business of forecasing oher ke macroeconomic variables as well, such as he rae of unemplomen, he growh rae of GDP, and ineres 631

3 632 CHAPTER 16 Addiional Topics in Time Series Regression 16.1 Vecor Auoregressions A vecor auoregression (VAR) is a se of k ime series regressions, in which he regressors are lagged values of all k series. A VAR eends he univariae auoregression o a lis, or "vecor," of ime series variables. When he number of lags in each of he equaions is he same and is equal op, he ssem of equaions is called a VAR (p). In he case of wo ime series variables, Y, and X,, he VAR(p) consiss of he wo equaions Y, = f f3 11Y, f3 1pY,- p + Y11.; 'Y1pX,- p + U1r (16.1) where he {3 's and he 's are unknown coefficiens and u 11 and u 2, are error erms. The VAR assumpions are he ime series regression assumpions of Ke Concep 14.6, applied o each equaion. The coefficiens of a VAR are esimaed b esimaing each equaion b OLS. raes. One approach is o develop a separae forecasing model for each variable using he mehods of Secion Anoher approach is o develop a single model ha can forecas all he variables, which can help o make he forecass muuall consisen. One wa o forecas several variables wih a single model is o use a vecor auoregression (VAR). A VAR eends he univariae auoregression o muliple ime series variables, ha is, i eends he univariae auoregression o a "vecor" of ime series variables. The VAR Model A vecor auoregression (VAR) wih wo ime series variables, Y, and XI' consiss of wo equaions: In one, he dependen variable is i;; in he oher, he dependen variable is X 1 The regressors in boh equaions are lagged values of boh variables. More generall, a VAR wih k ime series variables consiss of k equaions, one for each of he variables; where he regressors in all equaions are lagged values of all he variables. The coefficiens of he VAR are esimaed b esimaing each of he equaions b OLS. VARs are summarized in Ke Concep 16.1.

4 16.1 Vecor Auoregressions 633 Inference in VARs. Under he VAR assumpions, he OLS esimaors are consisen and have a join normal disribuion in large samples. Accordingl, saisical inference proceeds in he usual manner; for eample, 95% confidence inervals on coefficiens can be consruced as he esimaed coefficien ± 1.96 sandard errors. One new aspec of hpohesis esing arises in VARs because a VAR wih k variables is a collecion, or ssem, of k equaions. Thus i is possible o es join hpoheses ha involve resricions across muliple equaions. For eample, in he wo-variable VAR(p) in Equaions (16.1) and (16.2), ou could ask wheher he correc lag lengh is p or p - 1; ha is, ou could ask wheher he coefficiens on Yi-p and X-p are zero in hese wo equaions. The null hpohesis ha hese coefficiens are zero is Ho:f31p = 0, f32p = 0, Yip= 0, and 2P = 0. (16.3) The alernaive hpohesis is ha a leas one of hese four coefficiens is nonzero. Thus he null hpohesis involves coefficiens from boh of he equaions, wo from each equaion. Because he esimaed coefficiens have a joinl normal disribuion in large samples, i is possible o es resricions on hese coefficiens b compuing an F saisic. The precise formula for his saisic is complicaed because he noaion mus handle muliple equaions, so we omi i. In pracice, mos modern sofware packages have auomaed procedures for esing hpoheses on coefficiens in ssems of muliple equaions. How man variables should be included in a VAR? The number of coefficiens in each equaion of a VAR is proporional o he number of variables in he VAR. For eample, a VAR wih five variables and four lags will have 21 coefficiens (four lags each of five variables, plus he inercep) in each of he five equaions, for a oal of 105 coefficiens! Esimaing all hese coefficiens increases he amoun of esimaion error enering a forecas, which can resul in a deerioraion of he accurac of he forecas. The pracical implicaion is ha one needs o keep he number of variables in a VAR small and, especiall, o make sure ha he variables are plausibl relaed o each oher so ha he will be useful for forecasing one anoher. For eample, we know from a combinaion of empirical evidence (such as ha discussed in Chaper 14) and economic heor ha he inflaion rae, he unemplomen rae, and he shor-erm ineres rae are relaed o one anoher, suggesing ha hese variables could help o forecas one anoher oher in a VAR. Including an unrelaed

5 634 CHAPTER 16 Addiional Topics in Time Series Reg ression variable in a VAR, however, inroduces esimaion error wihou adding predicive conen, hereb reducing forecas accurac. Deermining lag lenghs in VARs. 1 Lag lenghs in a VAR can be deermined using eiher F-ess or informaion crieria. The informaion crierion for a ssem of equaions eends he singleequaion informaion crierion in Secion To define his informaion crierion we need o adop mari noaion. Le 2:u be he k k covariance mari of he A VAR errors and le 2: u be he esimae of he covariance mari where he i, j elemen of ~u is L-~ 1 uiruj1, where ui is he OLS residual from he ih equaion and uj is he OLS residual from he / h equaion. The BIC for he VAR is ln(t) A BIC(p) = ln[de(2:u)] + k(kp + 1)- T- ' (16.4) A where de(2:u) is he deerminan of he mari 2:w The AIC is compued using Equaion (16.4), modified b replacing he erm "ln(t)" b "2". The epression for he BIC for he k equaions in he VAR in Equaion (16.4) eends he epression for a single equaion given in Secion When here is a single equaion, he firs erm simplifies o ln[ SSR(p )IT]. The second erm in Equaion (16.4) is he penal for adding addiional regressors; k(kp + 1) is he oal number of regression coefficiens in he VAR (here are k equaions, each of which has an inercep and p lags of each of he k ime series variables). Lag lengh esimaion in a VAR using he BIC proceeds analogousl o he single equaion case: Among a se of candidae values of p, he esimaed lag lengh p is he value of p ha minimizes BIC(p ). A Using VARs for causal analsis. The discussion so far has focused on using VARs for forecasing. Anoher use of VAR models is for analzing causal relaionships among economic ime series variables; indeed, i was for his purpose ha VARs were firs inroduced o economics b he economerician and macroeconomis Chrisopher Sims (1980). The use of VARs for causal inference is known as srucural VAR modeling, "srucural" because in his applicaion VARs are used o model he underling srucure of he econom. Srucural VAR analsis uses he echniques inroduced in his secion in he cone of forecasing, plus some addiional ools. The bigges concepual difference beween using VARs for forecasing and using hem for srucural modeling, however, is ha srucural 1 This secion uses marices and ma be skipped for less mahemaical reamens.

6 16.1 Vecor Auoregressions 635 modeling requires ver specific assumpions, derived from economic heor and insiuional knowledge, of wha is eogenous and wha is no. The discussion of srucural VARs is bes underaken in he cone of esimaion of ssems of simulaneous equaions, which goes beond he scope of his book. For an inroducion o using VARs for forecasing and polic analsis, see Sock and Wason (2001). For addiional mahemaical deail on srucural VAR modeling, see Hamilon (1994) or Wason (1994). A VAR Model of he Raes of Inflaion and Unemplomen As an illusraion, consider a wo-variable VAR for he inflaion rae, lnf, and he rae of unemplomen, Unempr As in Chaper 14, we rea he rae of inflaion as having a sochasic rend, so i is appropriae o ransform i b compuing is firs difference, D.lnfr. The VAR for D.lnfr and Unemp consiss of wo equaions: one in which D.lnfr is he dependen variable and one in which Unemp is he dependen variable. The regressors in boh equaions are lagged values of D.lnfr and Unemp. Because of he apparen break in he Phillips curve in he earl 1980s found in Secion 14.7 using he QLR es, he VAR is esimaed using daa from 1982:1 o 2004:IV. The firs equaion of he VAR is he inflaion equaion: - D.lnfr = D.lnfr_1-0.64D.lnfr_2 - O.l3D.lnfr_ 3 - O.l3D.lnfr_ 4 (0.55) (0.12) (0.10) (0.11) (0.09) Unemp- l Unempr Unemp 1 _ Unempr_ 4. (16.5) (0.58) (0.94) (1.07) (0.55) The adjused R 2 is R 2 = The second equaion of he VAR is he unemplomen equaion, in which he regressors are he same as in he inflaion equaion bu he dependen variable is he unemplomen rae: - Unemp 1 = D.lnfr_ D.lnfr_ D.lnfr_ D.lnfr_ 4 (0.12) (0.017) (0.018) (0.018) (0.014) + l.52unemp-l Unempr Unemp_ 3 + O.l6Unempr_ 4. (16.6) (0.11) (0.18) (0.21) (0.11) The adjused R 2 is R 2 =

7 636 CHAPTER 16 Addiional Topics in Time Series Regression Equaions (16.5) and (16.6), aken ogeher, are a VAR( 4) model of he change in he rae of inflaion,!ilnf, and he unemplomen rae, Unemp 1 These VAR equaions can be used o perform Granger causali ess. The F saisic esing he null hpohesis ha he coefficiens on Unemp _ 1 1, Unemp 1 _ 2, Unemp 1 _ 3, and Unemp 1-4 are zero in he inflaion equaion [Equaion (16.5)] is 11.04, which has a p-value less han Thus he null hpohesis is rejeced, so we can conclude ha he unemplomen rae is a useful predicor of changes in inflaion, given lags in inflaion (ha is, he unemplomen rae Granger-causes changes in inflaion). The F-saisic esing he hpohesis ha he coefficiens on he four lags of!ilnf are zero in he unemplomen equaion [Equaion (16.6)] is 0.16, which has a p-value of Thus he change in he inflaion rae does no Granger-cause he unemplomen rae a he 10% significance level. Forecass of he raes of inflaion and unemplomen one period ahead are obained eacl as discussed in Secion The forecas of he change of inflaion from 2004:IV o 2005:!, based on Equaion (16.5), is 11In.f2oos:Il2004:IV -- = percenage poin. A similar calculaion using Equaion (16.6) gives a forecas of he unemplomen rae in 2005:! based on daa hrough 2004:IV of -Unemp 20 os:11 20 o 4 :1v = 5.4%, ver close o is acual value, Unemp 20 os:i = 5.3% Muliperiod Forecass The discussion of forecasing so far has focused on making forecass one period in advance. Ofen, however, forecasers are called upon o make forecass furher ino he fuure. This secion describes wo mehods for making muliperiod forecass. The usual mehod is o consruc "ieraed" forecass, in which a one-periodahead model is ieraed forward one period a a ime, in a wa ha is made precise in his secion. The second mehod is o make "direc" forecass b using a regression in which he dependen variable is he muliperiod variable ha one wans o forecas. For reasons discussed a he end of his secion, in mos applicaions he ieraed mehod is recommended over he direc mehod. Ieraed Muliperiod Forecass The essenial idea of an ieraed forecas is ha a forecasing model is used o make a forecas one period ahead, for period T + 1 using daa hrough period T. The model hen is used o make a forecas for dae T + 2 given he daa hrough dae T, where he forecased value for dae T + 1 is reaed as daa for he purpose of making he forecas for period T + 2. Thus he one-period-ahead forecas

8 CHAPTER 5 Vecor Auoregression and Vecor Error-Correcion Models Vecor auoregression (VAR) was inroduced b Sims (1980) as a echnique ha could be used b macroeconomiss o characerize he join dnamic behavior of a collecion of variables wihou requiring srong resricions of he kind needed o idenif underling srucural parameers. I has become a prevalen mehod of ime-series modeling. Alhough esimaing he equaions of a VAR does no require srong idenificaion assumpions, some of he mos useful applicaions of he esimaes, such as calculaing impulseresponse funcions (IRFs) or variance decomposiions do require idenifing resricions. A pical resricion akes he form of an assumpion abou he dnamic relaionship beween a pair of variables, for eample, ha affecs onl wih a lag, or ha does no affec in he long run. A VAR ssem conains a se of m variables, each of which is epressed as a linear funcion of p lags of iself and of all of he oher m 1 variables, plus an error erm. (I is possible o include eogenous variables such as seasonal dummies or ime rends in a VAR, bu we shall focus on he simple case.) Wih wo variables, and, an order-p VAR would be he wo equaions =β +β + +β +β + +β + v p p 1 1 p p =β +β + +β +β + +β + v p p 1 1 p p. (5.1) We adop he subscrip convenion ha β p represens he coefficien of in he equaion for a lag p. If we were o add anoher variable z o he ssem, here would be a hird equaion for z and erms involving p lagged values of z, for eample, β zp, would be added o he righhand side of each of he hree equaions. A ke feaure of equaions (5.1) is ha no curren variables appear on he righ-hand side of an of he equaions. This makes i plausible, hough no alwas cerain, ha he regressors of (5.1) are weakl eogenous and ha, if all of he variables are saionar and ergodic,

9 70 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models OLS can produce asmpoicall desirable esimaors. Variables ha are known o be eogenous a common eample is seasonal dumm variables ma be added o he righ-hand side of he VAR equaions wihou difficul, and obviousl wihou including addiional equaions o model hem. Our eamples will no include such eogenous variables. The error erms in (5.1) represen he pars of and ha are no relaed o pas values of he wo variables: he unpredicable innovaion in each variable. These innovaions will, in general, be correlaed wih one anoher because here will usuall be some endenc for movemens in and o be correlaed, perhaps because of a conemporaneous causal relaionship (or because of he common influence of oher variables). A ke disincion in undersanding and appling VARs is beween he innovaion erms v in he VAR and underling eogenous, orhogonal shocks o each variable, which we shall call ε. The innovaion in is he par of ha canno be prediced b pas values of and. Some of his unpredicable variaion in ha we measure b v is surel due o ε, an eogenous shock o ha is has no relaionship o wha is happening wih or an oher variable ha migh be included in he ssem. However, if has a conemporaneous effec on, hen some par of v will be due o he indirec effec of he curren shock o, ε, which eners he equaion in (5.1) hrough he error erm because curren is no allowed o be on he righ-hand side. We will sud in he ne secion how, b making idenifing assumpions, we can idenif he eogenous shocks ε from our esimaes of he VAR coefficiens and residuals. Correlaion beween he error erms of wo equaions, such as ha presen in (5.1), usuall means ha we can gain efficienc b using he seemingl unrelaed regressions (SUR) ssem esimaor raher han esimaing he equaions individuall b OLS. However, he VAR ssem conforms o he one ecepion o ha rule: he regressors of all of he equaions are idenical, meaning ha SUR and OLS lead o idenical esimaors. The onl siuaion in which we gain b esimaing he VAR as a ssem of seemingl unrelaed regressions is when we impose resricions on he coefficiens of he VAR, a case ha we shall ignore here. When he variables of a VAR are coinegraed, we use a vecor error-correcion (VEC) model. A VEC for wo variables migh look like =β +β + +β +γ + +γ p p 1 1 p p ( ) λ α α + v =β +β + +β +γ + +γ p p 1 1 p p ( ) λ α α v, (5.2) where =α 0 +α 1 is he long-run coinegraing relaionship beween he wo variables and λ and λ are he error-correcion parameers ha measure how and reac o deviaions from long-run equilibrium.

10 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models 71 When we appl he VEC model o more han wo variables, we mus consider he possibili ha more han one coinegraing relaionship eiss among he variables. For eample, if,, and z all end o be equal in he long run, hen = and = z (or, equivalenl, = z ) would be wo coinegraing relaionships. To deal wih his siuaion we need o generalize he procedure for esing for coinegraing relaionships o allow more han one coinegraing equaion, and we need a model ha allows muliple error-correcion erms in each equaion. 5.1 Forecasing and Granger Causali in a VAR In order o idenif srucural shocks and heir dnamic effecs we mus make addiional idenificaion assumpions. However, a simple VAR ssem such as (5.1) can be used for wo imporan economeric asks wihou making an addiional assumpions. We can use (5.1) as a convenien mehod o generae forecass for and, and we can aemp o infer informaion abou he direcion or direcions of causali beween and using he echnique of Granger causali analsis Forecasing wih a VAR The srucure of equaions (5.1) is designed o model how he values of he variables in period are relaed o pas values. This makes he VAR a naural for he ask of forecasing he fuure pahs of and condiional on heir pas hisories. Suppose ha we have a sample of observaions on and ha ends in period T, and ha we wish o forecas heir values in T + 1, T + 2, ec. To keep he algebra simple, suppose ha p = 1, so here is onl one lagged value on he righ-hand side. For period T + 1, our VAR is =β +β +β + v T T 1 T T + 1 =β +β +β + v. T T 1 T T + 1 (5.3) Taking he epecaion condiional on he relevan informaion from he sample ( T and T ) gives ( T + 1 T, T ) =β 0 +β 1 T +β 1 T + ( T + 1 T, T ) ( T + 1 T T ) =β 0 +β 1 T +β 1 T + ( T + 1 T T ) E E v E, E v,. (5.4) The condiional epecaion of he VAR error erms on he righ-hand side mus be zero in order for OLS o esimae he coefficiens consisenl. Wheher or no his assumpion is valid will depend on he serial correlaion properies of he v erms we have seen ha seriall correlaed errors and lagged dependen variables of he kind presen in he VAR can be a oic combinaion.

11 72 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models j Thus, we wan o make sure ha E( v v 1 v 1), = 0. As we saw in an earlier chaper, adding lagged values of and can ofen eliminae serial correlaion of he error, and his mehod is now more common han using GLS procedures o correc for possible auocorrelaion. We assume ha our VAR ssem has sufficien lag lengh ha he error erm is no seriall correlaed, so ha he condiional epecaion of he error erm for all periods afer T is zero. This means ha he final erm on he righ-hand side of each equaion in (5.4) is zero, so ( +, ) ( ) E =β +β +β T 1 T T 0 1 T 1 T E, =β +β +β. T + 1 T T 0 1 T 1 T (5.5) If we knew he β coefficiens, we could use (5.5) o calculae a forecas for period T + 1. Naurall, we use our esimaed VAR coefficiens in place of he rue values o calculae our predicions (, ) ( ) P ˆ =β ˆ +β ˆ +βˆ T + 1 T T T + 1 T 0 1 T 1 T P, ˆ =β ˆ +β ˆ +βˆ. T + 1 T T T + 1 T 0 1 T 1 T (5.6) The forecas error in he predicions in (5.6) will come from wo sources: he unpredicable period T + 1 error erm and he errors we make in esimaing he β coefficiens. Formall, ( ˆ ) ( ˆ ) ( ˆ ) ( ˆ ) ( ˆ ) ( ˆ ) ˆ = β β + β β + β β + v T + 1 T + 1 T T 1 1 T T + 1 ˆ = β β + β β + β β + v. T + 1 T + 1 T T 1 1 T T + 1 If our esimaes of he β coefficiens are consisen and here is no serial correlaion in v, hen he epecaion of he forecas error is asmpoicall zero. The variance of he forecas error is 2 2 ( ˆ ) ( ˆ ) ( ˆ ) ( ˆ T + 1 T + 1 T = β 0 + β 1 T + β1 ) T + 2cov ( βˆ 0, β ˆ 1) + 2cov ( βˆ 0, β ˆ 1) + 2cov ( βˆ ˆ 1, β 1) + var ( vt + 1 ) 2 2 ( ˆ 1 1 ) ( ˆ 0 ) ( ˆ 1 ) ( ˆ T + T + T = β + β T + β1 ) T + 2cov ( βˆ ˆ 0, 1) 2cov ( ˆ ˆ 0, 1) 2cov ˆ β T + β β T + ( β1, βˆ 1) + var ( v T + 1 ). var var var var var var var var T T T T T T As our consisen esimaes of he β coefficiens converge o he rue values (as T ges large), all of he erms in his epression converge o zero ecep he las one. Thus, in calculaing

12 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models 73 he variance of he forecas error, he error in esimaing he coefficiens is ofen negleced, giving var ( ˆ + + ) var ( v + ) ( ˆ + + ) ( v + ) σ 2 T 1 T 1 T T 1 v, var var σ. 2 T 1 T 1 T T 1 v, (5.7) One of he mos useful aribues of he VAR is ha i can be used recursivel o eend forecass ino he fuure. For period T + 2, so b recursive epecaions (, ) ( ) E =β +β +β T + 2 T + 1 T T T + 1 E, =β +β +β, T + 2 T + 1 T T T + 1 (, ) =β +β (, ) +β (, ) E E E T + 2 T T 0 1 T + 1 T T 1 T + 1 T T ( ) ( ) =β +β β +β +β +β β +β +β T 1 T T 1 T (, ) =β +β (, ) +β (, ) E E E T + 2 T T 0 1 T + 1 T T 1 T + 1 T T ( ) ( ) =β +β β +β +β +β β +β +β T 1 T T 1 T The corresponding forecass are again obained b subsiuing coefficien esimaes o ge. (, ) ( ) P ˆ =β ˆ +β ˆ ˆ +βˆ ˆ T + 2 T T T + 2 T 0 1 T + 1 T 1 T + 1 T P, ˆ =β ˆ +β ˆ ˆ +βˆ ˆ. T + 2 T T T + 2 T 0 1 T + 1 T 1 T + 1 T (5.8) If we once again ignore error in esimaing he coefficiens, hen he wo-period-ahead forecas error in (5.8) is ( ) ( ) ˆ β +β + v T + 2 T + 2 T 1 T + 1 T + 1 T 1 T + 1 T + 1 T T + 2 β v +β v + v 1 T T + 1 T + 2 ( ) ( ) ˆ β +β + v T + 2 T + 2 T 1 T + 1 T + 1 T 1 T + 1 T + 1 T T + 2 β v +β v + v 1 T T + 1 T + 2 In general, he error erms for period T + 1 will be correlaed across equaions, so he variance of he wo-period-ahead forecas is approimael,. ( ˆ + + ) var β σ +β σ + 2β β σ +σ T 2 T 2 T 1 v, 1 v, 1 1 v, v, ( ˆ + + ) ( ) = 1+β σ +β σ + 2 β β σ, v, 1 v, 1 1 v, var β σ +β σ + 2β β σ +σ T 2 T 2 T 1 v, 1 v, 1 1 v, v, ( ) =β σ + 1+β σ + 2 β β σ v, 1 v, 1 1 v, (5.9)

13 74 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models The wo-period-error forecas error has larger variance han he one-period-ahead error because he errors ha we make in forecasing period T + 1 propagae ino errors in he forecas for T + 2. As our forecas horizon increases, he variance ges larger and larger, reflecing our inabili o forecas a grea disance ino he fuure even if (as we have opimisicall assumed here) we have accurae esimaes of he coefficiens. The calculaions in equaion (5.9) become increasingl comple as one considers longer forecas horizons. Including more han wo variables in he VAR or more han one lag on he righ-hand side also increases he number of erms in boh (5.8) and (5.9) rapidl. We are forunae ha modern saisical sofware, including Saa, has auomaed hese asks for us. We now discuss he basics of esimaing a VAR in Saa Esimaing and forecasing a simple VAR in Saa A one level, esimaing a VAR is a simple ask because i is esimaed wih OLS, he Saa regression command will handle he esimaion. However, for everhing we do wih a VAR beond esimaion, we need o consider he ssem as a whole, so Saa provides a famil of procedures ha are ailored o he VAR applicaion. The wo essenial VAR commands are var and varbasic. The laer is eas o use (poeniall as eas as lising he variables ou wan in he ssem), bu lacks he fleibili of he former o deal wih asmmeric lag paerns across equaions, addiional eogenous variables ha have no equaions of heir own, and coefficien consrains across equaions. We discuss var firs; laer in he chaper we will go back and show he use of he simpler varbasic command. (As alwas, onl simple eamples of Saa commands are shown here. The curren Saa manual available hrough he Saa Help menu conains full documenaion of all opions and variaions, along wih addiional eamples.) To run a simple VAR for variables and wih wo lags or each variable in each equaion and no consrains or eogenous variables, we can simpl pe var, lags(1/2) Noice ha we need 1/2 raher han jus 2 in he lag specificaion because we wan lags 1 hrough 2, no jus he second lag. The oupu from his command will give he β coefficiens from OLS esimaion of he wo regressions, plus some ssem and individual-equaion goodness-of-fi saisics. Once we have esimaed a VAR model, here are a varie of ess ha can be used o help us deermine wheher we have a good model. In erms of model validaion, one imporan proper for our esimaes o have desirable asmpoic properies is ha he model j mus be sable in he sense ha he esimaed coefficiens impl ha + s/ v and j + s/ v (j =, ) become small as s ges large. If hese condiions do no hold, hen he VAR implies ha and are no joinl ergodic: he effecs of shocks do no die ou.

14 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models 75 The Saa command varsable (which usuall needs no argumens or opions) calculaes he eigenvalues of a companion mari for he ssem. If all of he calculaed eigenvalues (which can be comple) are less han one (in modulus, if he have imaginar pars), hen he model is sable. This condiion is he vecor eension o he saionari condiion ha he roos of an auoregressive polnomial of a single variable lie ouside he uni circle. If he varsable command repors an eigenvalue wih modulus greaer han one, hen he VAR is unsable and forecass will eplode. This can arise when he variables in he model are non-saionar or when he model is misspecified. Differencing (and perhaps, afer checking for coinegraion, using a VEC) ma ield a sable ssem. If he VAR is sable, hen he main issue in specificaion is lag lengh. We discussed lag lengh issues above in he cone of single-variable disribued lag models. The issues and mehods in a VAR are similar, bu appl simulaneousl o all of he equaions of he model and all of he variables, since we convenionall choose a common lengh for all lags. Forecasing wih a VAR assumes ha here is no serial correlaion in he error erm. The Saa command varlmar implemens a VAR version of he Lagrange muliplier es for se- j j cov v, v = 0 wih rial correlaion in he residual. This command ess he null hpoheses ( s) j indeing he variables of he model. The main opion in he varlmar command allows ou o specif he highes order of auocorrelaion (he defaul is 2) ha ou wan o es in he residual. For eample varlmar, mlag (4) would perform he above es individuall for s = 1, s = 2, s = 3, and s = 4. If he Lagrange muliplier es rejecs he null hpohesis of no serial correlaion, hen ou ma wan o include addiional lags in he equaions and perform he es again. The Akaike Informaion Crierion (AIC) and Schwarz-Baesian Informaion Crierion (SBIC) are ofen used o choose he opimal lag lengh in single-variable disribued-lag models. These and oher crieria have been eended o he VAR case and are repored b he varsoc command. Tping varsoc, malag(4) ells Saa o esimae VARs for lag lengh 0, (jus consans), 1, 2, 3, and 4, and compue he log-likelihood funcion and various informaion crieria for each choice. The oupu of he varsoc command includes likelihood-raio es saisics for he null hpohesis ha he ne lag is zero. The opimal lag lengh b each crierion is indicaed b an aserisk in he able of resuls. In general, he various crieria will no agree, so ou will need o eercise some degree of judgmen in choosing among he recommendaions. Anoher wa of deciding on lag lengh is o use sandard (Wald) es saisics o es wheher all of he coefficiens a each lag are zero. Saa auomaes his in he varwle command, which requires no opions. Once ou have seled on a VAR model ha includes an appropriae number of lags, is sable, and has seriall uncorrelaed errors, ou can proceed o use he model o generae forecass. There are wo commands for creaing and graphing forecass. The fcas compue command calculaes he predicions of he VAR and sores hem in a se of new vari-

15 76 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models ables. If ou wan our forecass o sar in he period immediael following he las period of he esimaing sample, hen he onl opion ou need in he fcas compue command is sep(#), wih which ou specif he forecas horizon (how man periods ahead ou wan o forecas). The forecas variables are sored in variables ha aach a prefi ha ou specif o he names of he VAR variables being forecased. For eample, o forecas our VAR model for 10 periods beginning afer he esimaing sample and sore prediced values of in pred_ and in pred_, ou could pe fcas compue pred_, sep(10) The fcas compue command also generaes sandard errors of he forecass and uses hem o calculae upper and lower confidence bounds. Afer compuing he forecass, ou can graph hem along wih he confidence b ping fcas graph pred_*. If ou have acual observed values for he variables for he forecas period, he can be added o he graph wih he observed opion (separaed from he command b a comma, as alwas wih Saa opions) Granger causali One of he firs, and undeniable, maims ha ever economerician or saisician is augh is ha correlaion does no impl causali. Correlaion or covariance is a smmeric, bivariae relaionship; cov (, ) = cov (, ). We canno, in general, infer anhing abou he eisence or direcion of causali beween and b observing non-zero covariance. Even if our saisical analsis is successful in esablishing ha he covariance is highl unlikel o have occurred b chance, such a relaionship could occur because causes, because causes, because each causes he oher, or because and are responding o some hird variable wihou an causal relaionship beween hem. However, Clive Granger defined he concep of Granger causali, which, under some conroversial assumpions, can be used o shed ligh on he direcion of possible causali beween pairs of variables. The formal definiion of Granger causali asks wheher pas values of aid in he predicion of, condiional on having alread accouned for he effecs on of pas values of (and perhaps of pas values of oher variables). If he do, he is said o Granger cause. The VAR is a naural framework for eamining Granger causali. Consider he wovariable ssem in equaions (5.1). The firs equaion models as a linear funcion of is own pas values, plus pas values of. If Granger causes (which we wrie as ), hen some or all of he lagged values have non-zero effecs: lagged affecs condiional on he effecs of lagged. Tesing for Granger causali in (5.1) amouns o esing he join blocks of coefficiens β s and β s o see if he are zero. The null hpohesis ( does no Granger cause ) in his VAR is H : β =β =... =β = 0, p

16 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models 77 which can be esed using a sandard Wald F or χ 2 es. Similarl, he null hpohesis can be epressed in he VAR as H : β =β =... =β = p Running boh of hese ess can ield four possible oucomes, as shown in Table 5-1: no Granger causali, one-wa Granger causali in eiher direcion, or feedback, wih Granger causali running boh was. Table 5-1. Granger causali es oucomes Fail o rejec: β 1 =β 2 =... =β s = 0 Rejec: β 1 =β 2 =... =β s = 0 Fail o rejec: β 1 =β 2 =... =β s = 0 (no Granger causali) ( Granger causes ) Rejec: β 1 =β 2 =... =β s = 0 ( Granger causes ) (bi-direcional Granger causali, or feedback) There are muliple was o perform Granger causali ess beween a pair of variables, so no resul is unique or definiive. Wihin he wo-variable VAR, one ma obain differen resuls wih differen lag lenghs p. Moreover, including addiional variables in he VAR ssem ma change he oucome of he Wald ess ha underpin Granger causali. In a hreevariable VAR, here are hree pairs of variables, (, ), (, z), and (, z) ha can be esed for Granger causali in boh direcions: si ess wih 36 possible combinaions of oucomes. The effec of lagged on can disappear when lagged values of a hird variable z are added o he regression. For eample, if z and z, hen omiing z from he VAR ssem could lead us o conclude ha even if here is no direc Granger causali in he larger ssem. Is Granger causali reall causali? Obviousl, if he maim abou correlaion and causali is rue, hen here mus be somehing rick happening, and indeed here is. Granger causali ess wheher lagged values of one variable condiionall help predic anoher variable. Under wha condiions can we inerpre his as causali? Two assumpions are sufficien. Firs, we use emporal priori in an imporan wa in Granger causali. We inerpre correlaion beween lagged and he par of curren ha is orhogonal o is own lagged values as. Could his insead reflec curren causing lagged? To rule his ou, inerpreing Granger causali as more general causali requires ha we assume ha he fuure canno cause he presen. While his ma ofen be a reasonable assumpion, modern economic heor has shown us ha epecaions of fuure variables (which are likel o be corre-

17 78 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models laed wih he fuure variables hemselves) can change agens curren choices, which migh resul in causali ha would appear o violae his assumpion. Second, an causal relaionship ha is sricl immediae in he sense ha a change in leads o a change in bu no change in an fuure values of would fl under he radar of a Granger causali es, which onl measures and ess lagged effecs. Mos causal economic relaionships are dnamic in ha effecs are no full realized wihin a single ime period, so his difficul ma no presen a pracical problem in man cases. To summarize, we mus be ver careful in inerpreing he resul of Granger causali ess o reflec rue causali in an non-economeric sense. Onl if we can rule ou he possibili of he fuure causing he presen and sricl immediae causal effecs can we confidenl hink of Granger causali as causali. Saa implemens Granger causali ess auomaicall wih vargranger, which ess all of he pairs of variables in a VAR for Granger causali. In ssems wih more han wo variables, i also ess he join hpohesis ha all of he oher variables fail o Granger cause each variable in urn. This join es amouns o esing wheher all of he lagged erms oher han hose of he dependen variable have zero effecs. 5.2 Idenificaion of Srucural Shocks in a VAR Ssem Two variables ha have a dnamic relaionship in a VAR ssem are also likel o have some degree of conemporaneous associaion. This will be refleced in correlaion in he innovaion erms v in (5.1) because here is no oher place in he equaions for his associaion o be manifesed. I is naural o hink of he VAR ssem as he reduced form of a srucural model in which conemporaneous effecs among he variables have been solved ou. We now consider a simple wo-equaion srucural model in which affecs conemporaneousl bu has no immediae effec on. Suppose ha wo variables, and, evolve over ime according o he srucural model =α +α +θ +ε =φ +φ +δ +δ +ε , (5.10) where he ε erms are eogenous whie-noise shocks o and ha are orhogonal (uncorrelaed) o one anoher: var ( ε ) =σ, var ( ), cov ε, ε = 0. The ε shocks 2 2 are ε =σ and ( ) changes in he variables ha come from ouside he VAR ssem. Because he are (assumed o be) eogenous, we can measure he effec of an eogenous change in on he pah of and b looking a he dnamic marginal effecs of ε, for eample, + s/ ε. This is he ke disincion beween he VAR error erms v and he eogenous srucural shocks ε depending on he idenifing assumpions we make, we canno generall inerpre a change in v as an eogenous shock o one variable.

18 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models 79 We assume ha and are saionar and ergodic, which imposes resricions on he auoregressive coefficiens of he model. 1 The firs equaion of (5.10) is alread in he form of a VAR equaion: i epresses he curren value of as a funcion of lagged values of and. If we solve (5.10) b subsiuing for in he second equaion using he firs equaion, we ge ( ) 1 1 ( ) ( ) ( ) ( ) =φ +φ +δ α +α +θ +ε +δ +ε = φ +δ α + φ +δ θ + δ +δ α + ε +δ ε , (5.11) which also has he VAR form. Thus, we can wrie he reduced-form ssem of (5.10) as =β +β +β + v =β +β +β + v , (5.12) wih β =φ +δ α β =α v β =φ +δ θ β =θ β =δ +δ α β =α =ε +δ0 ε v =ε. (5.13) Given our assumpions abou he disribuions of he eogenous shocks ε, we can deermine he variances and covariance of he VAR error erms v as var var 2 ( v ) = σ ( v ) =σ +δ0σ ( v v ) E( v v ) E ( ) 2 cov, = = ε ε +δε 0 =δσ 0. (5.14) Le s now consider wha can be esimaed using he VAR ssem (5.12) and o wha een hese esimaes allow us o infer value of he parameers in he srucural ssem (5.10). In erms of coefficiens, here are si β coefficiens ha can be esimaed in he VAR and seven srucural coefficiens in (5.10). This seems a pessimisic sar o he ask of idenificaion. However, we can also esimae he variances and covariance of he v erms using he VAR residuals: var ( v ), var ( v ), and cov (, ) v v. Condiions (5.14) allow us o esimae hree parameers σ 2, 2 σ, δ 0 from he hree esimaed variances and covariance: 1 In a single-variable auoregressive model, we would require ha he coefficien φ1 for 1 be in he range ( 1, 1). The corresponding condiions in he vecor seing are more involved, bu similar in naure.

19 80 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models ( v ) cov ( v, v ) var ( v ) ( ) ˆ v δ0 ( v ) 2 σ ˆ = var, δ ˆ =, σ ˆ = var var. (5.15) Armed wih an esimae of δ 0 from he covariance erm, we can now use he si β coefficiens o esimae he remaining si srucural coefficiens using (5.13). The ssem is jus idenified. So how did we manage o achieve idenificaion hrough his back-door covariance mehod? Le s consider wh v and v, which are he innovaions in and ha canno be prediced from pas values, migh be correlaed. In a general model he could be correlaed because (1) has an effec on, (2) has an effec on, or (3) he eogenous srucural shocks o and, ε and ε, are correlaed wih one anoher. Our VAR esimaes give us no mehod of discriminaing among hese hree possible sources of correlaion, so wihou ruling ou wo of hem we canno achieve idenificaion. In he model of (5.10), we have ruled ou, b assumpion, (2) and (3): does no affec and he eogenous shocks o and are orhogonal. Thus, we are inerpreing he covariance beween v and v as reflecing he conemporaneous effec of on. This allows us o idenif he coefficien measuring cov v, v as we do in he second equaion of (5.15). his effec δ 0 in (5.10) based on he ( ) This idenifing assumpion also allows us o reconsruc esimaes of he eogenous srucural shocks ε from he residuals of he VAR: ε ˆ = vˆ ε ˆ = ˆ ˆ. ˆ v δ0v This makes i clear ha we are inerpreing he VAR residual for o be an eogenous, srucural shock o. In order o erac he srucural shock o, we subrac he par of v ˆ, δ ˆ ˆ ˆ ˆ 0v =δε 0, ha is due o he effec of he shock o on. From an economeric sandpoin, we could equall well make he opposie assumpion, assuming ha affecs raher han vice versa, which would inerpre v ˆ as ε ˆ and calculae ε ˆ as he par of v ˆ ha is no eplained b v ˆ. Choosing which inerpreaion o use mus be done on he basis of heor: which variable is more plausibl eogenous wihin he immediae period. We ma ge differen resuls depending on which idenificaion assumpion we choose, so if here is no clear choice i ma be useful o eamine wheher resuls are robus across differen choices. Idenificaion of he underling srucural shocks is necessar if we are o esimae he effecs of an eogenous shock o a single variable on he dnamic pahs of all of he variables

20 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models 81 of he ssem, which we call impulse-response funcions (IRFs). We discuss he compuaion and inerpreaion of IRFs in he ne secion. In our eample, we idenified shocks b limiing he conemporaneous effecs among he variables. Wih onl wo variables, here are wo possible choices: (1) he assumpion we made, ha affecs immediael bu does no have an immediae effec on or (2) he opposie assumpion, ha affecs immediael bu does no affec ecep wih a lag. We can hink of he choice beween hese alernaives as an ordering of he variables, wih he variables ling higher in he order having insananeous effecs on hose lower in he order, bu he lower variables onl affecing hose above hem wih a lag. This ordering or orhogonalizaion sraeg of idenificaion eends direcl o VAR ssems wih more han wo variables. Sims s seminal VAR ssem had si variables, which he ordered as he mone suppl, real oupu, unemplomen, wages, prices, and impor prices. B adoping his ordering, Sims was imposing an arra of idenifing resricions abou he conemporaneous effecs of shocks on he variables of he ssem. The mone shock, because i was a he op of he lis, could affec all of he variables in he ssem wihin he curren period. The shock o oupu affecs all variables immediael ecep mone, because mone lies above i on he lis. The variable a he boom of he lis, impor prices, is assumed o have no conemporaneous effec on an of he oher variables of he ssem. Alhough idenificaion b ordering is sill common, subsequen research has shown ha oher kinds of resricions can be used. For eample, in some macroeconomic models we can assume ha changes in a variable such as he mone suppl would have no long-run effec on anoher variable such as real oupu. In a simple ssem such as (5.10), his migh show up as he assumpion ha δ 0 + δ 1 = 0, for eample. Imposing his condiion would allow he seven srucural coefficiens of (5.10) o be idenified from he si β coefficiens of he VAR wihou using resricions on he covariances. 5.3 Inerpreing he Resuls of Idenified VARs When we can idenif he srucural shocks o each variable in a VAR, we can perform wo kinds of analsis o eplain how each shock affecs he dnamic pah of all of he variables of he ssem. Impulse-response funcions (IRFs) measure he dnamic marginal effecs of each shock on all of he variables over ime. Variance decomposiions eamine how imporan each of he shocks is as a componen of he overall (unpredicable) variance of each of he variables over ime. I is imporan o sress ha, unlike forecass and Granger causali ess, boh IRFs and variance decomposiions can onl be calculaed based on a se of idenifing assumpions and ha a differen se of idenificaion assumpions ma lead o differen conclusions. Suppose ha we have an n-variable VAR wih lags up o order p. If he variables of he ssem are 1, 2,, n, hen we can wrie he n equaions of he VAR as

21 82 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models n p i j i =β i 0 + β ijs s + v, i = 1, 2,..., n. j= 1 s= 1 (5.16) We assume ha we have a se of idenifing resricions on he model eiher an ordering of assumed conemporaneous causali or anoher se of assumpions so ha we can idenif he n orhogonal srucural shocks i ε from he n VAR error erms The impulse-response funcions are he n n se of dnamic marginal effecs of a oneime shock o variable j on iself or anoher variable i: i v. ε i + s j, s = 0, 1, 2,... (5.17) Noe ha here is in principle no limi on how far ino he fuure hese dnamic impulse responses can eend. If he VAR is sable, hen he IRFs should converge o zero as he ime from he shock s ges large one-ime shocks should no have permanen effecs. As noed above, non-convergen IRFs and unsable VARs are indicaions of non-saionari in he variables of he model, which ma be correced b differencing. IRFs are usuall presened graphicall wih he ime lag s running from zero up o some user-se limi S on he horizonal ais and he impac a he s-order lag on he verical. The can also be epressed in abular form if he numbers hemselves are imporan. One common forma for he enire collecion of IRFs corresponding o a VAR is as an n n mari of graphs, wih he impulse variable (he shock) on one dimension and he response variable on he oher. Each of he n 2 IRF graphs ells us how a shock o one variable affecs anoher (or he same) variable. There are wo common convenions for deermining he size of he shock o he impulse variable. One is o use a shock of magniude one. Since we can hink of he impulse shock as he ε in he denominaor of (5.17), seing he shock o one means ha he values repored are he dnamic marginal effecs as in (5.17). However, a shock of size one does no alwas make economic sense: Suppose ha he shock variable is banks raio of reserves o deposis, epressed as a fracion. An increase of one in his variable, sa from 0.10 o 1.10, would be implausible. To aid in inerpreaion, some sofware packages normalize he size of he shocks o be one sandard deviaion of he variable raher han one uni. Under his convenion, he values ploed are ε i + s j σ ˆ, s = 0, 1, 2,... j and are inerpreed as he change in each response variable resuling from a one-sandarddeviaion increase in he impulse variable. This makes he magniude of he changes in he response variables more realisic, bu does no allow he IRF values o be inerpreed direcl as dnamic marginal effecs.

22 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models 83 Because he VAR model is linear, he marginal effecs in (5.17) are consan, so which normalizaion o choose for he shocks one uni or one sandard deviaion is arbirar and should be done o faciliae inerpreaion. Saa uses he convenion of he one-uni impulse in is simple IRFs and one sandard deviaion in is orhogonalized IRFs. If he impulse variable is he same as he response variable, hen he IRF ells us how persisen shocks o ha variable end o be. B definiion, ε i i = 1, so he zero-order own impulse response is alwas one. If he VAR is sable, reflecing he saionari and ergodici of he underling variables, hen he own impulse responses deca o zero as he ime horizon increases: i s lim + = 0. s i ε If he impulse responses deca o zero onl slowl hen shocks o he variable end o change is value for man periods, whereas a shor impulse response paern indicaes ha shocks are more ransior. For cross-variable effecs, where he impulse and response variables are differen, general paerns of posiive or negaive responses are possible. Depending on he idenificaion assumpion (he ordering ), he zero-period response ma be zero or non-zero. B assumpion, shocks o variables near he boom of he ordering have no curren-period effec on variables higher in he order, so he zero-lag impulse response in such cases is eacl zero. 5.4 A VAR Eample: GDP Growh in US and Canada To illusrae he various applicaions of VAR analsis, we eamine he join behavior of US and Canadian real GDP growh using a quarerl sample from 1975q1 hrough 2011q4. Each of he series is an annual coninuousl-compounded growh rae. For eample, USGR = 400 ( lnusgdp lnusgdp 1 ), wih he 4 included o epress he growh rae as an annual raher han quarerl rae and he 100 o pu he rae in percenage erms Geing he specificaion righ As a preliminar check, we verif ha boh growh series are saionar. To be conservaive, we include four lagged differences o eliminae serial correlaion in he error erm of he Dicke-Fuller regression.. dfuller usgr, lags(4) Augmened Dicke-Fuller es for uni roo Number of obs = 143

23 84 Chaper 4: Vecor Auoregression and Vecor Error-Correcion Models Inerpolaed Dicke-Fuller Tes 1% Criical 5% Criical 10% Criical Saisic Value Value Value Z() MacKinnon approimae p-value for Z() = dfuller cgr, lags(4) Augmened Dicke-Fuller es for uni roo Number of obs = Inerpolaed Dicke-Fuller Tes 1% Criical 5% Criical 10% Criical Saisic Value Value Value Z() MacKinnon approimae p-value for Z() = In boh cases, we comforabl rejec he presence of a uni roo in he growh series because he es saisic is more negaive han he criical value, even a a 1% level of significance. Phillips-Perron ess lead o similar conclusions. Therefore, we conclude ha VAR analsis can be performed on he wo growh series wihou differencing. To assess he opimal lag lengh, we use he Saa varsoc command wih a maimum lag lengh of four:. varsoc usgr cgr, malag(4) Selecion-order crieria Sample: 1976q1-2011q4 Number of obs = lag LL LR df p FPE AIC HQIC SBIC * * * * * Endogenous: usgr cgr Eogenous: _cons Noe ha all of he regressions leading o he numbers in he able are run for a sample beginning in 1976q1, which is he earlies dae for which 4 lags are available, even hough he regressions wih fewer han 4 lags could use a longer sample. In his VAR, all of he crieria suppor a lag of lengh one, so ha is wha we choose.