MSc Time Series Econometrics

MSc Time Series Econometrics

MSc Time Series Econometrics Module 2 Lecture 1: VARs, introduction, motivation, estimation, preliminaries Tony Yates Spring 2014, Bristol Me New to academia. 20 years in Bank of England, directorate responsible for monetary policy. Various jobs on Inflation Report, Inflation Forecast, latterly as senior advisor, leading monetary strategy team Research interests: VARs, TVP-VARs, monetary policy design, learning, DSGE, heterogeneous agents. Also teaching MSc time series on VARs. Follow my outputs if you are interested My research homepage Blog longandvariable on macro and public policy Twitter feed @tonyyates

Topics we will cover Vector autoregressions: motivation Estimation, MLE, OLS, Bayesian using analytical and Gibbs Sampling MCMC methods Identification [short run restrictions, long run restrictions, sign restrictions, max share criteria] interpretation, use, contribution to macroeconomics Factor models in vector autoregressions TVP VAR estimation using kernels. If we have time, for fun: The Kalman Filter, Bootstrapping Course/learning strategy Rudimentary algebra of VARs, estimation, identification, etc. Some examples of code to i) give an insight into what might lie in store if you continue ii) can sometimes help to demystify the algebra. Applications: their contribution, impact. In the exam, you will be expected to understand and reproduce the algebra, and to cite and comment on

the applications. You wont be expected to write code. VARs useful sources Chris Sims, 'Macroeonomics and reality Lutz Kilian 'Structural Vector Autoregressions Fabio Canova: Methods for Applied Business Cycle research James Hamilton 'Time Series Analysis Helmut Luktepohl 'New introduction to multiple time series anal ysis' Useful sources, ctd Stock and Watson: implications of dynamic fa ctor models for VAR analysis Stock and Watson: 'Dynamic factor models' Matrix/linear algebra pre-requisites

Scalar, vector, matrix. Transpose Inverse (matrix equivalent of dividing). Diagonal matrix. Eigenvalues and eigenvectors. Powers of a matrix. Matrix series sums. Matrix equivalent of geometric scalar sums. Variance-covariance matrix. Cholesky factor of a variance-covariance matrix. Givens matrix. Some applications Christiano, Eichenbaum, Evans: Monetary policy shocks: what have we learne d and to what end? Christiano, Eichenbaum and Evans (2005): Nominal

rigidities and the dynamics effects of a monetary policy shock Mountford, Uhlig (2008): what are the effects of fiscal policy shocks? Gali (1999): Technology, employment and the business cycle. VAR motivation: Cowles Commission models Dominant paradigm was large scale macroeconometric models, in policy institutions especially Many estimated equations. Academic origins in foundational work to create national accounts; Keynesian formulation of macroeconomics; Haavelmos notion of probability model applied to this. Nice discussion in Sims Nobel acceptance lecture. Silly example of a CC model Sims: Equation for C excludes U, TU Equation for Y excludes C, U And so on....

C t c 0 c 1 Yt c 2 Yt 1 u Ct Yt y 0 y 1 N t y 2 N t 1 y 3 W t u Yt W t w 0 w 1 U t w 2 TU t u Wt U t u 0 u 1 Yt u 2 Yt 1 u Ut TU t tu 0 tu 1 Yt tu 2 U t u TUt Those us look exogenous and are meant to be, but are they really primitive shocks? Both Sims and Lucas critiques would suggest not Lucas: equation for C sounds like common sense, but is it the C that reflects the solution to a consumers problem in a general equilibrium model? Maybe, or maybe not Critiques of Cowles Commission approach Lucas (1976): Laws of motion have to come from solving problems of agents in the model

If not, correlations will change if policy changes Sims (1981): Incredible identification restrictions Response to Sims and Lucas critiques No incredible restrictions. Everything left in. Reduced form shocks span the structural shocks. Structural shocks and their effects sought through identification, reference to classes of Lucas-Critique proof models Modest policy interventions [Sims and Zha]. Cowles Commission variables set out as a VAR model Yt Ct A 011 A 012 . . . A 016

Yt A 021 . . . . . . . . . . . . . . . .

. TU t . . . . . . Nt A 061 . . . . A 066 Wt Ut

Potentially, everything is a function of everything else lagged Yt 1 A 1 Yt 2 . . . Z t Simultaneity encoded in the reduced form errors. To be disentangled into structural shocks through identification. VAR topics and contributions to macro (1) Estimating parameters in a DSGE model: Rotemburg and Woodford (1998) Christiano, Eichenbaum and Evans (2005) Identify monetary policy shock Choose parameters of the model so that

impulse response to this shock in the DSGE model as close as possible to corresponding IRF in the VAR. VAR topics and contributions to macro (2) Evaluating the RBC claim that technology shocks cause business cycles Gali (1999) Identified technology shocks as the only thing that could change N/Y in long run Showed that these caused hours work to fall, not rise Inconsistent with RBC model (make hay while the sun shines) VAR topics and contributions to macro (3) Cogley and Sargent (2005) VAR with time-varying parameters Multivariate counterpart to persistence, predictability Bayesian estimation By how much did inflation predictability change in

the post-war period? If it changed a lot, what does that suggest about its causes? Misc technical preliminaries to help you read the papers The lag polynomial operator L y t 1 y t 1 2 y t 2 . . . p y t p 0 L 0 y t 1 1 L 1 y t 1 . . . p 1 L p 1 y t 1 Ly t 1 Ly t y t 1 , L 1 y t y t 1 Lag / lead operator denoted by positive, negative powers of L Misc technical preliminaries... Cholesky decomposition 11

21 31 . . 22 . 32

33 a 0 0 . b 0 a 0 0 a . . . b 0 0 b . . . c 0 0 c Sigma is a v-cov matrix,

with elements symmetric about the diagonal; Cholesky factor on the RHS a . . PP . . c 0 b . 0 0 c 1 0 0 PP I 0 1 0 0 0 1 a, b, c 0 Here we decompose further using an orthonormal

matrix P Diagonals of vcov matrix are positive beause these correspond to variances Misc technical preliminaries Givens matrix is an example of an orthonormal matrix 1 0 0 0 P 0 c s 0 0 s c 0 , c cos , s sin 0 0 0 1 Also known as a Givens rotation Useful theorem: any orthonormal matrix can be shown to be a product of Givens matrices with different thetas, the number depending on the dimension of the orthonormal matrix concerned.

Products of orthonormal matrices P a P a I, P b P b I P a P b P a P b I If two matrices are orthonormal, then so is the product of those matrices. The VAR impulse response function y t y t 1 e t y irf y1 e y2 e y3

2e ... ... yn n 1 e Impulse response function in a univariate time series model Take some unit value for e1, then substitute into eq for y repeatedly AR(1) impulse response function 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2

0.1 0 0 10 20 30 40 50 60 70 80 90

100 Y_t=0.85*y_t-1+e_t; e(1)=1 IRF shrinks back to zero as we multiply 1 by successively higher powers of 0.85 Matlab code to plot IRF in an AR(1) Matlab code to plot an AR(1) impulse response

%compute impulse response in an AR(1). %illustration for MSc Time Series Econometrics module. %y_t=rho*y_t-1+e_t. %calibrate parameters rho=0.85; %persistence parameter in the ar(1) samp=100; %length of time we will compute y for. y=zeros(samp,1); %create a vector to store our ys. %semi colons suppress output of every command %to screen. e=1; %value of the shock in period 1.

y(1)=e; %first period, y=shock. for t=2:samp y(t)=rho*y(t-1); end %loop to project forwards effect of the unit shock. %now plot the impulse response time=[1:samp]; %create a vector of numbers to record the progression of time plot(time,y) VAR impulse response Yt Yirf,1 y1 y2 AYt 1 e t t

a 11 a 12 y1 a 21 a 22 y2 e, Ae, A 2 e, . . . , e t 1 e1 e2 e1 e2 A multivariate example. For IRF to e1, choose e=[1,0] for first period, then project forwards....

Matlab code to plot VAR(1) impulse response function

%compute impulse response in an VAR(1). %illustration for MSc Time Series Econometrics module. %y_t=A*y_t-1+e_t. clear all; %bit of housekeeping to clear all variables so each time you run program as you are debugging %you know you are not adding onto previous %values %calibrate parameters A=[0.6 0.2; 0.2 0.6]; samp=100; y=zeros(samp,2);

e=[1;0]; y(1,:)=e; %length of time we will compute y for. %create a matrix this time to store our 2 by samp bivariate time series y={y1,y2}. %we will simulate a shock to the first equation. Note shock in first period is now a 2 by 1 vector. %first period, y=shock. the colon ':' means 'corresponding values in this dimension' for t=2:samp y(t,:)=A*y(t-1,:)'; %loop to project forwards effect of the unit shock. end %' is transpose %now plot the impulse response time=[1:samp]; %create a vector of numbers to record the progression of time subplot(2,1,1) plot(time,y(:,1)) subplot(2,1,2) plot(time,y(:,2)) VAR(1) impulse response 1

0.8 0.6 0.4 0.2 0 0 10 20 30 40 50 60 70 80

90 100 0 10 20 30 40 50 60 70 80

90 100 0.25 0.2 0.15 0.1 0.05 0 Y_t=A*Y_t-1+e_t, e_t=[1 0], A=[0.6 0.2;0.2 0.6] Note that the eigenvalues of A are 0.4 and 0.8 Using a VAR to forecast f Yt h | e s 0, s t f Yt h h e tA

Forecast at future horizon h, conditional on starting from steady state= IRF to the latest estimated shock. h h 1 h 2 e t A e t 1 A e t 2 A . . . e t n A h n Forecast conditional on all the shocks estimated to have occurred: Sum of IRF to that shock at increasing horizons. Terms further to the right get smaller as higher powers of h [=smaller for stable VAR] Reflects response to shocks that hit further and further back in time. Forecasts further and further out shrink back to steady state for the same reason. Higher powers of h , A has eigenvalues <1 in absolute value. VAR(1) representation of a VAR(p) Yt A 1 Yt 1 A 2 Yt 2 . . . A p Yt p e t Yt

Yt Yt 1 AYt 1 e t ... Yt p 1 A1 A2 . . . Ap A Ik 0 ... 0 0 Ik 0 ...

0 0 Ik 0 et ,et 0 ... 0 Moving average representation of a VAR(p) Yt

Ai e t i i 0 We have used the VAR(1) form of the VAR(p). In words, it means Y is the sum of shocks, where each shock taken to higher and higher powers as we go back in time. Notice how this is related to the formula for the VAR impulse response we computed before. Persistence, memory, predictability, stability When a shock hits, how long does it takes for its effects to die out? Applications: Business cycle theory concerned with mechanisms for propagation. Consumption and output not white noise: why? Bad monetary and fiscal policy could be part of the story about why shocks take time to die out Time series notions of persistence etc are one way to characterise propagation and bad policy. Why bad, because more persistence means larger variance, and larger variance for most utility functions is bad

Persistence and variance y t y t 1 e t vary t 2 vary t 1 vare t 2 covy t 1 , e t 2 vary t 1 vare t NB : vary t vary t 1 vare t vary t 1 2 Persistence, higher rho, means lower denominator, means higher unconditional variance of y Most economic models asssume agents dont like variance So persistence is interesting economically, since it usually indicates something bad is happening Persistence and predictability j Pt 1 ei e i

j 1 A ht h 0 A ht h 0 t A ht t A ht ei ei Multivariate predictability: if set horizon=2, dimension of

Y_t=1, then this formula delivers rho^2, ie persistence squared. AR stability y t y t 1 e t , | | 1 Univariate model. e, e, 2 e, . . . n e t limt e 0 Effects of shock eventually die out. So series converges to something. Stability=stationarity=series have convergent sums=first and second moments independent of t, and computable y t e t e t 1 2 e t 2 . . . n e t n

s e t s s 0 lims s e t s 0 The contribution of a shock very far back goes to zero Here we take the perspective that todays data is the sum of the effects of shocks going back into the infinite past. Since todays data is finite and well-defined, then it must be that shocks infinitely far back have no effect. Otherwise todays data would be infinitely large. VAR(1) stability Yt AYt 1 e t x eigA;|x | 1, x detI K Ax 0 |x | 1

Stability condition echoes the AR case, but where does the dependence on the eigenvalues come from? Yt A 0 e t A 1 e t 1 A 2 e t 2 . . . A n e t n A 0 L 0 e A Le t A 2 L 2 e t . . . L n A n e t Here we write out a vector Y as the sum of contributions from shocks going back further and further into time. Explaining VAR(1) eigenvalue stability condition A n PDn P 1 n1 Dn 0 0 0 n2

0 0 P 0 nd v1 v2 vd The crucial thing is to make this A^n go to zero as n goes to infinity. Importance of eigenvalues in this happening comes from the fact that we can write a square matrix using the eigenvalueeigenvector decomposition. And then compute the power of A using the powers of the eigenvales of A. So to make A^n go to zero, we have to make all the diagonal elements of D go to zero, and by analogy with the AR(1) case, this means the eigenvalues have to be < 1 in absolute value. VAR(p) stability Yt

Yt Yt 1 AYt 1 e t ... Yt p 1 A A1 A2 . . . Ap et Ik 0 ... 0 0

Ik 0 ... 0 0 Ik 0 0 x eigA; |x | 0, x ,et ... 0 VAR estimation

Linear, multivariate model Suggests estimation by... OLS! Or MLE, which in these circumstances [linear; Gaussian errors] is equivalent. MLE cumbersome because you may have many parameters over which to optimise Why is this cumbersome? Well, you tell me. OLS estimation of VAR parameters y t y t 1 e t y y 2 , . . . y T , x x x 1 x y A XX

1 X Y y 2 , . . . y T 1 Univariate case Multivariate case for VAR(1) representation of VAR(p) OLS estimate of reduced form residual variance covariance matrix y t y t 1 e t 1/T e e , e y x y y 2 . . . y T , x y 1 . . . y T 1 1/T e e , e Y AX

Univariate case. Take data, subtract prediction, multiply residual vector by transpose.... Analogously for the multivariate case The likelihood function for a VAR Why bother if we can use OLS? Given the drawbacks of MLE? Log posterior is sum of log likelihood and log prior: so we need it for Bayesian estimation Key to understanding many concepts: Origin and derivation of standard errors from slope of LF Estimation when we cant evaluate the LF but have to approximate it by simulation Pseudo-ML when the data are non-Gaussian Likelihood fn for an AR(1) y t y t 1 e t , e t N0, 2e Ey 1 0

2e vary 1 Ey 1 0 1 2 y 1 , y 01 f y 1 y 01 ; , 2e y 02 1 1 exp 2 2 e /1 2 2 2e 1 2 y2 y1 e2 y 2 y 1 y 01 N y 01 , 2e f y 2 y 1 y 02

y 01 , , 2e y 02 y 01 2 1 exp 2 2 2 2e /1 2 2 e 1 LF for an AR(1)/ctd... f y y 1 ,y 2 ... f y 1 f y 2 y 1 f y 3 y 2 . . . f y T y T 1 logf y y 1 ,y 2 ... y 02 1 0. 5 log2 0. 5 log 1 2 2 /1 2 2

2 T 1/2 log2 T 1/2 log 2 T y 0t y 0t 1 2 2 2 t 2 Logging turns complicated product into long sum We can maximise this and ignore constants. Likelihood for a VAR(p) Yt Yt 1 , Yt 2 , . . . Yt p 1 NYt A 1 Yt 1 A 2 Yt 2 . . . A p Yt p , Yt Yt 1 , Yt 2 , . . . Yt p A A 1 , A 2 . . . . A p Yt Yt 1 , Yt 2 , . . . Yt p 1 NAYt , f Y t Y t 1 ,Y t 2 ,...Y t p 1 Y0t , Y0t 1 , Y0t 2 . . . . ; A, 2 n/2 |

1 0.5 | exp 0. 5Y0t AY0t 1 Y0t AY0t T logf Y Y 1 ,Y 2 ...Y T logf Y 1 ... logf Y 2 ... . . . logf Y t Y t 1 ,... t 1 Tn/2 log2 T/2 log|

1 | T 0. 5 Y0t AY0t t 1 1 Y0t AY0t Recap Remember likelihood assumes Gaussian errors In some circumstances you can get consistent estimates of parameters (but not standard errors) even if this is violated. Distributions for a VARs impulse response functions y t y t 1 e t e, e, 2 e, . . . n e

irf Yt h h IRF is easy in an AR1. It involves powers of only 1 coefficient. So distributions of rho can be used to compute distributions of the elements of the IRFs vector. Work it out. A e, h 0, 1, 2. . . Things harder with a VAR as involves many, jointly distributed coefficients. Bootstrapping algorithm for VAR IRFs

Yt AYt 1 e t Suppose have estimated a VAR(p) 1. Draw, with replacement, a time series of shocks, e i e i1 , e i2 , , , 2. Create a new time series of observeables using Yt AYt 1 e it 3. Re-estimate the VAR to produce A i 4. Compute IRFs using a unit shock e 0 1, 0, 0. . . and powers of A i 5. set i i 1, return to step 1. if i iter Set iter=200 or so. Algorithm will generate iter vectors h long, h=max chosen horizon of the IRF. Q: how to do step 1 with computer random number generator? Why estimate VARs Estimate RBC/DSGE model by choosing the parameters to match the VARs IRF Estimate using indirect inference. Test implications of model by identifying a

shock. Accounting for the business cycle by identifying a shock. Forecasting. Indirect inference with VARs 1. Estimate a VAR on the data 2. Generate data from DSGE model for candidate parameter values i 3. Estimate a VAR on the generated data 4. Compute score S i distance of 1 from 2 5. Let i i 1;Go back to 2. i iterm 6. i such that S i minS Key reference is Gourieroux et al Measure of distance, eg, euclidian norm, perhaps weighted in some way. VAR impulse response matching [Rotemburg and Woodford] is akin to indirect inference. In some cases, DSGE models dont have VAR representations, though they have VAR approximations When they do, more properly called a partial information method, rather

than indirect inference. IRFs one of the infinite moments summarised by the likelihood Next topic, in fact most of rest of course, will be on identification in VARs Apologies we are going to do some economics. These are going to be credible identification restrictions, contrast with Cowles Commission. But still contestable. They will be based on classes of business cycle models. Enables a test of a theory without making too many auxiliary assumptions that could fail. From VARs to SVARs Short-run, zero restrictions [Sims, CEE] Long run restrictions. [Blanchard-Quah, Gali] Sign restrictions. [Faust, Uhlig, Mountford and Uhlig, Canova and de Nicolo] Max share restrictions and news shocks. [Francis et al, Barsky-Sims, Pinter, Theodoridis and Yates] Heteroskedasticity-based identification [Rigobon]

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