1. Generalized method of moments estimation FINA790C HKUST Spring 2006

2. Outline Basic motivation and examples First case: linear model and instrumental variables –Estimation –Asymptotic distribution –Test of model specification General case of GMM –Estimation –Testing

3. Basic motivation Method of moments: suppose we want to estimate the population mean  and population variance  2 of a random variable v t These two parameters satisfy the population moment conditions E[v t ] -  = 0 E[v t 2 ] – (  2 +  2 ) = 0

4. Method of moments So let’s think of the analogous sample moment conditions: T -1  v t -  = 0 T -1  v t 2 – (  2 +  2 ) = 0 This implies  = T -1  v t  2 = T -1  (v t -  )2 Basic idea: population moment conditions provide information which can be used for estimation of parameters

5. Example Simple supply and demand system: q t D =  p t + u t D q t S =  1 n t +  2 p t + u t S q t D = q t S = q t q t D, q t S are quantity demanded and supplied; p t is price and q t equals quantity produced Problem: how to estimate , given q t and p t OLS estimator runs into problems

6. Example One solution: find z t D such that cov(z t D, u t D ) = 0 Then cov(z t D,q t ) –  cov(z t D,p t ) = 0 So if E[u t D ] = 0 then E[z t D q t ] –  E[z t D p t ] = 0 Method of moments leads to  * = (T -1  z t D q t )/(T -1  z t D p t ) which is the instrumental variables estimator of  with instrument z t D

7. Method of moments Population moment condition: vector of observed variables, v t, and vector of p parameters θ, satisfy a px1 element vector of conditions E[f(v t,θ)] = 0 for all t The method of moments estimator θ* T solves the analogous sample moment conditions g T (θ*) = T -1  f(v t,θ* T ) = 0 (1) where T is the sample size Intuitively θ* T → in probability to the solution θ 0 of (1)

8. Generalized method of moments Now suppose f is a qx1 vector and q>p The GMM estimator chooses the value of θ which comes closest to satisfying (1) as the estimator of θ 0, where “closeness” is measured by Q T (θ) = T -1  f(v t,θ)W T T -1  f(v t,θ) = g T (θ)W T g T (θ) and W T is psd and plim(W T ) = W pd

9. GMM example 1 Power utility based asset pricing model E t [  (C t+1 /C t ) -  R it+1 – 1] = 0 with unknown parameters  The population unconditional moment conditions are E[(  (C t+1 /C t ) -  R it+1 – 1)z jt ] = 0 for j=1,…,q where z jt is in information set

10. GMM example 2 CAPM says E[ R it+1 - 0 (1-  i ) –  i R mt+1 ] =0 Market efficiency E[( R it+1 - 0 (1-  i ) –  i R mt+1 )z jt ] =0

11. GMM example 3 Suppose the conditional probability density function of the continuous stationary random vector v t, given V t-1 ={v t-1,v t-2,…} is p(v t ;θ 0,V t-1 ) The MLE of θ 0 based on the conditional log likelihood function is the value of which maximizes L T (θ) =  ln{p(v t ;θ,V t-1 )}, i.e. which solves ∂L T (θ)/∂θ = 0 This implies that the MLE is just the GMM estimator based on the population moment condition E[ ∂ln{p(v t ;θ,V t-1 )}/∂θ} ] =0

12. GMM estimation The GMM estimator θ* T = argmin θ ε  Q T (θ) generates the first order conditions {T -1  ∂f(v t,θ* T )/∂θ´}´W T {T -1  f(v t,θ* T )} = 0 (2) where ∂f(v t,θ* T )/∂θ´ is the q x p matrix with i,j element ∂f i (v t,θ* T )/∂θ j ´ There is typically no closed form solution for θ* T so it must be obtained through numerical optimization methods

13. Example: Instrumental variable estimation of linear model Suppose we have y t = x t ´θ 0 + u t, t=1,…,T Where x t is a (px1) vector of observed explanatory variables, y t is an observed scalar, u t is an unobserved error term with E[u t ] = 0 Let z t be a qx1 vector of instruments such that E[z t ´u t ]=0 (contemporaneously uncorrelated) Problem is to estimate θ 0

14. IV estimation The GMM estimator θ* T = argmin θ ε  Q T (θ) where Q T (θ) = {T -1 u(θ)’Z}W T {T -1 Z’u(θ)} The FOCs are (T -1 X’Z)W T (T -1 Z’y) = (T -1 X’Z)W T (T -1 Z’X)θ* T When # of instruments q = # of parameters p (“just-identified”) and (T -1 Z’X) is nonsingular then θ* T = (T -1 Z’X) -1 (T -1 Z’y) independently of the weighting matrix W T

15. IV estimation When # instruments q > # parameters p (“over-identified”) then θ* T = {(T -1 X’Z)W T (T -1 Z’X)} -1 (T -1 X’Z)W T (T -1 Z’y)

16. Identifying and overidentifying restrictions Go back to the first-order conditions (T -1 X’Z)W T (T -1 Z’u(θ* T )) = 0 These imply that GMM = method of moments based on population moment conditions E[x t z t ’]WE[z t u t (θ 0 )] = 0 When q = p GMM = method of moments based on E[z t u t (θ 0 )] = 0 When q > p GMM sets p linear combinations of E[z t u t (θ 0 )] equal to 0

17. Identifying and over-identifying restrictions: details From (2), GMM is method of moments estimator based on population moments {E[∂f(v t,θ 0 )/∂θ’]}’W{E[f(v t,θ 0 )]} = 0(3) Let F(θ 0 )=W ½ E[∂f(v t,θ 0 )/∂θ´] and rank(F(θ 0 ))=p. (The rank condition is necessary for identification of θ 0 ). Rewrite (3) as F(θ 0 )’W ½ E[f(v t,θ 0 )] = 0 or F(θ 0 )[F(θ 0 )’F(θ 0 )] -1 F(θ 0 )’W ½ E[f(v t,θ 0 )]=0(4) This says that the least squares projection of W ½ E[f(v t,θ 0 )] on to the column space of F(θ 0 ) is zero. In other words the GMM estimator is based on rank{F(θ 0 )[F(θ 0 )’F(θ 0 )] -1 F(θ 0 )’} = p restrictions on the (transformed) population moment condition W ½ E[f(v t,θ 0 )]. These are the identifying restrictions; GMM chooses θ* T to satisfy them.

18. Identifying and over-identifying restrictions: details The restrictions that are left over are {I q - F(θ 0 )[F(θ 0 )’F(θ 0 )] -1 F(θ 0 )’}W ½ E[f(v t,θ 0 )]=0 This says that the projection of W ½ E[f(v t,θ 0 )] on to the orthogonal complement of F(θ 0 ) is zero, generating q-p restrictions on the transformed population moment condition. These over-identifying restrictions are ignored by the GMM estimator, so they need not be satisfied in the sample From (2), W T ½ T -1  f(v t,θ* T )= {I q - F T (θ* T )[F T (θ* T )’F T (θ* T )] -1 F T (θ* T )’}W T ½ T -1 f(v t,θ* T ) where F T (θ)= W T ½ T -1  ∂f(v t,θ)/ ∂θ´. Therefore Q T (θ* T ) is like a sum of squared residuals, and can be interpreted as a measure of how far the sample is from satisfying the over-identifying restrictions.

19. Asymptotic properties of GMM instrumental variables estimator It can be shown that: θ* T is consistent for θ 0 (θ* T,i – θ 0,i )/√V* T,ii /T ~ N(0,1) asymptotically where –V* T = (X’ZW T Z’X) -1 X’ZW T S* T W T Z’X (X’ZW T Z’X) -1 –S* T = lim T→∞ Var[T -½ Z’u]

20. Covariance matrix estimation Assuming u t is serially uncorrelated Var[T -½ Z’u] = E{T -½  z t u t }{T -½  z t u t } = T -1  E[u t 2 z t z t ’} Therefore, S* T = T -1  u t (θ* T ) 2 z t z t ’

21. Two step estimator Notice that the asymptotic variance depends on the weighting matrix W T The optimal choice is W T = S* T -1 to give V* T = (X’Z S* T -1 Z’X) -1 But we need θ* T to construct S* T This suggests a two-step (iterative) procedure –(a) estimate with sub-optimal W T, get θ* T (1),get S* T (1) –(b) estimate with W T = S* T (1) -1 –(c) iterated GMM: from (b) get θ* T (2), get S* T (2), set W T = S* T (2) -1, repeat

22. GMM and instrumental variables If u t is homoskedastic: var[u]=  2 I T then S* T =  * 2 Z t Z t ’ where Z t is a (Txq) matrix with t-th row = z t ’ and  * 2 is a consistent estimate of  2 Choosing this as the weighting matrix W T = S* T -1 gives θ* T = {X’Z(Z’Z) -1 Z’X)} -1 X’Z(Z’Z) -1 Z’y = {X^’X^} -1 X^’y where X^=Z(Z’Z) -1 Z’X is the predicted value of X from a regression of X on Z. This is two-stage least squares (2SLS): first regress X on Z, then regress y on the fitted value of X from the first stage.

23. Model specification test Identifying restrictions are satisfied in the sample regardless of whether the model is correct Over-identifying restrictions not imposed in the sample This gives rise to the overidentifying restrictions test J T = TQ T (θ* T ) = T -½ u(θ* T )’Z S* T -1 T -½ Z’u(θ* T ) Under H 0 : E[z t u t (θ 0 )] = 0, J T asymptotically follows a  2 (q-p) distribution

24. (From last time) In this case the MRS is m t,t+j =  j { C t+j /C t } -  Substituting in above gives: E t [ln(R it,t+j )] = -jln  +  E t  c t+j - ½  2 var t [  lnc t+j ] - ½var t [lnR it,t+j ] +  cov t [  lnc t+j,lnR it,t+j ]

25. Example: power utility + lognormal distributions First order condition from investor maximizing power utility with log-normal distribution gives: ln(R it+1 ) =  i +  lnC t+1 + u it+1 the error term u it+1 could be correlated with  lnC t+1 so we can’t use OLS However E t [u it+1 ] = 0 means u it+1 is uncorrelated with any z t known at time t

26. Instrumental variables regressions for returns and consumption growth Return Stage 1 Stage 2J T test r Δlnc γ  r Δlnc CP 0.297 0.102 -0.953 -0.118 0.221 0.091 (0.00) (0.15) (0.57) (0.11) (0.00) (0.10) Stock 0.110 0.102 -0.235 -0.008 0.105 0.097 (0.11) (0.15) (1.65) (0.06) (0.06) (0.08) Annual US data 1889-1994 on growth in log real consumption of nondurables & services, log real return on S&P500, real return on 6- month commercial paper. Instruments are 2 lags each of: real commercial paper rate, real consumption growth rate and log of dividend-price ratio

27. GMM estimation – general case Go back to GMM estimation but let f be a vector of continuous nonlinear functions of the data and unknown parameters In our case, we have N assets and the moment condition is that E[(m(x t,  0 )R it -1)z jt-1 ]=0 using instruments z jt-1 for each asset i=1,…,N and each instrument j=1,…,q Collect these as f(v t,  )= z t-1 ’  u t (X t,  ) where z t is a 1xq vector of instruments and u t is a Nx1 vector. f is a column vector with qN elements – it contains the cross-product of each instrument with each element of u. The population moment condition is E[f(v t,  0 )] = 0

28. GMM estimation – general case As before, let g T (θ) = T -1  f(v t,θ). The GMM estimator is θ* T = argmin θ ε  Q T (θ) The FOCs are G T (  )’W T g T (  ) = 0 where G T (  ) is a matrix of partial derivatives with i, j element δg Ti /δ  j

29. GMM asymptotics It can be shown that: θ* T is consistent for θ 0 asyvar(θ* T,ii ) = MSM’ where –M = (G 0 ’WG 0 ) -1 G 0 ’W –G 0 = E[∂f(v t,θ 0 )/∂θ’] –S = lim T→∞ Var[T -½ g T (θ 0 )]

30. Covariance matrix estimation for GMM In practice V* T = M* T S* T M* T ’ is a consistent estimator of V, where M* T = (G T (θ* T )’W T G T (θ* T )) -1 G T (θ* T )’W T S* T is a consistent estimator of S Estimator of S depends on time series properties of f(v t,  0 ). In general it is S = Γ 0 +  ( Γ i + Γ i ’) where Γ i = E{f t -E(f t )}{f t-i -E(f t-i )}’=E[f t f t-I ’] is the i-th autocovariance matrix of f t = f(v t,  0 ).

31. GMM asymptotics So (θ* T,i – θ 0,i )/√V* T,ii /T ~ N(0,1) asymptotically As before, we can choose W T = S* T -1 and – V* T is then (G T (θ* T )’S T -1 G T (θ* T )) -1 –a test of the model’s over-identifying restrictions is given by J T = TQ T (θ* T ) which is asymptotically  2 (qN-p )

32. Covariance matrix estimation We can consistently estimate S with S* T = Γ* 0 (θ* T ) +  ( Γ* i (θ* T ) + Γ* i (θ* T )’) where Γ* i (θ* T ) = T -1  f t (v t,θ* T )f t-i (v t-i,θ* T )’ If theory implies that the autocovariances of f(v t,θ 0 ) = 0 for some lag j then we can exclude these from S* T –e.g. u t are serially uncorrelated implies S* T = T -1  { u t (v t,θ* T )u t (v t,θ* T )’  (z t ’z t ) }

33. GMM adjustments Iterated GMM is recommended in small samples More powerful tests by subtracting sample means of f t (v t,θ* T ) in calculating Γ* i (θ* T ) Asymptotic standard errors may be understated in small samples: multiply asymptotic variances by “degrees of freedom adjustment” T/(T-p) or (N+q)T/{(N+q)T – k} where k = p+((Nq) 2 +Nq)/2