Bayesian Inference for Random Coefficient Dynamic Panel Data Models

Transcription

1 Bayesian Inference for Random Coefficient Dynamic Panel Data Models By Peng Zhang and Dylan Small* 1 Department of Statistics, The Wharton School, University of Pennsylvania Abstract We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit s process to be correlated with the unit-specific coefficients. We impose a stationarity assumption for each unit s process by assuming that the unit-specific autoregressive coefficient is drawn from a logitnormal distribution. Our method is shown to have favorable properties compared to the mean group estimator in a Monte Carlo study. We apply our approach to analyze a labor demand model for Spanish firms. JEL classification: C11;C3 Keywords: DYNAMIC PANEL DATA; BAYESIAN INFERENCE; GIBBS SAMPLING; METROPO- LIS ALGORITHM. 1 * Corresponding author. Tel: ; Fax: addresses: dsmall@wharton.upenn.edu(d.small), pzhang@wharton.upenn.edu(p.zhang).

2 1 Introduction Dynamic panel data models with autoregressive coefficients are widely used in the analysis of economic data (Arellano and Honoré, 001). Traditionally, the econometrics literature has focused on models that allow intercepts to vary across units but that assume the same autoregressive coefficients for all units. However, in many settings, it is more realistic to allow the autoregressive coefficients to vary across units, allowing, for example, for good-specific speeds of reversion to purchasing power parity (Imbs, Mumtaz, Ravn and Rey, 005), individual-specific speeds of adjustment to income shocks (Hu and Ng, 004), and country-specific dynamics in savings behavior (Haque, Pesaran and Sharma, 000). For dynamic panel data models with heterogeneous autoregressive coefficients, Pesaran and Smith (1995) showed that not accounting for the heterogeneity produces inconsistent estimates of the mean autoregressive coefficient, even for a large N, large T panel. To address this problem, Pesaran and Smith (1995) proposed the use of the mean group estimator which averages the coefficients estimated in separate regressions for each unit (or each group of units). Hsiao, Pesaran and Tahmiscioglu (1999) showed that the mean group estimator is a consistent and asymptotically normal estimate of the average coefficient as long as N, T and N/T 0. However, the mean group estimator is not consistent for the large N, small T setting that has been the traditional focus of panel data analysis. As an alternative to the mean group estimator, Hsiao, Pesaran and Tahmiscioglu (hereafter HPT) proposed a hierarchical Bayesian approach to estimate the mean autoregressive coefficient for random effect AR(1) models. HPT showed that their Bayesian estimator is asymptotically equivalent to the mean group estimator for the large N, large T setting and showed that the Bayesian estimator has better sampling properties than the mean group estimator for small and moderate T settings in a Monte Carlo study. However, there are some limitations to the hierarchical Bayesian approach proposed by HPT. First, the model assumes that the initial values y i0 of the dependent variable are fixed and uncorrelated with the unit-specific coefficients. This means that the unit-specific coefficients do not affect the unit s process at time 0 but affect the unit s process at time 1 and later. Such a model is not realistic when the decision about when to start sampling the panel is arbitrary; if the process has been going on for some time, there is no particular reason to believe that y i0 should be viewed differently than y it (Hsiao, 003). A second limitation of HPT s Bayesian approach is that although the model they consider assumes that each unit s process is stationary, i.e., the AR(1) coefficient for each unit is assumed to have abso-

3 lute value less than 1, the distribution of the autoregressive coefficients is assumed to be normal and is not constrained to have absolute value less than 1 in order to facilitate Gibbs sampling. In this paper, we build on HPT s hierarchical Bayes approach for the random coefficient AR(1) model, improving two features of it by allowing the initial values y i0 to be correlated with the unitspecific coefficients and imposing stationarity on the unit-specific AR(1) coefficients. We consider two assumptions about the initial value y i0 for unit i: (1) the unit s process has been going on for a long time before time 0 and the initial value is generated from a stationary process and () the unit s process started from a finite time before the 0th period. To impose stationarity on the AR(1) coefficients γ i, we assume that the γ i are generated from a distribution whose support is ( 1, 1), in particular the logitnormal distribution. To sample from the posterior distribution of our model, we use a Metropolis algorithm. We conduct a Monte Carlo study to examine the frequentist properties of our Bayesian approach. The results show that our approach provides good estimates even when T is small. Besides its good frequentist properties for estimating mean coefficients and variance components, our Bayesian approach has the attractive feature that inferences on the unit-specific coefficients can easily be made. We illustrate our approach by applying it to a model for labor demand for Spanish firms. Our paper is organized as follows. Section describes our formulation of the random coefficient dynamic panel data model. Section 3 describes our prior distribution for our model parameters and our Markov chain Monte Carlo approach for drawing from the posterior distribution. Section 4 provides a Monte Carlo study of our approach s frequentist properties. Section 5 illustrates our approach by applying it to a labor demand model for Spanish firms. Section 6 provides conclusions. Formulation of the model To focus on main issues, we first consider a model without covariates. We extend our approach to a model with covariates at the end of Section 3. The dynamic panel data model we consider is: y i = γ i y i, 1 + α i + u i (1) where γ i < 1, i = 1,,..., N, y i = (y i1, y i,..., y it ) is a T 1 vector of observations for the dependent variable and y i, 1 = (y i0, y i1,..., y i,t 1 ). The unit-specific coefficients γ i and α i are time invariant but vary over the cross section. The u i = (u i1, u i,..., u it ) are assumed to be iid 3

4 disturbances with a N(0, σ ui) distribution. The model (1) can be equivalently written in state space form (Hsiao, 003) as: where w it is a hidden state and η i = w it = γ i w i,t 1 + u it () y it = w it + η i (3) α i 1 γ i is the long-run mean for unit i. In a random coefficient model, both the AR coefficient γ i and the long-run mean η i are random. Parameters of interest include mean coefficients µ γ = E(γ i ), µ η = E(η i ) as well as the corresponding variance components σ γ = V ar(γ i ), σ η = V ar(η i ) and σ γη = cov(γ i, η i ). Because we assume that each unit s process is stationary ( γ i < 1), we want the distribution for γ i to have support on ( 1, 1). We assume γ i is drawn from a logitnormal distribution scaled to have support ( exp(γ on (-1,1), i.e., γ i = i ) 1+exp(γ ), i ).5 where γi is assumed to have a normal distribution. The logitnormal is a flexible family of distributions for a random variable constrained to an interval. Frederic and Lad (003) provide a review of the logitnormal distribution s properties. We assume that θ i = (γ i, η i) has a bivariate normal distribution with mean θ = (µ γ, µ η ) and covariance matrix (with variances σ γ and σ η and correlation ρ). Additionally, we assume that each unit s coefficients are independent of other units random coefficients, i.e., Cov(θ i, θ j ) = 0 if i j. We consider two scenarios for how the initial value y i0 is generated: Case 1: Each unit s process starts from the infinite past and y i0 is generated from the stationary distribution, i.e., a normal distribution conditional on (α i, γ i ) with mean σu. 1 γi α i 1 γ i and variance Case : The initial value is generated from the finite past. We consider the following flexible formulation: y i0 = η i (a + bγ i ) + v i0, (4) where v i0 N(0, σv). Note that the process starting at time 0, i.e., y i0 = α i + v i0, corresponds to a=1,b=-1 and the process starting in the infinite past, i.e. E(y i0 α i, γ i ) = α i /(1 γ i ), corresponds to a=1,b=0 (Note 4

5 that case does not contain case 1 because when y i0 is generated from the infinite past, case does not use the information that var(y i0 ) = σu/(1 γi )). The formulation in case also allows for different nonstationary models with varied values of a and b. 3 Bayesian Approach In this section, we develop a hierarchical Bayesian approach for estimating cases 1 and of the models above. For case 1, we need to put a prior distribution on the parameters θ,, and σu. We choose the normal-inverse Wishart distribution as the prior distribution for θ and with parameters (µ 0, Λ 0 /κ 0 ; ν 0, Λ 0 ): IW ν0 (Λ 1 0 ) (5) θ N(µ 0, /κ 0 ) (6) where IW represents the Inverse-Wishart distribution; ν 0 and Λ 0 are the degrees of freedom and the scale matrix for the inverse-wishart distribution; µ 0 is the prior mean; and κ 0 is the number of prior measurements on the scale. We put a noninformative prior on σ u: The joint posterior density can be written as: p(σ u) (σ u) 1 (7) p(y 10, y 1,..., y N0, y N, θ 1,..., θ N, θ, N, σu) [ f(y i0, y i θ i )f(θ i θ, )]f( θ )f( )f(σu) i=1 N σu 1 i=1 N σu T i=1 1 γi exp[ 1 σ u (1 γi )(y i0 η i ) ] exp[ 1 σ u N exp[ 1 T (y it γ i y i,t 1 α i ) ] t=1 N (θ i θ) 1 (θ i θ)] i=1 ((ν0+n)/+1) exp[ 1 tr(λ 0 1 ) κ 0 ( θ µ 0 ) T 1 ( θ µ 0 )] σu. 5

6 We derive the posterior conditional densities from the joint density above: p(γi y i0, y i, η i, θ,, σu) = C 1 σu 1 1 γi exp[ 1 σ u (1 γi )(y i0 η i ) ] (8) σ T u exp[ 1 σ u T (y it γ i y i,t 1 η i (1 γ i )) ] (9) t=1 N(µ γ + ρ σ γ σ η (η i µ η ), σ γ (1 ρ )) (10) where N(c1,c) denotes the normal density with mean c1 and variance c. p(η i y i0, y i, γi, θ,, σu) = C σu 1 1 γi exp[ 1 σ u (1 γi )(y i0 η i ) ] where C 1, C are constants and, σ T u exp[ 1 σ u T (y it γ i y i,t 1 η i (1 γ i )) ] t=1 N(µ η + ρ σ η (γi µ σ γ), ση(1 ρ )) γ = N( B A, σ uση(1 ρ ) ) A A = (1 γi )ση(1 ρ ) + T (1 γ i ) ση(1 ρ ) + σu B = T (1 γi )y i0 ση(1 ρ ) + (y it γ i y i,t 1 )ση(1 ρ )(1 γ i ) + σu(µ η + ρ σ η (γi µ γ)) t=1 p( y 0, y, θ 1,..., θ N, θ, σ u) = IW νn (Λ 1 n ) σ γ p( θ y 0, y, θ 1,..., θ N,, σ u) = N(µ n, /κ n ) p(σ u y 0, y, θ 1,..., θ N, θ, ) = Inv χ (v + N, (s ) ), where v = NT and (s ) = v v+n s 1 + N v+n s with s 1 = 1 N T (y it γ i y i,t 1 α i ) v i=1 t=1 s = 1 N (1 γi )(y i0 η i ) N i=1 6

7 HPT applied Gibbs sampling in their Bayes estimation of dynamic panel data models. Gibbs sampling is a Markov chain Monte Carlo algorithm that successively draws components of the parameter vector from the posterior distribution conditional on the other components of the parameter vector (Gelfand and Smith, 1990). To use Gibbs sampling, one needs to be able to draw from the posterior conditional distributions. However, in our model, the posterior conditional distribution of γi given the other parameter is not easy to draw from. Instead of Gibbs sampling, we use a Metropolis Hastings within Gibbs algorithm that is a particular type of Metropolis Hastings algorithm (Gilks, 1996). To obtain a new sample from the posterior distribution, we successively draw, θ, σ u and (η 1,..., η N ) from the above posterior conditional distributions, conditioning on the most recently drawn values of the parameters. We then use the following Metropolis step to draw a new γ i for i = 1,..., N. We draw γ i,trial from an easy to draw distribution g(x) described below. Letting γ i,old denote the γ i from the previous sample, we compute the acceptance ratio r = f(γ i,trial ) f(γ i,old ) g(γ i,old ) g(γ i,trial ), where f( ) = p(γ i y i0, y i, η i, θ,, σ u) is the posterior distribution of γ i conditional on the current samples of all the other parameters,. If r is larger than 1, we set γ i,new = γ i,trial as our new sample of γi ; if r is less than 1, we draw a uniform number u and set γ i,new = γ i,trial if r u and otherwise set γi,new = γ i,old. See Gilks (1996) for further discussion of the Metropolis-Hastings within Gibbs sampling approach. We use g(γ i η i) = N(µ γ + ρ σ γ σ η (η i µ η ), σ γ (1 ρ )) as our proposal density for the true density p(γ i y i0, y i, η i, θ,, σ u). Our proposal density is the marginal distribution of γ i given η i, θ, and σ u (but not the data y). As long as the data is not highly informative about γ i, we have found that this proposal density is not too far from the true posterior conditional density p(γ i y i0, y i, η i, θ,, σ u). For case, we have the linear form (4) for the initial conditions. We choose independent priors for a, b and σv with a and b having normal priors N(0, σa) and N(0, σb ) respectively and σv having the noninformative prior p(σv) (σv) 1. We keep other prior distributions the same as in case 1. The conditional posterior densities for case are the following: p(a y 0, y, θ 1,..., θ N, θ,, b, σv) = Cσa 1 exp[ 1 σ a a ] 7

8 N i=1 σv 1 exp[ 1 σ v (y i0 aη i bη i γ i ) ] Ni=1 = N( σ a η i (y i0 bη i γ i ) σ σa Ni=1 ηi +, vσ a σ v σa Ni=1 ηi + ) σ v p(b y 0, y, θ 1,..., θ N, θ,, a, σ v) = Cσ N b N i=1 exp[ N σ b b ] σv 1 exp[ 1 σ v (y i0 aη i bη i γ i ) ] Ni=1 = N( σ b η i γ i (y i0 aη i ) σ σb Ni=1 ηi γ i +, vσ b σ v σb Ni=1 ηi γ i + ) σ v p(γi y i0, y i, η i, θ,, σu, a, b, σv) = C 1 σv 1 exp[ 1 σ v (y i0 aη i bη i γ i ) ] σ T u exp[ 1 σ u T (y it γ i y i,t 1 η i (1 γ i )) ] t=1 N(µ γ + ρ σ γ σ η (η i µ η ), σ γ (1 ρ )) p(η i y i0, y i, γi, θ,, σu, a, b, σv) = C σv 1 exp[ 1 σ v (y i0 aη i bη i γ i ) ] where C 1, C are constants and, σ T u exp[ 1 σ u T (y it γ i y i,t 1 η i (1 γ i )) ] t=1 N(µ η + ρ σ η σ γ i (γ i µ γ), σ η(1 ρ )) = N( B A, σ vσ uσ η(1 ρ ) A ) A = (a + bγ i ) σuσ η(1 ρ ) + T (1 γ i ) σvσ η(1 ρ ) + σvσ u B = T (a + bγ i )y i0 σuσ η(1 ρ ) + (y it γ i y i,t 1 )σvσ η(1 ρ )(1 γ i ) + σvσ u(µ η + ρ σ η (γi µ γ)) t=1 σ γ p( y 0, y, θ 1,..., θ N, θ, σ u, a, b, σ v) = IW νn (Λ 1 n ) 8

9 p( θ y 0, y, θ 1,..., θ N,, σ u, a, b, σ v) = N(µ n, /κ n ) p(σu y 0, y, θ 1,..., θ N, θ,, a, b, σv) = IG( NT, 1 N T (y it γ i y i,t 1 α i ) ) i=1 t=1 p(σv y 0, y, θ 1,..., θ N, θ,, a, b, σv) = IG( N, 1 N (y i0 aη i bη i γ i ) ) i=1 where IG stands for Inverse-Gamma distribution. All of the above conditional densities have standard forms except for the conditional density of γi. We apply the same Metropolis within Gibbs Sampling method as for case 1 to draw γ i. model, Covariates can easily be incorporated into our Bayesian framework. For example, consider the y it = γ i y i,t 1 + β i x it + α i + u it where x is an exogenous covariate. We can assume β i N(µ β, σβ ) and choose a Normal-Inverse χ prior for the hyperparameters µ β and σβ. Our Metropolis-within-Gibbs approach can be used to obtain draws from the posterior distribution with the addition of two Gibbs steps for draws of (β 1,..., β n ) and (µ β, σ β ). 4 The Monte Carlo Study 4.1 Design of Study We constructed a Monte Carlo study to examine the performance of our hierarchical Bayes approach. We generate data from the model, y it = γ i y i,t 1 + α i + u it, (11) where (γi, η i) have a bivariate normal distribution with mean θ ( exp(γ ) and covariance, γ i = i ) 1+exp(γi ).5 and η i = α i /(1 γ i ). The different cases of the true parameter values we consider are shown in 9

10 Table 1. The disturbances u it are generated from a normal distribution N(0, σu). Because σ u is not a parameter of interest in our model, we take the value of σ u to be 0.1 in all cases. To reflect the effect of coefficient heterogeneity, we use a similar design as HPT, where σ γ and σ η are chosen to be equal to either the mean coefficients or half of the mean coefficients. In our design, the mean coefficients have the values µ γ = 0.3 or 0.6 and µ η = 0.1 or 0.. The number of cross-sectional units is N = 50 or 1000 and the number of time periods is T = 5 or 0. For case 1, where the process starts from infinite past, y i0 is generated from a normal distribution with mean α i σu 1 γ i and variance. For case, where the initial value y 1 γi i0 is generated according to the linear form y i0 = η i (a + bγ i ) + v i0, we choose values 0 and 0.8 for a and b separately. v i0 is a mean zero normal with standard deviation σ v = 0.1. The prior parameters σ a and σ b are set to be 10. For the hyperparameters, we take µ 0 = (0, 0), κ 0 = 0, ν 0 = 1 and Λ 11 = Λ = , Λ 1 = Λ 1 = 0, where Λ ij, i = 1,, j = 1, is the ijth element of Λ 0. Our Bayesian procedure works with the transformed autoregressive coefficient γi, therefore we obtain estimates of µ γ and σ γ from our procedure. To obtain our parameters of interest µ γ and σ γ, we use numerical integration (Gaussian Quadrature). The values of (µ γ, σ γ ) that correspond to (µ γ, σ γ ) are reported in Table 1 and we show the final results in terms of µ γ and σ γ in other tables. To monitor the convergence of our MCMC algorithm, we use the method suggested in Gelman (1996). We calculate an estimate ˆR which summarizes the ratio of between- and within-sequence variances. When ˆR is near 1, we normally view the sequence as having converged. In our study, we use 0 sequences with scattered starting values and ˆR generally becomes close to 1 after 500 iterations. We set the number of iterations to be 000 and, to be conservative, discarded the first 1000 iterations. We evaluate the frequentist properties of our Bayesian procedure by repeating the simulation 00 times. 4. Analysis of Results The Monte Carlo study results are presented in Tables to 7. Table to 6 report results for the initial values y i0 generated from the stationary distribution (Case 1). Table shows the bias of µ γ and corresponding root mean square error (RMSE). In all cases, the hierarchical Bayes 10

11 estimator performs much better than the mean group estimator. The mean group estimator is biased downwards heavily in many cases. The hierarchical Bayes estimator performs very well even when both N and T are small (N = 50, T = 5) with bias at most 13%. When T=0, the bias in most cases drops below %. The RMSE of the mean group estimator is much larger than the RMSE of the hierarchical Bayes estimator in all cases. For example, for N=50,T=5, the RMSE for the hierarchical Bayes estimator is at most 0% of the RMSE for the mean group estimator. Table 3 shows the results for µ η. The mean group estimator s bias is acceptable in most cases but it is heavily biased in cases 3 and 7. With one exception of a bias of 11% in case 3 for N=1000, the hierarchical Bayes estimator has consistently low bias of less than 6%. The RMSE of the mean group estimator is much larger than that of the hierarchical Bayes estimator for all cases, more than ten times as large in many cases. Table 4 shows the results for σ γ. We show the results of the Swamy estimator along with our hierarchical Bayes estimators. Proposed by Swamy(1971), the Swamy estimator (1971) is: ˆ = 1 N N (ˆθ i 1 N i=1 N ˆθ i )(ˆθ i 1 N i=1 N ˆθ i ) 1 N i=1 N ˆσ i (Z iz i ) 1 (1) i=1 where ˆσ i û iûi = T k. Following HPT, we drop the second term on the righthand side of (1), which is O p (T 1 ), in order to ensure that the estimate of ˆ is non-negative definite. When N = 50 and T = 5, the Swamy estimator overestimates and the hierarchical Bayes underestimates the true values except for case 3 and 4. When N increases to 1000, the bias of the Swamy estimators does not significantly improve but the performance of hierarchical Bayes estimator improves substantially, with bias at most 0%. When T increases to 0, both estimators improve substantially, but the hierarchical Bayes estimators have much less bias, less than 3% in most cases. The RMSE of the hierarchical Bayes estimator is lower than that of the Swamy estimator in all cases, especially when for N and T large. Table 5 shows the hierarchical Bayes estimator of σ η. The bias seldom exceeds 10% except for case 3. In Table 6, we report the empirical coverage rates for µ γ and µ η for 90% and 95% credibility intervals. When both N and T are small, the coverage rate for µ γ is significantly lower than the nominal level. In the other cases, the empirical coverage rates are generally around the nominal levels, indicating that the credibility intervals can be used as approximate confidence intervals. 11

12 Table 7 gives the results when we assume a linear form for initial values (Case ). For most settings, the hierarchical Bayes estimates perform well and have little bias. For cases 5 and 7, the estimates of σ γ, a and b exhibit some bias for N=50 but the bias disappears for N= Empirical Application To illustrate our methods, we consider the panel of firms used by Alonso-Borrego and Arellano (1999). This is a balanced panel of 738 Spanish manufacturing companies, for which there are available annual observations for the period We focus on estimating a dynamic random coefficient model for firms employment levels: y it = β t + γ i y i,t 1 + α i + u it, (13) where y it is the employment level of the ith firm in the tth time period. In this model, time dummies β t are included to capture time-varying macroeconomic effects. Our model can be modified to incorporate these time dummies as follows. We choose a normal prior N(µ β, σβ ) on β t, t = 1,,... and we set the baseline level β 0 = 0. Under this prior, we have only some minor changes in the conditional densities presented in section 3 and one more posterior for β t in the Bayesian procedure, p(β t y 0, y, θ 1,..., θ N, θ,, σu) = N( B 1E1 + A 1D1 E1 +, D 1 D 1 E 1 for t = 1,,...T and A 1 = 1 n ni=1 (e it γ i e i,t 1 α i ), B 1 = µ β, D 1 = σ β, E 1 = σ u n D1 + ) (14) E 1 Our hierarchical Bayes estimate of µ γ is 0.83 and the estimate of σ γ is 0.5. We also get estimates of the time effects β t (t=1,...,7) and the values match with the actual employment levels very well. The estimation results are reported in Table 8. Arellano (003) reports that the GMM estimate of µ γ for a homogeneous (γ i = µ γ ) version of (18) is This is close to our estimate of µ γ. Because there is only a small amount of heterogeneity in the coefficients of different firms(σ γ = 0.5), the GMM estimator of µ γ is not heavily biased in this setting. 1

13 INSERT FIGURE 1 AND HERE 6 Conclusion Our paper further develops Hsiao, Pesaran and Tahmiscioglu s hierarchical Bayesian method for random coefficient dynamic panel data. Instead of treating initial observations as fixed constants, we allow the initial values to either come from a stationary process or a flexible process. We also use the logitnormal distribution to enforce a stationary constraint on the coefficient γ i. We use a Metropolis-within-Gibbs-Sampling algorithm to generate the hierarchical Bayes estimators. Our Monte Carlo study provides evidence that these estimates have good frequentist properties. The hierarchical Bayes estimators performs well even when both N and T are small and perform substantially better than the mean group estimator. 13

14 References Alonso-Borrego, C. and Arellano, M. (1999) Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data. Journal of Business&Economic Statistics, 17, Arellano and Honoré (001), Handbook of Econometrics, Elserier, Amsterdam. Frederic, P. and Lad, F. (003) A Technical Note on the Logitnormal distribution. University of Canterbury Mathematics and Statistics Research Report. Gelfand, A.E. and Smith, A.M.F. (1990) Sampling-Based Approaches to Calculating Marginal Densities. Journal of the American Statistical Association, 85, Gelman,A. (1996) Markov Chain Monte Carlo in Practice. Chapman&Hall. Chapter 8, pp Gilks,W.R. (1996) Markov Chain Monte Carlo in Practice. Chapman&Hall. Chapter 5, pp Hsiao, C.,Pesaran, M.H. and Tahmiscioglu, A.K. (1999) Bayes Estimation of Short-Run Coefficients in Dynamic Panel Data Models in (eds.) C. Hsiao, K. Lahiri, L-F Lee and M.H. Pesaran, Analysis of Panels and Limited Dependent Variables: A Volume in Honour of G S Maddala, Cambridge University Press, Cambridge, 1999, chapter 11, pp Haque, N., Pesaran, M.H. and Sharma, S. (000), Neglected Heterogeneity and Dynamics in Cross-country Savings Regressions in (eds.) J.Krishnakumar and E. Ronchetti, Panel Data Econometrics Future Directions: Papers in Honor of Pietro Balestra, Elserier, Amsterdam. Hsiao, C. (003) Analysis of panel data: Second Edition. Cambridge University Press, Cambridge. Hu, X. and Ng, S (004) Estimating Covariance Structures of Dynamic Heterogeneous Panels Working Paper. Imbs, Mumtaz,Ravn and Rey (005) PPP strikes back: Aggregation and the Real Exchange Rate, The Quarterly Journal of Economics, 10, 1-43 Pesaran,M.H. and Smith,R. (1995), Estimating Long-Run Relationships from Dynamic Heterogeneous Panels, Journal of Econometrics, 68,

15 Swamy, P.A.V.B. (1971), Statistical Inference in Random Coefficient Models. Berlin: Springer- Verlag. 15

16 Table 1: Monte Carlo Design µ γ σ γ µ γ σ γ µ η σ η a b

17 Table : Bias and RMSE of Coefficient µ γ Mean Group Hierarchical Bayes µ γ Percent Bias(%) RMSE Percent Bias(%) RMSE n=50,t= n=50,t= n=1000,t= n=1000,t=

18 Table 3: Bias and RMSE of Coefficient µ η Mean Group Hierarchical Bayes µ η Percent Bias(%) RMSE Percent Bias(%) RMSE n=50,t= n=50,t= n=1000,t= n=1000,t=

19 Table 4: Bias and RMSE of Coefficient σ γ Swamy Hierarchical Bayes σ γ Percent Bias(%) RMSE Percent Bias(%) RMSE n=50,t= n=50,t= n=1000,t= n=1000,t=

20 Table 5: Bias and RMSE of Coefficient σ η Hierarchical Bayes σ η Percent Bias(%) RMSE n=50,t= n=50,t= n=1000,t= n=1000,t=

21 Table 6: Coverage Rate of the Estimates of µ γ and µ η µ γ µ η 90% 95% 90% 95% n=50,t= n=50,t= n=1000,t= n=1000,t=

22 Table 7: Percent Bias of Parameters in Case µ γ µ η σ γ σ η a b n=50,t= n=50,t= n=1000,t= n=1000,t=

23 Table 8: Spanish Firm Data Estimates Hierarchical Bayes µ γ 0.83 σ γ 0.5 µ α 4.8 σ α 1.0 β β β β β β β

24 Frequency Figure 1: Histogram of the coefficient γ for one individual firm. Frequency Figure : Histogram of the mean coefficient µ γ 4