[PDF] Correcting The Biases In Dynamic Models With Fixed Effects eBook

Author : Jinyong Hahn
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Page : 61 pages
File Size : 14,50 MB
Release : 2001
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This paper analyzes the second order bias of instrumental variables estimators for a dynamic panel model with fixed effects. Three different methods of second order bias correction are considered. Simulation experiments show that these methods perform well if the model does not have a root near unity but break down near the unit circle. To remedy the problem near the unit root a weak instrument approximation is used. We show that an estimator based on long differencing the model is approximately achieving the minimal bias in a certain class of instrumental variables (IV) estimators. Simulation experiments document the performance of the proposed procedure in finite samples. Keywords: dynamic panel, bias correction, second order, unit root, weak instrument.

Author : Jinyong Hahn
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Page : 0 pages
File Size : 35,8 MB
Release : 2003
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This paper analyzes the second order bias of instrumental variables estimators for a dynamic panel model with fixed effects. Three different methods of second order bias correction are considered. Simulation experiments show that these methods perform well if the model does not have a root near unity but break down near the unit circle. To remedy the problem near the unit root a weak instrument approximation is used. We show that an estimator based on long differencing the model is approximately achieving the minimal bias in a certain class of instrumental variables (IV) estimators. Simulation experiments document the performance of the proposed procedure in finite samples.

Author : Yoonseok Lee
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Page : 0 pages
File Size : 26,79 MB
Release : 2011
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This paper considers higher-order autoregressive (AR(p)) panel models with fixed effects, where the lag order p is unknown and possibly misspecified. A pooled least squares estimator is considered and its asymptotic biases are studied. Specifically, we first extend the N-asymptotic bias formula in Nickell (1981) to the case where the dynamics follow a general autoregressive form. Second, √(NT)-normalized limit distribution for the pooled estimators is developed that allows for lag order misspecification, when both N and T are large. Third, a higher order approximation for the bias up to order N^(-1)T^(-2) is explored. Besides the well-known endogeneity bias incurred by the within-transformation in dynamic fixed-effects models, additional bias under misspecification is analytically derived, which argues that model specification should precede any bias correction in dynamic panel modeling. We suggest a general form for bias correction, which specifically incorporates the lag order selection. A consistent lag order selection criterion is also proposed, which is more suitable for large panel system with fixed effects. Some extensions of the bias correction are also considered under exogenous variable, and the bias corrected short-run and long-run coefficients are discussed. Lastly, as an empirical application, a study on habit formation in consumption preferences is presented using U.S. state-level data.

Author : Iván Fernández-Val
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Page : 54 pages
File Size : 20,12 MB
Release : 2005
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Fixed effects estimators of nonlinear panel models can be severely biased due to the incidental parameters problem. In this paper I find that the most important component of this incidental parameters bias for probit fixed effects estimators of index coefficients is proportional to the true value of these coefficients, using a large-T expansion of the bias. This result allows me to derive a lower bound for this bias, and to show that fixed effects estimates of ratios of coefficients and average marginal effects have zero bias in the absence of heterogeneity and have negligible bias relative to their true values for a wide variety of distributions of regressors and individual effects. Numerical examples suggest that this small bias property also holds for logit and linear probability models, and for exogenous variables in dynamic binary choice models. An empirical analysis of female labor force participation using data from the PSID shows that whereas the significant biases in fixed effects estimates of index coefficients do not contaminate the estimates of marginal effects in static models, estimates of both index coefficients and marginal effects can be severely biased in dynamic models. Improved bias corrected estimators for index coefficients and marginal effects are also proposed for both static and dynamic models.

Author : Jinyong Hahn
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Page : 29 pages
File Size : 38,7 MB
Release : 2000
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We consider a dynamic panel AR(1) model with fixed effects when both "n" and "T" are large. Under the "T fixed n large" asymptotic approximation, the maximum likelihood estimator is known to be inconsistent due to the well-known incidental parameter problem. We consider an alternative asymptotic approximation where "n" and "T" grow at the same rate. It is shown that, although the MLE is asymptotically biased, a relatively simple fix to the MLE results in an asymptotically unbiased estimator. The bias corrected MLE is shown to be asymptotically efficient by a Hajek type convolution theorem. Keywords: dynamic Panel, VAR, large n-large T asymptotics, bias correction, efficiency.

Author : Gary Solon
Publisher :
Page : 68 pages
File Size : 36,42 MB
Release : 1984
Category : Autocorrelation (Statistics)
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This paper discusses the estimation of serial correlation in fixed effects models for longitudinal data. Like time series data, longitudinal data often contain serially correlated error terms, but the autocorrelation estimators commonly used for time series, which are consistent as the length of the time series goes to infinity, are not consistent for a short time series as the size of the cross-section goes to infinity. This form of inconsistency is of particular concern because a short time series of a large cross-section is the typical case in longitudinal data. This paper extends Nickell's method of correcting for the inconsistency of autocorrelation estimators by generalizing to higher than first-order autocorrelations and to error processes other than first-order autoregressions. The paper also presents statistical tables that facilitate the identification and estimation of autocorrelation processes in both the generalized Nickell method and an alternative method due to MaCurdy. Finally, the paper uses Monte Carlo methods to explore the finite-sample properties of both methods.

Author : Jinyong Hahn
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Page : 0 pages
File Size : 17,93 MB
Release : 2005
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This paper investigates a simple dynamic linear panel regression model with both fixed effects and time effects. Using large n and large T asymptotics, we approximate the distribution of the fixed effect estimator of the autoregressive parameter in the dynamic linear panel model and derive its asymptotic bias. We find that the same higher order bias correction approach proposed by Hahn and Kuersteiner (2002) can be applied to the dynamic linear panel model even when time specifc effects are present.