[PDF] Biases In Dynamic Models With Fixed Effects eBook

Author : Iván Fernández-Val
Publisher :
Page : 54 pages
File Size : 27,7 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
Publisher :
Page : 61 pages
File Size : 28,78 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 : Yoonseok Lee
Publisher :
Page : 0 pages
File Size : 38,93 MB
Release : 2011
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We consider within-group estimation of higher-order autoregressive panel models with exogenous regressors and fixed effects, where the lag order is possibly misspecified. Even when disregarding the misspecification bias, the fixed-effect bias formula is quite different from the correctly specified case though its asymptotic order remains the same under the stationarity. We suggest bias reduction methods under the possible time series misspecification.

Author : Yoonseok Lee
Publisher :
Page : 0 pages
File Size : 49,46 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 : Thomas B. Fomby
Publisher : Emerald Group Publishing
Page : 772 pages
File Size : 43,95 MB
Release : 2014-11-21
Category : Political Science
ISBN : 1784411825

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This volume honors Professor Peter C.B. Phillips' many contributions to the field of econometrics. The topics include non-stationary time series, panel models, financial econometrics, predictive tests, IV estimation and inference, difference-in-difference regressions, stochastic dominance techniques, and information matrix testing.

Author : Lászlo Mátyás
Publisher : Springer Science & Business Media
Page : 966 pages
File Size : 38,50 MB
Release : 2008-04-06
Category : Business & Economics
ISBN : 3540758925

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This restructured, updated Third Edition provides a general overview of the econometrics of panel data, from both theoretical and applied viewpoints. Readers discover how econometric tools are used to study organizational and household behaviors as well as other macroeconomic phenomena such as economic growth. The book contains sixteen entirely new chapters; all other chapters have been revised to account for recent developments. With contributions from well known specialists in the field, this handbook is a standard reference for all those involved in the use of panel data in econometrics.

Author : Henning Best
Publisher : SAGE
Page : 425 pages
File Size : 39,51 MB
Release : 2013-12-20
Category : Social Science
ISBN : 1473908353

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′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.