Book contents
- Frontmatter
- Contents
- List of contributors
- Foreword
- Introduction
- 1 A note on left censoring
- 2 Autoregressive models with sample selectivity for panel data
- 3 Mixture of normals probit models
- 4 Estimation of dynamic limited-dependent rational expectations models
- 5 A Monte Carlo study of EC estimation in panel data models with limited dependent variables and heterogeneity
- 6 Properties of alternative estimators of dynamic panel models: an empirical analysis of cross-country data for the study of economic growth
- 7 Modified generalized instrumental variables estimation of panel data models with strictly exogenous instrumental variables
- 8 Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors
- 9 Re-examining the rational expectations hypothesis using panel data on multi-period forecasts
- 10 Prediction from the regression model with one-way error components
- 11 Bayes estimation of short-run coefficients in dynamic panel data models
- 12 Bias reduction in estimating long-run relationships from dynamic heterogeneous panels
- CV of G.S. Maddala
- Index
8 - Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors
Published online by Cambridge University Press: 22 September 2009
- Frontmatter
- Contents
- List of contributors
- Foreword
- Introduction
- 1 A note on left censoring
- 2 Autoregressive models with sample selectivity for panel data
- 3 Mixture of normals probit models
- 4 Estimation of dynamic limited-dependent rational expectations models
- 5 A Monte Carlo study of EC estimation in panel data models with limited dependent variables and heterogeneity
- 6 Properties of alternative estimators of dynamic panel models: an empirical analysis of cross-country data for the study of economic growth
- 7 Modified generalized instrumental variables estimation of panel data models with strictly exogenous instrumental variables
- 8 Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors
- 9 Re-examining the rational expectations hypothesis using panel data on multi-period forecasts
- 10 Prediction from the regression model with one-way error components
- 11 Bayes estimation of short-run coefficients in dynamic panel data models
- 12 Bias reduction in estimating long-run relationships from dynamic heterogeneous panels
- CV of G.S. Maddala
- Index
Summary
Introduction
In linear dynamic panel data models, the dependent variable (and hence also the lagged dependent regressor variable that makes the model dynamic) is determined by some linear function of present and past values of both the explanatory variables and the various stochastic error components. Since regression coefficient estimators are non-linear in all the regressor variables, the coefficient estimators are also non-linear in the stochastic error components in a linear dynamic panel data model. This non-linearity is the reason why the finite sample properties of inference methods for this type of model are very hard to establish. The actual probability distributions of estimators and test statistics are highly non-standard and usually they also depend on unknown values of nuisance parameters. Some knowledge on their actual shape and behavior can be obtained either experimentally by computer simulation or analytically by asymptotic approximations. For common model specifications the standard first-order asymptotic properties have been assessed for various estimation methods. Some methods are found to be inconsistent, but others are consistent and may even be asymptotically efficient for certain classes of models. However, from simulation studies it has often been found that first-order asymptotic properties do not always offer the right criterion for (dis)qualifying inference procedures as far as their finite sample properties are concerned; see for instance, Nerlove (1971), Arellano and Bond (1991), van den Doel and Kiviet (1995), and Kiviet (1995).
- Type
- Chapter
- Information
- Analysis of Panels and Limited Dependent Variable Models , pp. 199 - 225Publisher: Cambridge University PressPrint publication year: 1999
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