Badi H. Baltagi

UNEQUALLY SPACED PANEL DATA REGRESSIONS WITH AR( 1) DISTURBANCES

This paper deals with the estimation of unequally spaced panel data regression models with AR(1) remainder disturbances. A feasible generalized least squares (GLS) procedure is proposed as a weighted least squares that can handle a wide range of unequally spaced panel data patterns. This procedure is simple to compute and provides natural estimates of the serial correlation and variance components parameters. The paper also provides a locally best invariant test for zero first-order serial correlation against positive or negative serial correlation in case of unequally spaced panel data.

Nonstationary Panels, Cointegration in Panels and Dynamic Panels: A Survey

This paper provides an overview of topics in nonstationary panels: panel unit root tests, panel cointegration tests, and estimation of panel cointegration models. In addition it surveys recent developments in dynamic panel data models.

A General Index of Technical Change

This paper outlines a procedure for estimating a general index of technical change within the context of a quite general production technology. Specifically, when panel data are available for firms in an industry, time-specific dummies can be combined in a nonlinear estimation procedure to yield a general index of technical change that may be both nonneutral and scale augmenting. This approach offers numerous advantages over the traditional time trend representation of technical change. For example, the general index can serve as the basis for analysis of the determinants of technical change. Results for a sample of 30 electric utilities over the period 1951-78 show that the productivity decline of the 1970s can be attributed primarily to sulphur oxide restrictions and secularly declining capacity utilization due to rapidly increasing peak-load demands.

A generalized design for bilateral trade flow models

Abstract This paper suggests a full interaction effects design to analyze bilateral trade flows. This is illustrated with an unbalanced panel of bilateral trade between the triad (EU15, USA and Japan) economies and their 57 most important trading partners over the period 1986–1997. Our full interaction model finds empirical support for the New Trade Theory and Linder’s hypothesis. We show that the omission of one or more interaction effects can result in biased estimates and misleading inference.

Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline

Abstract This paper utilizes an international panel data set and a dynamic demand specification for gasoline to compare the performance of homogeneous and heterogeneous parameter estimators. In addition to comparing the plausibility of the various estimates, a forecast performance comparison is performed to examine differences in predictions over one-, five-, and ten-year horizons.

TO POOL OR NOT TO POOL: HOMOGENEOUS VERSUS HETEROGENEOUS ESTIMATORS APPLIED TO CIGARETTE DEMAND

This paper reexamines the benefits of pooling and, in addition, contrasts the performance of newly proposed heterogeneous estimators. The analysis utilizes a panel data set from 46 American states over the period 1963 to 1992 and a dynamic demand specification for cigarettes. Also, the forecast performance of the various estimators is compared.

Simultaneous equations with error components

Abstract In this paper we derive a limited as well as a full information estimator for the structural parameters of a simultaneous equations model with error components. Under this model, the gain in efficiency by performing these estimators rather than the classical two-stage and three-stage least squares procedures is demonstrated. It is shown that the full information estimator will reduce to the limited information estimator when the disturbances of different structural equations are uncorrelated with each other but not necessarily when all structural equations are just identified. This is different from the analogous situation in the classical case.

Fixed effects, random effects or Hausman-Taylor?: A pretest estimator

Abstract This paper suggests a pretest estimator based upon two Hausman tests as an alternative to the fixed effects or random effects estimators for panel data models. The bias and RMSE properties of these estimators are investigated using Monte Carlo experiments.

Gasoline demand in the OECD: An application of pooling and testing procedures

Abstract This study utilizes a pooled inter-country data set, finding the long-run price-elasticity falls in the range −0.55 to −0.9, depending on the choice of pooled estimators. The estimators included the OLS, within-, and between-country estimators, plus five feasible GLS estimators. Even allowing for a ten-year distributed lag on price to reflect changes in auto-efficiency characteristics, the within-country estimator yields appreciably more inelastic estimates than did the O:S estimator, which was heavily influenced by the between- or inter-country variation. This difference raises intriguing questions for future research.

Incomplete panels. A comparative study of alternative estimators for the unbalanced one-way error component regression model

Abstract This paper considers a one-way error component regression model with unbalanced data and investigates by means of Monte Carlo experiments the performance of ANOVA, MLE, and MIVQUE type estimators of the variance components. Some of the basic findings are the following: (1) For the regression coefficients, the computationally simple ANOVA methods perform reasonably well when compared with the computationally involved MLE and MIVQUE methods. (2) MLE and MIVQUE perform better than the ANOVA methods in the estimation of the individual variance component, especially for severely unbalanced patterns and large variance component ratio. However, for the remainder variance component, there is nothing much to choose among these methods. (3) Better estimates of the variance components do not necessarily imply better estimates of the regression coefficients. (4) Making the data balanced, by dropping observations, worsens the performance of these estimators when compared to those from the entire unbalanced data.