Tests for Unbalanced Error Component Models Under Local Misspecication

This paper derives unbalanced versions of the tests statistics for ¯rst order serial correlation and random individual e®ects summarized in Sosa Escudero and Bera (2001), and updates their xttest1 routine. The derived tests statistics should be useful for applied researchers faced with the increasing availability of panel information where not every individual or country is observed for the full time span. Also, as it was the case of the previously available tests, the test statistics proposed here are based on the OLS residuals, and hence are computationally very simple.error components model, unbalanced panel data, testing, misspecification.

Estimating Functions and Equations: An Essay on Historical Developments with Applications to Econometrics

The idea of using estimating functions goes a long way back, at least to Karl Pearson's introduction to the method of moments in 1894. It is now a very active area of research in the statistics literature. One aim of this chapter is to provide an account of the developments relating to the theory of estimating functions. Starting from the simple case of a single parameter under independence, we cover the multiparameter, presence of nuisance parameters and dependent data cases. Application of the estimating functions technique to econometrics is still at its infancy. However, we illustrate how this estimation approach could be used in a number of time series models, such as random coefficient, threshold, bilinear, autoregressive conditional heteroscedasticity models, in models of spatial and longitudinal data, and median regression analysis. The chapter is concluded with some remarks on the place of estimating functions in the history of estimation.

Robust tests for heteroskedasticity and autocorrelation in the multiple regression model: Working paper series--02-05

The standard Rao's (1948) score or Lagrange multiplier test for heteroskedasticity was originally developed assuming normality of the disturbance term [see Godfrey (1978b), and Bruesch and Pagan (1979)]. Therefore, the resulting test depends heavily on the normality assumption. Koenker (1981) suggests a studentized for which is robust to nonnormality. This approach seems to be limited because of the unavailability of a general procedure that transforms a test to a robust one. Following Bickel (1978), we use a different approach to take account of nonnormality. Our tests will be based on the score function which is defined as the negative derivitive of the log-density function with respect to the underlying random variable. To implement the test we use a nonparametric estimate of the score function. Our robust test for heteroskedasticity is obtained by running a regression of the product of the score function and ordinary least squares residuals on some exogenous variables which are thought to be causing the heteroskedasticity. We also use our procedure to develop a robust test for autocorrelation which can be computed by regressing the score function on the lagged ordinary least squares residuals and the independent variables. Finally, we carry out an extensive Monte Carlo study which demonstrates that our proposed tests have superior finite sample properties compared to the standard tests

Tests for unbalanced error component models under local misspecification

This paper derives unbalanced versions of the tests statistics for first order serial correlation and random individual e®ects summarized in Sosa Escudero and Bera (2001), and updates their xttest1 routine. The derived tests statistics should be useful for applied researchers faced with the increasing availability of panel information where not every individual or country is observed for the full time span. Also, as it was the case of the previously available tests, the test statistics proposed here are based on the OLS residuals, and hence are computationally very simple.Trabajo publicado en The Stata Journal, Volume 8, Number 1, pp. 68-78.Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS

Tests for the error component model in the presence of local misspecification

It is well known that most of the standard specification tests are not valid when the alternative hypothesis is misspecified. This is particularly true in the error component model, when one tests for either random effects or serial correlation without taking account of the presence of the other effect. In this paper we study the size and power of the standard Rao´s score tests analytically and by simulation when the data is contaminated by local misspecification. These tests are adversely affected under misspecification. We suggest simple procedures to test for random effects (or serial correlation) in the presence of local serial correlation (or random effects), and these tests require ordinary least squares residuals only. Our Monte Carlo results demonstrate that the suggested tests have good finite sample properties for local misspecification, and in some cases even for far distant misspecification. Our tests are also capable of detecting the right direction of the departure from the null hypothesis. We also provide some empirical illustrations to highlight the usefulness of our tests.Departamento de Economí