A model for network panel data is discussed, based on the assumption that the observed data are discrete observations of a continuous-time Markov process on the space of all directed graphs on a given node set, in which changes in tie variables are independent conditional on the current graph. The model for tie changes is parametric and designed for applications to social network analysis, wher...

This paper considers models for panel data in which the individual effects vary over time. The temporal pattern of variation is arbitrary, but it is the same for all individuals. The model thus allows one to control for time-varying unobservables that are faced by all individuals (e.g., macroeconomic events) and to which individuals may respond differently. A generalized within estimator is con...

There is a large literature devoted to the size of the response of nominal interest rates to changes in expected inflation, the Fisher effect. The interest in the topic stems from the implications of the Fisher effect for both asset pricing models and monetary neutrality propositions. Unfortunately no consensus has been achieved concerning the Fisher effect. Part of the problem for this has bee...

In this article we extend the comparative analysis between the results of a pooled OLS regression and the use of fixed effects panel models concerning the determinants of debt, comparing the results of using static panel models and dynamic panel estimators, including the dynamic estimator of correction of fixed effects. The results show that the differences between the results of static panel m...

In this paper we study the least squares (LS) estimator in a linear panel regression model with unknown number of factors appearing as interactive fixed effects. Assuming that the number of factors used in estimation is larger than the true number of factors in the data we establish the limiting distribution of the LS estimator for the regression coefficients, as the number of time periods and ...

We modify a previously suggested GMM estimator in a spatial panel regression model by taking into account the difference between disturbances and regression residuals and derive its asymptotic properties. Simulation results and an empirical application to Indonesian rice data illustrate the improvement in finite samples. JEL Classification: C13, C21

This paper considers the problem of estimating a partially linear semiparametric fixed effects panel data model with possible endogeneity. Using the series method, we establish the root N normality result for the estimator of the parametric component, and we show that the unknown function can be consistently estimated at the standard nonparametric rate.

This paper considers the problem of estimating a partially linear semipara-metric fixed effects panel data model with possible endogeneity. Using the series method, we establish the root N normality result for the estimator of the parametric component, and we show that the unknown function can be consistently estimated at the standard nonparametric rate. c 2002 Peking University Press

This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao, Pesaran, and Tahmiscioglu (2002) to the case where the errors are crosssectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem that arises, and its implications for estimation and inference. We approach the problem by working with a mis...

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although con...