In this paper, we study the asymptotic distributions for least-squares (OLS), fully modified (FM), and dynamic OLS (DOLS) estimators in cointegrated regression models in panel data. We show that the OLS, FM, and DOLS estimators are all asymptotically normally distributed. However, the asymptotic...

This paper presents an inference approach for dependent data in time series, spatial, and panel data applications. The method involves constructing and Wald statistics using a cluster covariance matrix estimator (CCE). We use an approximation that takes the number of clusters/groups as fixed and...

In this paper we introduce fixed-T unit root tests for panel data models with serially correlated and heteroscedastic disturbance terms. The tests are based on pooled least squares estimators for the autoregressive coefficient of the AR(1) panel model adjusted for their inconsistency. The...

In this paper, we apply the asymptotic theory of panel cointegration developed by Kao and Chiang (1997) to Coe and Helpman's (1995) international R&D spillovers regression. The OLS with bias-correction, the fully-modified (FM) and the dynamic OLS (DOLS) estimations produce different predictions...

This paper merges two isolated bodies of literature: the Markov chain model with macro data (MacRae, 1977) and the ecological inference model (Robinson, 1950). Both are choice models. They have the same likelihood function and the same regression equation.Decades ago, this likelihood function...

We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns. Our approach augments the popular two‐way‐fixed‐effects specification with unit‐specific weights that arise from a model for the assignment mechanism....

We propose procedures for estimating the time-dependent transition matrices for the general class of finite nonhomogeneous continuous-time semi-Markov processes. We prove the existence and uniqueness of solutions for the system of Volterra integral equations defining the transition matrices,...

Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks to macroeconomic indicators. In this paper we examine various choices in the specification of quantile regressions for macro applications, for example, choices related...

This paper reviews the literature on applications of state-space modelling to macroeconomic questions, with four examples related to modelling unobserved trends, transition across different steady states, expectations formation and forecasting/data revision issues. Due to the flexibility of the...