This paper investigates efficient estimation of heterogeneous coefficients in panel data models with common shocks, which have been a particular focus of recent theoretical and empirical literature. We propose a new two-step method to estimate the heterogeneous coefficients. In the first step,...

This paper considers the maximum likelihood estimation of the panel data models with interactive effects. Motivated in economics and other social sciences, a notable feature of the model is that the explanatory variables are correlated with the unobserved effects. The usual within-group...

An approximate factor model of high dimension has two key features. First, the idiosyncratic errors are correlated and heteroskedastic over both the cross-section and time dimensions; the correlations and heteroskedasticities are of unknown forms. Second, the number of variables is comparable or...

Spatial effects and common-shocks effects are of increasing empirical importance. Each type of effect has been analyzed separately in a growing literature. This paper considers a joint modeling of both types. Joint modeling allows one to determine whether one or both of these effects are...

This paper considers the problem of estimating a simultaneous spatial autoregressive model (SSAR). We propose using the quasi maximum likelihood method to estimate the model. The asymptotic properties of the maximum likelihood estimator including consistency and limiting distribution are...

Motivated by the great moderation in major U.S. macroeconomic time series, we formulate the regime switching problem through a conditional Markov chain. We model the long-run volatility change as a recurrent structure change, while short-run changes in the mean growth rate as regime switches....

We propose a simple and intuitive method for estimating betas when factors are measured with error: ordinary least squares instrumental variable estimator (OLIVE). OLIVE performs well when the number of instruments becomes large, while the performance of conventional instrumental variable...

The paper proposes statistics to test the null hypothesis of no cointegration in panel data when common factors drive the cross sectional dependence. We consider both the case in which regressors are independent of the common factors and the case in which regressors are correlated with the...

This paper studies the weak convergence of the sequential empirical process $\hat{K}_n$ of the estimated residuals in ARMA(p,q) models when the errors are independent and identically distributed. It is shown that, under some mild conditions, $\hat{K}_n$ converges weakly to a Kiefer process. The...

This paper considers a mean shift with an unknown shift point in a linear process and estimates the unknown shift point (change point) by the method of least squares. Pre-shift and post-shift means are estimated concurrently with the change point. The consistency and the rate of convergence for...