This paper considers the problem of prediction in a panel data regression model with spatial auto-correlation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965-1994. The spatial auto-correlation due to neighboring states and the...

This paper proposes a first-order zero-drift GARCH (ZD-GARCH(1, 1)) model to study conditional heteroscedasticity and heteroscedasticity together. Unlike the classical GARCH model, ZD-GARCH(1, 1) model is always non-stationary regardless of the sign of the Lyapunov exponent $\gamma_{0}$ , but...

In this article we develop a tractable procedure for testing strict stationarity in a double autoregressive model and formulate the problem as testing if the top Lyapunov exponent is negative. Without strict stationarity assumption, we construct a consistent estimator of the associated top...

This paper considers the problem of prediction in a panel data regression model with spatial autocorrelation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965-1994. The spatial autocorrelation due to neighboring states and the...

This paper derives Lagrangian Multiplier tests to jointly test for functional form and spatial error correlation. In particular, this paper tests for linear and loglinear models with no spatial error dependence against a more general Box-Cox model with spatial error correlation. Conditional LM...

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...

We examine some aspects of estimating sample autocovariances for spatial processes. Especially, we note that for such processes, it is not possible to approximate the expectation by the sample mean, like in the case of time series data. Then, we propose a consistent nonparametric estimation of...

We examine some aspects of estimating sample autocovariances for spatial processes. Especially, we note that for such processes, it is not possible to approximate the expectation by the sample mean, like in the case of time series data. Then, we propose a consistent nonparametric estimation of...

This note generalizes the results in Li et al. (2012) to threshold moving‐average (TMA) models with more than two regimes. Under some mild conditions, it is shown that multiple‐regime TMA models are always strictly stationary and ergodic without any restriction on the coefficients. This is...