Jihai Yu

A SPATIAL DYNAMIC PANEL DATA MODEL WITH BOTH TIME AND INDIVIDUAL FIXED EFFECTS

This paper establishes asymptotic properties of quasi-maximum likelihood estimators for spatial dynamic panel data with both time and individual fixed effects when the number of individuals n and the number of time periods T can be large. We propose a data transformation approach to eliminate the time effects. When n / T → 0, the estimators are null consistent and asymptotically centered normal; when n is asymptotically proportional to T , they are null consistent and asymptotically normal, but the limit distribution is not centered around 0; when n / T → ∞, the estimators are consistent with rate T and have a degenerate limit distribution. We also propose a bias correction for our estimators. When n 1/3 / T → 0, the correction will asymptotically eliminate the bias and yield a centered confidence interval. The estimates from the transformation approach can be consistent when n is a fixed finite number.

ESTIMATION OF UNIT ROOT SPATIAL DYNAMIC PANEL DATA MODELS

This paper examines the asymptotics of the QMLE for unit root dynamic panel data models with spatial effect and fixed effects. We consider a unit root dynamic panel data model with spatially correlated disturbances and a unit root spatial dynamic panel data model. For both models the estimate of the dynamic coefficient is null consistent and the estimates of other parameters are null consistent, and all of them are asymptotically normal. For the latter model the sum of the contemporaneous spatial effect and dynamic spatial effect converges at null rate. We also propose a bias-correction procedure so that the asymptotic biases of those estimates are eliminated as long as n/T 3 → 0.

CONVERGENCE: A SPATIAL DYNAMIC PANEL DATA APPROACH

A spatial dynamic panel data approach is adopted to study regional growth convergence in the US economy. In the neoclassical growth model, regions and countries are assumed to be independent from each other, which may not be valid in the real world. We introduce technological spillovers into the neoclassical framework, showing that the convergence rate is higher and there is spatial interaction. By examing annual data on personal state income spanning the period of 1930–2006 for the 48 contiguous states, we obtain empirical results that are consistent with the theoretical prediction.

Estimation of Spatial Panels

Spatial panel models have panel data structures to capture spatial interactions across spatial units and over time. There are static as well as dynamic models. This text provides some recent developments on the specification and estimation of such models. The first part will consider estimation for static models. The second part is devoted to the estimation for spatial dynamic panels, where both stable and unstable dynamic models with fixed effects will be considered.