Econometric Analysis of Panel Data Models with Multifactor Error Structures

Economic panel data often exhibit cross-sectional dependence, even after conditioning on appropriate explanatory variables. Two approaches to modeling cross-sectional dependence in economic panel data are often used: the spatial dependence approach, which explains cross-sectional dependence in terms of distance among units, and the residual multifactor approach, which explains cross-sectional dependence by common factors that affect individuals to a different extent. This article reviews the theory on estimation and statistical inference for stationary and nonstationary panel data with cross-sectional dependence, particularly for models with a multifactor error structure. Tests and diagnostics for testing for unit roots, slope homogeneity, cointegration, and the number of factors are provided. We discuss issues such as estimating common factors, dealing with parameter plethora in practice, testing for structural stability and nonlinearity, and dealing with model and parameter uncertainty. Finally, we address issues related to the use of these economic panel models.

PETER C.B. PHILLIPS’S CONTRIBUTIONS TO PANEL DATA METHODS

This paper discusses Peter Phillips’s contributions to panel data methods. These include contributions in the areas of seemingly unrelated regressions, nonstationary panel data, dynamic panels, and the development of multiple index asymptotic theory. We also discuss his empirical contributions in the area of economic growth and convergence that use macro panel data.

A NOTE ON THE POOLING OF INDIVIDUAL PANIC UNIT ROOT TESTS

One of the most cited studies in recent years within the field of nonstationary panel data analysis is that of Bai and Ng (2004), in which the authors propose PANIC, a new framework for analyzing the nonstationarity of panels with idiosyncratic and common components. The problem is that the asymptotic validity of PANIC as a platform for constructing pooled panel unit root tests based on averaging is not fully proven. This paper provides the required results, whose usefulness is verified through simulations.

An Examination of Exchange Rate Pass-Through to U.S. Motor Vehicle Products and Auto-Parts Import Prices

A distinctive feature of present globalization is the development of international production sharing activities (i.e. production fragmentation). The recent developments in transportation and communication technologies led to a surge in intermediate goods trade. However, intermediate goods trade is often neglected in the empirical studies of the exchange rate pass-through (ERPT). Using import unit values of 79 motor vehicle products and 245 auto-part products, which are classified by the 10-digit level of Harmonized Tariff Schedule (HTS), this study examines the pass-through of exchange rate changes into the U.S. auto-industry import prices from 5 major trading partners for the period of 1998.01 to 2006.12. Nonstationary panel data estimation techniques and tests for cointegration are employed in this study. Secondly, this study aims to compare the ERPT for the motor vehicle products (final goods) to the ERPT for the auto-parts (intermediate goods) in the U.S. The results suggest that import prices do not respond proportionately to the exchange rates and the estimated pass-through elasticities for motor vehicle products are lower than that for auto-parts.