Transition Modeling and Econometric Convergence Tests

TLDR

The study introduces a flexible panel data model for economies in transition that captures diverse time paths and individual heterogeneity, and establishes asymptotic representations to support econometric estimation and testing. The authors formulate a nonlinear time‑varying factor model with common and individual components, derive a regression‑based convergence test and a club‑convergence clustering method, and apply these tools to analyze cost‑of‑living convergence among 19 U.S.

Abstract

A new panel data model is proposed to represent the behavior of economies in transition allowing for a wide range of possible time paths and individual heterogeneity. The model has both common and individual specific components and is formulated as a nonlinear time varying factor model. When applied to a micro panel, the decomposition provides flexibility in idiosyncratic behavior over time and across section, while retaining some commonality across the panel by means of an unknown common growth component. This commonality means that when the heterogeneous time varying idiosyncratic components converge over time to a constant, a form of panel convergence holds, analogous to the concept of conditional sigma convergence. The paper provides a framework of asymptotic representations for the factor components which enables the development of econometric procedures of estimation and testing. In particular, a simple regression based convergence test is developed, whose asymptotic properties are analyzed under both null and local alternatives, and a new method of clustering panels into club convergence groups is constructed. These econometric methods are applied to analyze convergence in cost of living indices among 19 US. metropolitan cities.