Interpreting Tests of the Convergence Hypothesis
为理解检验收敛假说的横截面和时间序列方法提供了分析框架，区分了两种收敛定义及其与检验方法的关系，并指出方法选择取决于假设和数据初始条件。
This paper provides a framework for understanding the cross- section and time series approaches which have been used to test the convergence hypothesis. First, we present two definitions of convergence which capture the implications of the neoclassical growth model for the relationship between current and future cross-country output differences. Second, we identify how the cross-section and time series approaches relate to these definitions. Cross-section tests are shown to be associated with a weaker notion of convergence than time series tests. Third, we show how these alternative approaches make different assumptions on whether the data are well characterized by a limiting distribution. As a result, the choice of an appropriate testing framework is shown to depend on both the specific null and alternative hypotheses under consideration as well as on the initial conditions characterizing the data being studied.