Powerful and Serial Correlation Robust Tests of the Economic Convergence Hypothesis

In this paper, a likelihood ratio approach is taken to derive a test of the economic convergence hypothesis in the context of the linear deterministic trend model. The test is designed to directly address the nonstandard nature of the hypothesis, and is a systematic improvement over existing methods for testing convergence in the same context. The test is first derived under the assumption of Gaussian errors with known serial correlation. However, the normality assumption is then relaxed, and the results are naturally extended to the case of covariance stationary errors with unknown serial correlation. The test statistic is a continuous function of individual t-statistics on the intercept and slope parameters of the linear deterministic trend model, and therefore, standard heteroskedasticity and autocorrelation consistent estimators of the long-run variance can be directly implemented. Building upon the likelihood ratio framework, concrete and specific tests are recommended to be used in practice. The recommended tests do not require the knowledge of the form of serial correlation in the data, and they are robust to highly persistent serial correlation, including the case of a unit root in the errors. The recommended tests utilize the nonparametric kernel variance estimators, which are analyzed using the fixed bandwidth (fixed-b) asymptotic framework recently proposed by Kiefer and Vogelsang (2003). The fixed-b framework makes possible the choice of kernel and bandwidth that deliver tests with maximal asymptotic power within a specific class of tests. It is shown that when the Daniell kernel variance estimator is implemented with specific bandwidth choices, the recommended tests have asymptotic power close that of the known variance case, as well as good finite sample size and power properties. Finally, the newly developed tests are used to investigate economic convergence among eight regions of the United States (as defined by the Bureau of Economic Analysis) in the post-World-War-II period. Empirical evidence is found for convergence in three of the eight regions.Likelihood Ratio, Joint Inequality, HAC Estimator, Fixed-b Asymptotics, Power Envelope, Unit Root, Linear Trend, BEA Regions.

Powerful and Serial Correlation Robust Tests of the Economic Convergence Hypothesis

In this paper, a likelihood ratio approach is taken to derive a test of the economic convergence hypothesis in the context of the linear deterministic trend model. The test is designed to directly address the nonstandard nature of the hypothesis, and is a systematic improvement over existing methods for testing convergence in the same context. The test is first derived under the assumption of Gaussian errors with known serial correlation. However, the normality assumption is then relaxed, and the results are naturally extended to the case of covariance stationary errors with unknown serial correlation. The test statistic is a continuous function of individual t-statistics on the intercept and slope parameters of the linear deterministic trend model, and therefore, standard heteroskedasticity and autocorrelation consistent estimators of the long-run variance can be directly implemented. Building upon the likelihood ratio framework, concrete and specific tests are recommended to be used in practice. The recommended tests do not require the knowledge of the form of serial correlation in the data, and they are robust to highly persistent serial correlation, including the case of a unit root in the errors. The recommended tests utilize the nonparametric kernel variance estimators, which are analyzed using the fixed bandwidth (fixed-b) asymptotic framework recently proposed by Kiefer and Vogelsang (2003). The fixed-b framework makes possible the choice of kernel and bandwidth that deliver tests with maximal asymptotic power within a specific class of tests. It is shown that when the Daniell kernel variance estimator is implemented with specific bandwidth choices, the recommended tests have asymptotic power close that of the known variance case, as well as good finite sample size and power properties. Finally, the newly developed tests are used to investigate economic convergence among eight regions of the United States (as defined by the Bureau of Economic Analysis) in the post-World-War-II period. Empirical evidence is found for convergence in three of the eight regions.Likelihood Ratio, Economic Convergence, ?-convergence Hypothesis, Joint Inequality, HAC Estimator, Fixed-b Asymptotics, Power Envelope, Unit Root, Linear Trend, BEA Regions.

Powerful Tests of Structural Change That are Robust to Strong Serial Correlation

This paper proposes powerful and serial correlation robust test statistics that can be used to test for the presence of structural change in the trend function of a univariate time series. Four models are analyzed, each model corresponding to a different way in which a trend break might occur. Given a model, the proposed tests are designed to detect a single break at an unknown date. The tests do not require the knowledge of the form of serial correlation in the data, and they are made robust to the presence of highly persistent serial correlation and a unit root in the errors by using a more comprehensive version of the scaling factor approach of Vogelsang (1998b). The tests utilize the popular nonparametric kernel variance estimators. The fixed-bandwidth asymptotic framework, proposed by Kiefer and Vogelsang (2003), is used to approximate the effects of the variance estimators on the test statistics. The fixed-bandwidth framework makes possible the choice of kernel and bandwidth that deliver tests with maximal asymptotic power within a specific class of tests. For each of the proposed tests, concrete and specific recommendations are made for the bandwidth and kernel to be used in practice. The recommended tests are shown to have good finite sample size and power properties.

Cigarette Demand, Taxation, and the Poor: A Case Study of Bulgaria

This paper usesthe Living Standards Measurement Study (LSMS) Household Survey, conducted in 1995, to analyze demand for cigarettes in Bulgaria and the effects on the poor of a hypothetical revenue-maximizing specific excise tax increase levied on cigarettes. One important finding is that the tax increase has a beneficial effect on the poor, in the sense that the tax burden on the low and lower-middle group is reduced