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Of necessity, many tests for political influence on policies or outcomes involve the use of dummy variables. However, it is often the case that the hypothesis against which the political dummies are tested is the null hypothesis that the intercept is otherwise constant throughout the sample. This simple null can cause inference problems if there are (nonpolitical) intercept shifts in the data and the political dummies are correlated with these unmodeled shifts. Here we present a method for more rigorously testing the significance of political dummy variables in single equation models estimated with time series data. Our method is based on recent work on detecting multiple regime shifts by Bai and Perron. The article illustrates the potential problem caused by an overly simple null hypothesis, exposits the Bai and Perron model, gives a proposed methodology for testing the significance of political dummy variables, and illustrates the method with two examples. Before the curse of statistics fell upon mankind we lived a happy, innocent life—Hilaire Belloc, On Statistics

Although previous comparisons of log-linear techniques with the regression analysis of dummy dependent variables have focused on the statistical superiority of the log-linear techniques, this paper presents three advantages of dummy dependent-variable regressions. First, dummy dependent-variable regression is able to accommodate both categorical and continuous independent variables. Second, the calculation of indirect effects and reciprocal effects is possible only with dummy dependent-variable regression. Third, the slopes yielded by dummy dependent-variable regression possess the properties of fundamental parameters; the effect parameters of log-linear models do not. In particular, controlling for a variable that is irrelevant to the causal system under investigation can yield a partial relationship whose expected value differs from that of the zero-order relationship when using log-linear techniques.

This paper provides a method for testing for regime differences when regimes are long-lasting. Standard testing procedures are generally inappropriate because regime persistence causes a spurious regression problem - a problem that has led to incorrect inference in a broad range of studies involving regimes representing political, business, and seasonal cycles. The paper outlines analytically how standard estimators can be adjusted for regime dummy variable persistence. While the adjustments are helpful asymptotically, spurious regression remains a problem in small samples and must be addressed using simulation or bootstrap procedures. We provide a simulation procedure for testing hypotheses in situations where an independent variable in a time-series regression is a persistent regime dummy variable. We also develop a procedure for testing hypotheses in situations where the dependent variable has similar properties.

This paper extends the work on challenger spending in congressional elections to allow for the possibility that such spending has varying effects, depending upon circumstances. Using data from the 1988 and 1990 U.S. House elections, it begins with a baseline model for the incumbent's margin. Pairs of dummy variables and interaction terms were then added to test several hypotheses. The results provide some evidence to suggest that challenger spending is less effective if the challenger faced a toughprimary. Contrary to previous work, an advantage was found for repeat challengers. Also contrary to previous work, the results do not suggest that high‐quality challengers spend their money more efficiently than do other challengers.

Reasonably assessing the effectiveness of government expenditure on the Grain for Green project (GFG) in providing forest carbon sequestration would contribute to the development of China&rsquo ; s forest carbon sequestration. Using the government expenditure data from the GFG in Yunnan Province from 2001 to 2015 and the MODIS Land Cover Type (MCD12Q1) time-series datasets, we calculated the forest carbon sequestration of various counties (cities or districts). The impacts of GFG government expenditure on forest carbon sequestration were empirically evaluated by the least squares dummy variables method (LSDV). The research results indicate that a 1% increase in government expenditure on the GFG yielded a 0.0364% increase in forest carbon sequestration. However, the effects of GFG government expenditure on forest carbon sequestration differed greatly in different areas because of the diversity of the natural environments, forest resource endowment, and government policies. If the initial forest endowment was not considered, the effectiveness of government expenditure on the GFG in providing forest carbon sequestration would have been overestimated. This study argues that, to improve the efficiency of GFG government expenditure in Yunnan Province, more investment should be made in regions with positive regression coefficients that have passed the significance t-test, such as Diqing Tibetan Autonomous Prefecture in the northwest, Baoshan City in the west, Honghe Hani and Yi Autonomous Prefecture in the south, and Wenshan Zhuang and Miao Autonomous Prefecture in the east.