How Smart Is my Dummy? Time Series Tests for the Influence of Politics

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

[1]  P. Perron,et al.  Estimating and testing linear models with multiple structural changes , 1995 .

[2]  Nathaniel N. Beck.  Presidential Influence on the Federal Reserve in the 1970’s , 1982 .

[3]  P. Perron,et al.  Computation and Analysis of Multiple Structural-Change Models , 1998 .

[4]  J. Bai,et al.  Estimating Multiple Breaks One at a Time , 1997, Econometric Theory.

[5]  B. Hansen.  The New Econometrics of Structural Change: Dating Breaks in U.S. Labour Productivity , 2001 .

[6]  D. Hakes.  The Objectives and Priorities of Monetary Policy under Different Federal Reserve Chairmen , 1990 .

[7]  W. Newey,et al.  A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix , 1986 .

[8]  D. Andrews,et al.  Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis , 1992 .

[9]  D. Andrews.  Tests for Parameter Instability and Structural Change with Unknown Change Point , 1993 .

[10]  G. Chow.  Tests of equality between sets of coefficients in two linear regressions (econometrics voi 28 , 1960 .

[11]  René Garcia,et al.  Série Scientifique Scientific Series an Analysis of the Real Interest Rate under Regime Shifts , 2022 .

[12]  Douglas A. Hibbs,et al.  Political Parties and Macroeconomic Policy , 1977, American Political Science Review.

[13]  J. Sachs,et al.  Political Parties and the Business Cycle in the United States, 1948-1984 , 1986 .

[14]  J. MacKinnon,et al.  Several Tests for Model Specication in the Pres-ence of Alternative Hypotheses , 1981 .

[15]  P. Perron,et al.  Further Evidence on Breaking Trend Functions in Macroeconomic Variables. , 1997 .

[16]  D. Andrews.  Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation , 1991 .

[17]  K. Grier.  Congressional influence on U.S. monetary policy: An empirical test☆ , 1991 .

[18]  D. Andrews.  Tests for Parameter Instability and Structural Change with Unknown Change Point , 1993 .

[19]  P. Perron,et al.  The Great Crash, The Oil Price Shock And The Unit Root Hypothesis , 1989 .

[20]  P. Perron,et al.  Critical Values for Multiple Structural Change Tests , 2003 .

[21]  J. Durbin,et al.  Techniques for Testing the Constancy of Regression Relationships Over Time , 1975 .

[22]  George A. Krause.  Federal Reserve Policy Decision Making: Political and Bureaucratic Influences , 1994 .

[23]  K. Grier,et al.  Political Regime Change and the Real Interest Rate , 2000 .