James Morley (Department of Economics Washington University in St. Louis) Tara M. Sinclair () (Department of Economics The George Washington University)
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We compare the finite-sample performance of different stationarity tests. Monte Carlo analysis reveals that tests based on Lagrange multiplier (LM) statistics with nonstandard asymptotic distributions reject far more often than their nominal size for trend-stationary processes of the kind estimated for macroeconomic data. Bootstrap versions of these LM tests have empirical rejection probabilities that are closer to nominal size, but they still tend to over-reject. Meanwhile, we find that a bootstrap likelihood ratio (LR) test has very accurate finite-sample size, while at the same time having higher power than the bootstrap LM tests against empiricallyrelevant nonstationary alternatives. Based on the bootstrap LR test, and in some cases contrary to the bootstrap LM tests, we can reject trend stationarity for US real GDP, the unemployment rate, consumer prices, and payroll employment in favour of unit root processes with large permanent movements.
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Paper provided by The George Washinton University, Department of Economics, Research Program on Forecasting in its series Working Papers with number
2008-11.