We discuss a method to estimate the confidence bounds for average economic growth, which is robust to misspecification of the unit root property of a given time series. We derive asymptotic theory for the consequences of such misspecification. Our empirical method amounts to an implementation of the subsampling procedure advocated in Romano and Wolf (Econometrica, 2001, Vol. 69, p. 1283). Simulation evidence supports the theory and it also indicates the practical relevance of the subsampling method. We use quarterly postwar US industrial production for illustration and we show that non-robust approaches rather lead to different conclusions on average economic growth than our robust approach.

Additional Metadata
Keywords growth, misspecification Robust testing, unit root
JEL Simulation Methods; Monte Carlo Methods; Bootstrap Methods (jel C15), Time-Series Models; Dynamic Quantile Regressions (jel C22), Measurement of Economic Growth; Aggregate Productivity (jel O47)
Persistent URL dx.doi.org/10.1111/j.1468-0084.2006.00165.x, hdl.handle.net/1765/13349
Series Econometric Institute Reprint Series
Journal Oxford Bulletin of Economics and Statistics
Citation
Boswijk, H.P, & Franses, Ph.H.B.F. (2006). Robust Inference on Average Economic Growth. Oxford Bulletin of Economics and Statistics (Vol. 68, pp. 345–370). doi:10.1111/j.1468-0084.2006.00165.x