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.

growth, misspecification Robust testing, unit root
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)
dx.doi.org/10.1111/j.1468-0084.2006.00165.x, hdl.handle.net/1765/13349
Econometric Institute Reprint Series
Oxford Bulletin of Economics and Statistics
Erasmus Research Institute of Management

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