`Weak` trends for inference and forecasting in finite samples. by Chevillon, G

“…In this framework, parameter estimates, unit-root test statistics and forecast errors are functions of ‘drifting’ Ornstein-Uhlenbeck processes. …”

Weak' trends for inference and forecasting in finite samples by Chevillon, G

“…In this framework, parameter estimates, unit-root test statistics and forecast errors are functions of 'drifting' Ornstein-Uhlenbeck processes. …”

A Comparison of Multi-step GDP Forecasts for South Africa. by Chevillon, G

“…It is known that structural breaks, unit-root non-stationarity and residual autocorrelation benefit DMS accuracy in finite samples, all of which occuring when modelling the South African GDP over the last thirty years. …”

Multi-step forecasting in unstable economies: robustness issues in the presence of location shifts by Chevillon, G

“…To forecast at several, say h, periods into the future, a modeller faces two techniques: iterating one-step ahead forecasts (the IMS technique) or directly modelling the relation between observations separated by an h-period interval and using it for forecasting (DMS forecasting). It is known that unit-root non-stationarity and residual autocorrelation benefit DMS accuracy in finite samples. …”

Multi-step Forecasting in Unstable Economies: Robustness Issues in the Presence of Location Shifts by Chevillon, G

“…To forecast at several, say h, periods into the future, a modeller faces two techniques: iterating onestep ahead forecasts (the IMS technique) or directly modelling the relation between observations separated by an h-period interval and using it for forecasting (DMS forecasting). It is known that unit-root non-stationarity and residual autocorrelation benefit DMS accuracy in finite samples. …”

Robust inference in structural vector autoregressions with long-run restrictions by Chevillon, G, Mavroeidis, S, Zhan, Z

“…Long-run restrictions are a very popular method for identifying structural vector autoregressions, but they suffer from weak identification when the data is very persistent, i.e., when the highest autoregressive roots are near unity. Near unit roots introduce additional nuisance parameters and make standard weak-instrument-robust methods of inference inapplicable. …”