This note shows how conditional forecasts from identified VAR models can be computed using Kalman filtering techniques. These techniques are nowadays routine for applied macroeconomists, and hence the computation of conditional forecasts using these methods are simple to implement.

The present paper suggests a new way to carry out IV estimation with many instruments. Our suggestion is to cross-sectionally average the instruments and use these averages as instruments. We provide a theoretical and Monte Carlo analysis of this approach.

We extend GLS detrending procedure to testing for unit roots against STAR and SETAR alternatives. Monte Carlo simulations and applications to DM/Yen real exchange rates demonstrate that GLS detrending-based nonlinear unit root tests are more powerful than OLS detrending-based counterparts.