This paper develops a new approach to the problem of testing the existence of a level relationship between a dependent variable and a set of regressors, when it is not known with certainty whether the underlying regressors are trend- or first-difference stationary. The proposed tests are based...

This paper considers estimation and inference in panel vector autoregressions with fixed effects when the time dimension of the panel is finite, and the cross-sectional dimension is large. A Maximum Likelihood estimator based on a transformed likelihood function is proposed and shown to be...

This paper addresses two related issues in the literature of non-nested hypotheses testing. Firstly, by means of a measure of “closeness” of probability density functions, it shows how <italic>any</italic> two hypotheses can be placed into the nested and the non-nested categories with the latter category...

The paper develops a general framework for identification, estimation, and hypothesis testing in cointegrated systems when the cointegrating coefficients are subject to (possibly) non-linear and cross-equation restrictions, obtained from economic theory or other relevant a priori information. It...

This paper discusses the ?structural cointegrating VAR? approach to macroeconometric modelling and compares it to other approaches currently followed in the literature, namely, the large-scale simultaneous equation macroeconometric models, the structural VARs, and the dynamic stochastic general...

The standard derivation of a Phillips curve from a DSGE model requires that all variables are measured as deviations from their steady states. But in practice this is not done. The steady state for output is estimated by some statistical procedure, such as the HP filter, and the steady state for...

We investigate the effect of forecast uncertainty in a cointegrating vector error correction model for Switzerland. Forecast uncertainty is evaluated in three different dimensions. First, we investigate the effect on forecasting performance of averaging over forecasts from different models....