We propose a general procedure for testing that a regression function has a prescribed parametric form. We allow for multivariate regressors, non-normal errors and heteroscedasticity of unknown form. The test relies upon a nonparametric linear estimation method, such as a sieves expansion or the...

We propose new data-driven smooth tests for a parametric regression function. The smoothing parameter is selected through a new criterion that favors a large smoothing parameter under the null hypothesis. The resulting test is adaptive rate-optimal and consistent against Pitman local...

The authors examine the issue of lag-length selection in the context of a structural vector autoregression (VAR) and a vector error-correction model with long-run restrictions. First, they show that imposing long- run restrictions implies, in general, a moving-average (MA) component in the...

In this paper the authors show how potential output can be estimated and projected through an approach derived from the structural vector autoregression methodology. This approach is applied to the Mexican economy. To identify demand, supply and world oil shocks, the authors assume that demand...