In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

This paper proposes a test for missing at random (MAR). The MAR assumption is shown to be testable given instrumental variables which are independent of response given potential outcomes. A nonparametric testing procedure based on integrated squared distance is proposed. The statistic's...

Virtually each seasonal adjustment software includes an ensemble of seasonality tests for assessing whether a given time series is in fact a candidate for seasonal adjustment. However, such tests are certain to produce either the same resultor conflicting results, raising the question if there...

There has been increased interest in the use of "big data" when it comes to forecasting macroeconomic time series such as private consumption or unemployment. However, applications on forecasting GDP are rather rare. In this paper we incorporate Google search data into a Bridge Equation Model, a...

Recent research provides controversial evidence on the stability of yield-curve based binary probit models for forecasting U.S. recessions. This paper reviews so far applied specifications and presents new procedures for examining the stability of selected probit models. It finds that a...

Various papers indicate that the yield-curve has superior predictive power for U.S. recessions. However, there is controversial evidence on the stability of the predictive relationship and it has remained unclear how the persistence of the underlying binary recession indicator should be taken...