It is common in empirical macroeconomics to fit vector autoregressive (VAR) models to construct estimates of impulse responses. An important preliminary step in impulse response analysis is the selection of the VAR lag order. In this paper, we compare the six lag-order selection criteria most...

In this paper we study various methods for detecting the co integrating rank as the number of variables gets large. We show that the use of standard tools will always lead to misleading inferences in such settings due to excessive size distortions. Particularly the LR test tends to produce too...

The purpose of the paper is to present and apply the accumulative one-step-ahead prediction error (APE) not only as a method (strategy) of model selection, but also as a tool of model selection strategy (meta-selection). The APE method is compared with the information approach to model selection...

This paper compares the performance of using an information criterion, such as the Akaike information criterion or the Schwarz (Bayesian) information criterion, rather than hypothesis testing in consideration of the presence of a unit root for a variable and, if unknown, the presence of a trend...

It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean squared error (PMSE) in simulated out-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using information criteria (IC). We compare the asymptotic and...

Automated model searches using information criteria are used for the estimation of linear single equation models. Genetic algorithms are described and used for this purpose. These algorithms are shown to be a practical method for model selection when the number of sub-models are very large....

Simple forecast combinations such as medians and trimmed or winsorized means are known to improve the accuracy of point forecasts, and Akaike’s Information Criterion (AIC) has given rise to so-called Akaike weights, which have been used successfully to combine statistical models for inference...

Reduced rank regression (RRR) models with time varying heterogeneity are considered. Standard information criteria for selecting cointegrating rank are shown to be weakly consistent in semiparametric RRR models in which the errors have general nonparametric short memory components and shifting...

Artificial neural networks (ANNs) have been successfully used for solving variety of problems. One major disadvantage of ANNs is that there is no formal systematic model building approach. This paper presents the application of the Taguchi method in the optimization of the design parameters of...