In this paper we examine the interaction between data transformation and the empirical evidence obtained when testing for (non-)linearity. For this purpose we examine nonlinear features in 64 monthly and 53 quarterly US macroeconomic variables for a range of Box-Cox data transformations. Our...

In this paper, we investigate the magnitude of errors that come about as a consequence of using a linear approximation to a well-known optimising model. We do this by taking a calibrated version of the neoclassical adjustment-cost model of investment due to Hayashi (1982).

The presence of nuisance parameters causes unwanted complications in statistical and econometric inference procedures. A number of modified likelihood and message length functions have been developed for better handling of nuisance parameters but they are not equally efficient. In this paper, we...

Part of conventional wisdom glaned from econometric theory and the "learning" literature is that agents ahould use all the data they have for prediction. In this paper it is shown that agents can improve their prediction by throwing away data.

We address the issue of time varying persistence of shocks to macroeconomic time series variables by proposing a new and parsimonious time series model. Our model assumes that this time varying persistence depends on a linear combination of lagged explanatory variables, where this combination...

In this article we develop a new test procedure for testing a linear hypothesis in a fixed effects linear model with heteroscedastic errors. This test is based on the ordinary least squares estimator (OLSE) of the regression parameter and uses the variance estimator of OLSE that accounts for...

In this paper we discuss two methods for the estimation of linear dynamic factor models. The first method is behavioural in nature and consists of the least squares approximation of the observed data by means of a linear system. The second method is based on the statistical concept of principal...

In economics density forecasts are rarely available, and as a result attention has traditionally focused on poit forecasts of the mean and the use of mean square error statistics to represent the loss function. We extend the methods of forecasts density evaluation in Diebold, Gunther and Tay...