We explore convenient analytic properties of distributions constructed as mixtures of scaled and shifted t-distributions. A feature that makes this family particularly desirable for econometric applications is that it possesses closed-form expressions for its anti-derivatives (e.g., the...

Davidson and MacKinnon (1981) proposed a simple procedure for testing the specification of a non-linear regression model against the evidence provided by a non-nested alternative. We extend their results in several directions. First, we relax a number of assumptions of the previous paper: We...

In this paper we utilize White's Reality Check bootstrap methodology (White (1999)) to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn. Hence, for the...

We take a model selection approach to the question of whether a class of adaptive prediction models (artificial neural networks) is useful for predicting future values of nine macroeconomic variables. We use a variety of out-of-sample forecast-based model selection criteria, including forecast...

We propose a new family of density functions that possess both flexibility and closed form expressions for moments and anti-derivatives, making them particularly appealing for applications. We illustrate its usefulness by applying our new family to obtain density forecasts of U.S. inflation. Our...

A correction on the optimal block size algorithms of Politis and White (2004) is given following a correction of Lahiri's (Lahiri 1999) theoretical results by Nordman (2008).

The authors consider tests for changing trend that do not require prior knowledge about the location of the changepoint. The limiting distribution is derived from the functional central limit theorem and the critical value from the hitting probability of a Brownian bridge. Applying a test...