Well known encompassing tests are usually difficult to implement because it is difficult to compute the pseudo-true value of the quasi-maximum likelihood estimator. In this paper, we propose a more operational encompassing test that does not involve such pseudo-true value. Instead, the proposed test relies on the “pseudo-true score” which is relatively easier to evaluate. We show that this test...

This paper analyzes the application of the Markov-switching ARCH model (Hamilton and Susmel, 1994) in improving value-at-risk (VaR) forecast. By considering a mixture of normal distributions with varying variances over different time and regimes, we find that the “spurious high persistence” found in the GARCH model is adjusted. Under relative performance and hypothesis-testing evaluations, the ...

The use of log-transformed data has become standard in macroeconomic forecasting with VAR models. However, its appropriateness in the context of out-of-sample forecasts has not yet been exposed to a thorough empirical investigation. With the aim of filling this void, a broad sample of VAR models is employed in a multi-country setup and approximately 16 Mio. pseudo-out-of-sample forecasts are ev...

The study of the innovative output of firms often relies on a count of patents filed at one single office of reference such as the European Patent Office (EPO). Yet, not all firms file their patents at the EPO, raising the specter of a selection bias. Using a novel dataset of the whole population of patents by Belgian firms, we show that the singleoffice count results in a selection bias that a...

Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for di¤erent predictors to a¤ect di¤erent quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future in‡a...

This paper extends the distributional theory for the problem of testing for structural change in the linear model when the timing of the change is unknown, and proposes a simple method of obtaining approximate critical values for the mean-Wald test. The results apply for a very wide range of regressor types, including integrated and trending regressors, and regressors that exhibit their own str...

Unemployment in the OECD: Models and Mysteries This paper compares models used to explain OECD unemployment. The models suggest that the “natural rate of unemployment” has been driven up mainly by wage push factors. Panel data on twenty-two OECD countries are used to investigate the explanatory power of these models over the past two decades. Our estimates reveal that coefficients on key variab...

This chapter reviews the literature on variable selection in nonparametric and semiparametric regression models via shrinkage. We highlight recent developments on simultaneous variable selection and estimation through the methods of least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD) or their variants, but restrict our attention to nonparametric a...

This note conducts a simulation study of parameter-stability tests using conditional cointegrated vector autoregressive (CVAR) models. Monte Carlo experiments show that, in small samples, the stability tests based on conditional CVAR models under the assumption of weak exogeneity are more powerful than those based on a joint CVAR model; the reverse is observed, however, when the assumption does...

We propose a methodology for constructing confidence regions with partially identified models of general form. The region is obtained by inverting a test of internal consistency of the econometric structure. We develop a dilation bootstrap methodology to deal with sampling uncertainty without reference to the hypothesized economic structure, and apply a duality principle to reduce the dimension...