This paper considers a multivariate extension of the test for neglected nonlinearity proposed by Tsay (1986) that uses principal components to overcome the problem of dimensionality that is common with tests of this type. Monte Carlo experiments reveal that the modified multivariate test...

We investigate the time series properties of both filtered and unfiltered real exchange rate series produced by DSGE models that feature local currency pricing, home bias, nontraded goods, and incomplete markets. Detrended series produced by several specifications approach the empirically...

This paper suggests using a unit t-value criterion in imposing restrictions on lags to formulate a subset vector autoregressive (VAR) model for the purpose of point forecasts. Among any other alternative models nested to the initial VAR model, this less restrictive modeling strategy produces the...

We examine the efficiency of German forecasts for output growth and inflation allowing for an asymmetric loss function of the forecasters. We find the loss of output growth forecasts to be approximately symmetric while there is an asymmetry in the loss of the inflation forecasts. The information...

We transpose the Generalized Impulse-Response Function (GIRF) developed by Koop et al. (1996) to Markov-Switching structural VARs. As the algorithm displays an exponentially increasing complexity as regards the prediction horizon, we use the collapsing technique to easily obtain simulated...

A new method of assessing the comparative quality of forecasting models is introduced. This method focuses on the quality of forecasting models over a set of series (cf. the traditionally adopted series-by-series approach)–with a forecasting model that produces good forecasts over a series set...

This note shows how conditional forecasts from identified VAR models can be computed using Kalman filtering techniques. These techniques are nowadays routine for applied macroeconomists, and hence the computation of conditional forecasts using these methods are simple to implement.

There is a growing literature on the realized volatility (RV) forecasting of asset returns using high-frequency data. We explore the possibility of forecasting RV with factor analysis; once considering the significant jumps. A real high-frequency financial data application suggests that the...

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

In this study, we determine the reliability and exogeneity of four popular monetary policy shock measures, namely the narrative series of Romer and Romer (2004), the high-frequency series of Barakchian and Crowe (2013), the high-frequency series of Gertler and Karadi (2015), and the hybrid...