Various inflation forecasting models are compared using a simulated out-of-sample forecasting framework. We focus on the question of whether monetary aggregates are useful for forecasting inflation, but unlike previous work we examine a wide range of forecast horizons and allow for estimated as...

Various inflation forecasting models are compared using a simulated out-of-sample forecasting framework. We focus on the question of whether monetary aggregates are useful for forecasting inflation, but unlike previous work we examine a wide range of forecast horizons and allow for estimated as...

Various inflation forecasting models are compared for the period 1979--2003 using a simulated out-of-sample forecasting framework. Our findings are (1) M2 has marginal predictive content for inflation; (2) it is necessary to allow for the possibility that money, prices, and output are...

In the conduct of empirical macroeconomic research, unit root, cointegration, common cycle, and related test statistics are often constructed using logged data, even though there is often no clear reason, at least from an empirical perspective, why logs should be used rather than levels....

This paper introduces bootstrap specification tests for diffusion processes. In the one-dimensional case, the proposed test is closest to the non parametric test introduced by Ait-Sahalia (1996), in the sense that both procedures determine whether the drift and variance components of a...

In this paper, we show the first order validity of the block bootstrap in the context of Kolmogorov type conditional distribution tests when there is dynamic misspecification and parameter estimation error. Our approach di®ers from the literature to date because we construct a bootstrap...

We perform a series of Monte Carlo experiments in order to evaluate the impact of data transformation on forecasting models, and find that vector error-corrections dominate differenced data vector autoregressions when the correct data transformation is used, but not when data are incorrectly...

We perform a series of Monte Carlo experiments in order to evaluate the impact of data transformation on forecasting models, and find that vector error-corrections dominate differenced data vector autoregressions when the correct data transformation is used, but not when data are incorrectly...

We make use of the extant testing methodology of Barndorff-Nielsen and Shephard (2006) and Aït-Sahalia and Jacod (2009a,b,c) to examine the importance of jumps, and in particular “large" and “small" jumps, using high frequency price returns on 25 stocks in the DOW 30 and S&P futures index....

In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned...