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In this paper, we propose a time-varying parameter vector autoregression (VAR) model with stochastic volatility which allows for estimation on data sampled at different frequencies. Our contribution is twofold. First, we extend the methodology developed by Cogley and Sargent (Drifts and volatilities: monetary policies and outcomes in the post WWII U.S. Review of Economic Studies 2005; 8: 262-302) and Primiceri (Time varying structural vector autoregressions and monetary policy. Review of Economic Studies 2005; 72: 821-852) to a mixed-frequency setting. In particular, our approach allows for the inclusion of two different categories of variables (high-frequency and low-frequency) into the same time-varying model. Second, we use this model to study the macroeconomic effects of government spending shocks in Italy over the 1988:Q4-2013:Q3 period. Italy-as well as most other euro area economies-is characterized by short quarterly time series for fiscal variables, whereas annual data are generally available for a longer sample before 1999. Our results show that the proposed time-varying mixed-frequency model improves on the performance of a simple linear interpolation model in generating the true path of the missing observations. Second, our empirical analysis suggests that government spending shocks tend to have positive effects on output in Italy. The fiscal multiplier, which is maximized at the 1-year horizon, follows a U-shape over the sample considered: it peaks at around 1.5 at the beginning of the sample; it then stabilizes between 0.8 and 0.9 from the mid 1990s to the late 2000s, before rising again to above unity during the recent crisis. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real-time out-of-sample exercise using a time-varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time-varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This article compares the predictive ability of the factor models of and using a 'large' panel of macroeconomic variables of the United States. We propose a nesting procedure of comparison that clarifies and partially overturns the results of similar exercises in the literature. Our main conclusion is that with the dataset at hand the two methods have a similar performance and produce highly collinear forecasts. [ABSTRACT FROM AUTHOR]

We construct a measure of global liquidity using the growth rates of broad money for the G7 economies. Global liquidity produces forecasts of U.S. inflation that are significantly more accurate than the forecasts based on U.S. money growth, Phillips curve, and autoregressive and moving average models. The marginal predictive power of global liquidity is strong at 3-year horizons. Results are robust to alternative measures of inflation. [ABSTRACT FROM AUTHOR]