Jacopo Cimadomo
Antonello D'Agostino

combining time variation and mixed frequencies: an analysis of government spending multipliers in italy (replication data)

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.

Data and Resources

Suggested Citation

Cimadomo, Jacopo; D'Agostino, Antonello (2016): Combining Time Variation and Mixed Frequencies: an Analysis of Government Spending Multipliers in Italy (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022326.0700830442