Macroeconomic Forecasting and Structural Change

By Antonello D’Agostino, Luca Gambetti, Domenico Giannone. Social Science Research Network, 2010

362 citations recorded by the source corpus.

The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coefficients VAR with Stochastic Volatility (TV-VAR) for the US. The model generates accurate predictions for the three variables. In particular for inflation the TV-VAR outperforms, in terms of mean square forecast error, all the competing models: fixed coefficients VARs, Time-Varying ARs and the naive random walk model. These results are also shown to hold over the most recent period in which it has been hard to forecast inflation.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/dd83e6c0a42c48b19791021327893878ea81f491

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Citations in the corpus

Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts (2014, background): “A range of studies have considered similar variable sets (e.g., Clark 2011; D’Agostino, Gambetti, and Giannone 2013).”
Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy (2011, positive): “D’Agostino et al. (2008) show the superior forecasting performance of the TVP-VAR specification over other VAR models using US macroeconomic data.”
On the effects of world stock market and oil price shocks on food prices: An empirical investigation based on TVP-VAR models with stochastic volatility (2014, background): “…among the variables which can be due to variations in the structural dynamic interrelations among macroeconomic variables and heteroskedasticity of the innovations which can be due to changes in the size of exogenous shocks or their impact on macroeconomic variables (D’Agostino et al., 2013).”
Large time-varying parameter VARs (2013, positive): “Even the small (trivariate) TVP-VAR recursive forecasting exercises of D’Agostino et al. (2011) and Korobilis (2013) were hugely computationally demanding. Forecasting with large TVP-VARs is typically, in practice, computationally infeasible using MCMC methods. A first contribution of this paper is ”