Real-time inflation forecast combination for time-varying coefficient models

We use real-time macroeconomic variables and combination forecasts with both time-varying weights and equal weights to forecast inflation in the USA. The combination forecasts compare three sets of commonly used time-varying coefficient autoregressive models: Gaussian distributed errors, errors with stochastic volatility, and errors with moving average stochastic volatility. Both point forecasts and density forecasts suggest that models combined by equal weights do not produce worse forecasts than those with time-varying weights. We also find that variable selection, the allowance of time-varying lag length choice, and the stochastic volatility specification significantly improve forecast performance over standard benchmarks. Finally, when compared with the Survey of Professional Forecasters, the results of the best combination model are found to be highly competitive during the 2007/08 financial crisis.