This paper presents one of the inflation forecasting models used by the Magyar Nemzeti Bank in its recent inflation forecasts. The model attempts to integrate all the properties of the former models considered by the author as being advantageous and desirable into a unified framework. Thus, this...

Experts can rely on statistical model forecasts when creating their own forecasts. Usually it is not known what experts actually do. In this paper we focus on three questions, which we try to answer given the availability of expert forecasts and model forecasts. First, is the expert forecast...

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample...

We provide a formulation of stochastic volatility (SV) based on Gaussian process regression (GPR). Forecasting volatility out-of-sample, both simulation and empirical analyses show that our GPR-based stochastic volatility (GPSV) model clearly outperforms SV and GARCH benchmarks, especially at...

We document five novel empirical findings on the well-known potential ordering drawback associated with the time-varying parameter vector autoregression with stochastic volatility developed by Cogley and Sargent (2005) and Primiceri (2005), CSP-SV. First, the ordering does not affect point...

This paper uses a simple New Keynesian monetary DSGE model as a prior for a vector autoregression and shows that the resulting model is competitive with standard benchmarks in terms of forecasting and can be used for policy analysis

This paper presents a new Bayesian methodology for predicting a turning point in an economic system. The methodology utilizes information-theoretic measurements for assessing likelihood functions for a turning point. This methodology shows that the total information of a likelihood function...

Recent studies have showed that it is troublesome, in practice, to distinguish between long memory and nonlinear processes. Therefore, it is of obvious interest to try to capture both features of long memory and non-linearity into a single time series model to be able to assess their relative...

Electricity demand is modeled as a time-varying parameters (TVP) vector autoegression with or without imposing cointegration. The paper applies Bayesian strategies where all or a part of the parameters are allowed to vary, and compares their forecasts performances with alternative time series...

We examine the importance of incorporating macroeconomic information and, in particular, accounting for model uncertainty when forecasting the term structure of U.S. interest rates. We start off by analyzing and comparing the forecast performance of several individual term structure models. Our...