This paper combines a data-rich environment with machine learning algorithm to provide new estimates of time-varying systematic expectational errors (“belief distortions”) embedded in survey responses. We find sizable distortions even for professional forecasters, all respondent-types overweighting the implicit judgmental component their forecasts relative what can be learned from publicly avai...

this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

Abstract The aim of the paper is to compare forecasting performance a class statedependent autoregressive (SDAR) models for univariate time series with two alternative families nonlinear models, such as SETAR and GARCH models. study conducted on US GDP growth rate using quarterly data. Two methods forecast comparison are employed. first method consists in evaluation average by measures root mea...

This paper aims at improved accuracy in testing for long-run predictability in noisy series, such as stock market returns. Long-horizon regressions have previously been the dominant approach in this area. We suggest an alternative method that yields more accurate results. We find evidence of predictability in S&P 500 returns even when the confidence intervals are constructed using model-free me...

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions ability to flexibly whole distribution inflation. In order make our approach accessible and relevant forecasting, we derive an efficient Gibbs sampler by transforming stat...

This paper brings together two important but hitherto largely unrelated areas of the forecasting literature, density forecasting and forecast combination. It proposes a simple data-driven approach to direct combination of density forecasts using optimal weights. These optimal weights are those weights that minimise the ‘distance’, as measured by the Kullback-Leibler Information Criterion, betwe...