Time filter

Source Type

Breitenfurt bei Wien, Austria

Posch J.,University of Florence | Rumler F.,Oesterreichische Nationalbank
Journal of Forecasting | Year: 2015

We develop a semi-structural model for forecasting inflation in the UK in which the New Keynesian Phillips curve (NKPC) is augmented with a time series model for marginal cost. By combining structural and time series elements we hope to reap the benefits of both approaches, namely the relatively better forecasting performance of time series models in the short run and a theory-consistent economic interpretation of the forecast coming from the structural model. In our model we consider the hybrid version of the NKPC and use an open-economy measure of marginal cost. The results suggest that our semi-structural model performs better than a random-walk forecast and most of the competing models (conventional time series models and strictly structural models) only in the short run (one quarter ahead) but it is outperformed by some of the competing models at medium and long forecast horizons (four and eight quarters ahead). In addition, the open-economy specification of our semi-structural model delivers more accurate forecasts than its closed-economy alternative at all horizons. © 2014 John Wiley and Sons, Ltd. Source

Feldkircher M.,Oesterreichische Nationalbank
Journal of Forecasting | Year: 2012

In this study we evaluate the forecast performance of model-averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold. First, the predictive likelihood does always better than the traditionally employed 'marginal' likelihood in settings where the true model is not part of the model space. Secondly, forecast accuracy as measured by the root mean square error (RMSE) is maximized for the median probability model. On the other hand, model averaging excels in predicting direction of changes. Lastly, g should be set according to Laud and Ibrahim (1995: Predictive model selection. Journal of the Royal Statistical Society B 57: 247-262) with a hold-out sample size of 25% to minimize the RMSE (median model) and 75% to optimize direction of change forecasts (model averaging). We finally apply the aforementioned recommendations to forecast the monthly industrial production output of six countries, beating for almost all countries the AR(1) benchmark model. Copyright © 2011 John Wiley & Sons, Ltd. Source

Kaufmann S.,Oesterreichische Nationalbank | Kugler P.,University of Basel
Journal of Forecasting | Year: 2010

Based on a vector error correction model we produce conditional euro area inflation forecasts. We use real-time data on M3 and HICP, and include real GPD, the 3-month EURIBOR and the 10-year government bond yield as control variables. Real money growth and the term spread enter the system as stationary linear combinations. Missing and outlying values are substituted by model-based estimates using all available data information. In general, the conditional inflation forecasts are consistent with the European Central Bank's assessment of liquidity conditions for future inflation prospects. The evaluation of inflation forecasts under different monetary scenarios reveals the importance of keeping track of money growth rate in particular at the end of 2005. Copyright © 2009 John Wiley & Sons, Ltd. Source

Zeugner S.,Roosevelt University | Feldkircher M.,Oesterreichische Nationalbank
Journal of Statistical Software | Year: 2015

This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian model averaging for linear regression models. The package excels in allowing for a variety of prior structures, among them the “binomial-beta” prior on the model space and the so-called “hyper-g” specifications for Zellner's g prior. Furthermore, the BMS package allows the user to specify her own model priors and offers a possibility of subjective inference by setting “prior inclusion probabilities” according to the researcher's beliefs. Furthermore, graphical analysis of results is provided by numerous built-in plot functions of posterior densities, predictive densities and graphical illustrations to compare results under different prior settings. Finally, the package provides full enumeration of the model space for small scale problems as well as two efficient MCMC (Markov chain Monte Carlo) samplers that sort through the model space when the number of potential covariates is large. © 2015, American Statistical Association. All rights reserved. Source

Cuaresma J.C.,Vienna University of Economics and Business | Cuaresma J.C.,International Institute For Applied Systems Analysis | Cuaresma J.C.,Wittgenstein Center for Demography and Global Human Capital | Cuaresma J.C.,Austrian Institute for Economic Research WIFO | Feldkircher M.,Oesterreichische Nationalbank
Regional Studies | Year: 2014

Regional Studies. This paper uses Bayesian model averaging (BMA) to find robust determinants of economic growth between 1995 and 2005 in a new data set of 255 European regions. It finds that income convergence between countries is dominated by the catching-up of regions in new member states in Central and Eastern Europe, whereas convergence within countries is driven by regions in old European Union member states. Regions containing capital cities are growing faster, particularly in Central and Eastern European countries, as do regions with a large share of workers with a higher education. The results are robust when allowing for spatial spillovers among European regions. © 2012 © 2012 Regional Studies Association. Source

Discover hidden collaborations