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2019 | 11 | nr 1 | 23--45
Tytuł artykułu

One-Period Joint Forecasts of Polish Inflation, Unemployment and Interest Rate Using Bayesian VEC-MSF Models

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper aims at comparing forecast ability of VAR/VEC models with a non-changing covariance matrix and two classes of Bayesian Vector Error Correction - Stochastic Volatility (VEC-SV) models, which combine the VEC representation of a VAR structure with stochastic volatility, represented by the Multiplicative Stochastic Factor (MSF) process, the SBEKK form or the MSFSBEKK specification. Based on macro-data coming from the Polish economy (time series of unemployment, inflation and interest rates) we evaluate predictive density functions employing of such measures as log predictive density score, continuous rank probability score, energy score, probability integral transform. Each of them takes account of different feature of the obtained predictive density functions. (original abstract)
Rocznik
Tom
11
Numer
Strony
23--45
Opis fizyczny
Twórcy
  • Cracow University of Economics, Poland
autor
  • Cracow University of Economics, Poland
Bibliografia
  • [1] Abbate A., Marcellino M., (2018), Point, interval and density forecast of exchange rates with time varying parameter models, Journal of the Royal Statistical Society: Series A (Statistics in Society) 181, 155-179.
  • [2] Berg T.O., Henzel S.R., (2015), Point and density forecasts for the euro area using Bayesian VARs, International Journal of Forecasting 31, 1067-1095.
  • [3] Berg T. O., (2017), Forecast accuracy of a BVAR under alternative specifications of the zero lower bound, Studies in Nonlinear Dynamics and Econometrics 21, 1-29.
  • [4] Carriero A., Kapetanios G., Marcellino M., (2011), Forecasting large datasets with Bayesian reduced rank multivariate models, Journal of Applied Econometrics 26, 735-761.
  • [5] Chikuse Y., (2002), Statistics on special manifolds. Lecture Notes in Statistics 174, Springer-Verlag, New York.
  • [6] Clark T. E., Ravazzolo F., (2015), Macroeconomic forecasting performance under alternative specifications of time-varying volatility, Journal of Applied Econometrics 30, 551-575.
  • [7] Geweke J., Amisano G., (2010), Comparing and evaluating Bayesian predictive distributions of asset returns, International Journal of Forecasting 26, 216-230.
  • [8] Gneiting T., Balabdaoui F., Raftery A.E., (2007), Probabilistic forecasts, calibration and sharpness, Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69, 243-268.
  • [9] Gneiting T., Raftery A.E., (2007), Strictly proper scoring rules, prediction, and estimation, Journal of the American Statistical Association 102, 359-378.
  • [10] Hersbach H., (2000), Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather and Forecasting 15, 559-570.
  • [11] Kass R. E., Raftery A. E., (1995), Bayes Factors, Journal of the American Statistical Association 90, 773-795.
  • [12] Koop G., León-González R., Strachan R. W., (2010), Efficient posterior simulation for cointegrated models with priors on the cointegration space, Econometric Reviews 29, 224-242.
  • [13] Mitchell J., Wallis K.F., (2011), Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness, Journal of Applied Econometrics 26, 1023-1040.
  • [14] Osiewalski J., (2009), New Hybrid Models of Multivariate Volatility (a Bayesian Perspective), Przegląd Statystyczny (Statistical Review) 56, 15-22.
  • [15] Osiewalski J., Pajor A., (2009), Bayesian analysis for hybrid MSF-SBEKK models of multivariate volatility, Central European Journal of Economic Modelling and Econometrics 1, 179-202.
  • [16] Pajor A., Osiewalski J., (2012), Bayesian Value-at-Risk and expected shortfall for a large portfolio (multi- and univariate approaches), Acta Physica Polonica A 121, B-101-B-109.
  • [17] Pajor A., Wróblewska J., (2017), VEC-MSF models in Bayesian analysis of shortand long-run relationships, Studies in Nonlinear Dynamics and Econometrics 21, 1-22.
  • [18] Primiceri G. E., (2005), Time Varying Structural Vector Autoregressions and Monetary Policy, Review of Economic Studies 72, 821-852.
  • [19] Rossi B., Sekhposyan T., (2014), Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set, International Journal of Forecasting 30, 662-682.
  • [20] Stelmasiak D., Szafrański G., (2016), Forecasting the Polish inflation using Bayesian VAR models with seasonality, Central European Journal of Economic Modelling and Econometrics 8, 21-42.
  • [21] Weiss A.A., (1996), Estimating time series models using the relevant cost function, Journal of Applied Econometrics 11, 539-560
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171552923

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