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2006 | 7 | 151--160
Tytuł artykułu

A Bayesian Estimation and Testing of STUR Models With Application to Polish Financial Time-series

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of this paper is to present with the Bayesian estimation and testing of STUR processes, where the random parameter follows first-order stationary autoregressive process. Probably the first attempt to employ the Bayesian inference was presented in Jones and Marriott (1999). In their paper they have used Granger and Swanson (1997) model to derive posterior marginals and summary statistics. This paper is concerned with the STUR model introduced by Leybourne, McCabe and Tremayne (1996), which is computationally less demanding, and easy to implement. The marginal posteriors of parameters and summary statistics can be obtained by Gibbs sampler.(fragment of text)
Rocznik
Tom
7
Strony
151--160
Opis fizyczny
Twórcy
  • Nicolaus Copernicus University in Toruń, Poland
Bibliografia
  • Box, G.E.P., Jenkins, G.M. (1976), Time Series Analysis: Forecasting and Control, San Francisco, Holden-Day.
  • Carlin, B.P., Louis, T.A. (2000), Bayes and Empirical Bayes Methods for Data Analysis, New York, Chapman & Hall/CRC.
  • Gelman, A., Carlin, J., Stern, H., Rubin, D. (1997), Bayesian Data Analysis. London, Chapman & Hall.
  • Granger, C.W.J., Swanson, N.R. (1997), An Introduction to Stochastic Unit-root Process, Journal of Econometrics, 80, 35-62.
  • Jones, C.R., Marriott, J.M. (1999), A Bayesian analysis of stochastic unit root models, Bayesian Statistics, 6, 785-794.
  • Jostova, G., Philipov, A. (2005), Bayesian analysis of stochastic betas. Journal of Financial and Quantitative Analysis, 40, 4, 747-778.
  • Kwiatkowski, J. (2005a), Maximum likelihood estimation of stochastic unit root models with GARCH disturbances. Forecasting Financial Markets, Theory and Applications, Lodz, 149-157.
  • Kwiatkowski, J. (2005b), Bayesian analysis of STUR models, working paper version.
  • Kwiatkowski, J., Osińska, M. (2005), Forecasting STUR processes. A comparison to threshold and GARCH models. Acta Universitatis Lodziensis Folia Oeconomica, 190, Lodz, 159-176.
  • Leybourne, S.J., McCabe, B.P.M., Mills, T.C. (1996), Randomized unit root processes for modelling and forecasting financial time series: theory and applications, Journal of Forecasting, 15, 253-270.
  • Leybourne, S.J., McCabe, B.P.M., Tremayne, A.R. (1996), Can economic time series be differenced to stationarity? Journal of Business and Economic Statistics, 14, 435-446.
  • Nicholls, D.F., Quinn, B.G. (1982), Random Coefficient Autoregressive Models: An Introduction, New York, Springer-Verlag.
  • Newton, M.A., Raftery, A.E. (1994), Approximate Bayesian inference by the weighted likelihood bootstrap (with discussion). Journal of the Royal Statistical Society B, 56, 3-48.
  • Osiewalski, J., Pipień, M. (2004), Bayesian comparison of bivariate ARCH-type models for main exchange rates in Poland, Journal of Econometrics, 123, 371-391.
  • Sollis, R., Leybourne, S.J., Newbold, P. (2000), Stochastic unit roots modeling of stock price indices, Applied Financial Economics, 10, 311-315.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171295013

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