Grassi, Stefano and Proietti, Tommaso (2010): Characterizing economic trends by Bayesian stochastic model specifi cation search.
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We apply a recently proposed Bayesian model selection technique, known as stochastic model specification search, for characterising the nature of the trend in macroeconomic time series. We illustrate that the methodology can be quite successfully applied to discriminate between stochastic and deterministic trends. In particular, we formulate autoregressive models with stochastic trends components and decide on whether a specific feature of the series, i.e. the underlying level and/or the rate of drift, are fixed or evolutive.
|Item Type:||MPRA Paper|
|Original Title:||Characterizing economic trends by Bayesian stochastic model specifi cation search|
|Keywords:||Bayesian model selection; stationarity; unit roots; stochastic trends; variable selection.|
|Subjects:||E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations; Cycles
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
|Depositing User:||Tommaso Proietti|
|Date Deposited:||09. May 2010 14:30|
|Last Modified:||22. Feb 2013 19:14|
Caner, M. and Kilian, L. (2001). “Size distortions of tests of the null hypothesis of stationarity: evidence and implications for the PPP debate”, Journal of International Money and Finance, Vol. 20(5), 639 - 657.
Christiano, L., Eichenbaum, M., and Vigfusson, R. (2003). What happens after a technology shock? Federal Reserve Board, International Finance Discussion Paper, 2003-2768.
Chib, S. and Jeliazkov, I. (2001). “Marginal Likelihood from the Metropolis-Hastings Output”, Journal of the American Statistical Association, 96, 270 - 281.
De Jong, D. N. and Whiteman, C. H. (1991). “The Case for Trend-Stationarity Is Stronger Than We Thought”, Journal of Applied Econometrics, Vol. 6(4), 413 - 421.
Dickey, D.A. and Fuller, W.A. (1979). “Distribution of the Estimators for Autoregressive Time Series with a Unit Root”, Journal of the American Statistical Association, 74, 427 - 431.
Doornik, J.A. (2007), Ox: An Object-Oriented Matrix Programming Language, Timberlake Consultants Press, London.
Durbin, J. and Koopman, S.J. (2002). “A simple and efficient simulation smoother for state space time series analysis”, Biometrika, 89, 603 - 615.
Elliott, G., Rothenberg, T.J. and Stock, J.H. (1996). “Efficient Tests for an Autoregressive Unit Root”, Econometrica, Vol. 64, No. 4, 813 - 836.
Fruehwirth-Schnatter, S. (1995). “Bayesian model discrimination and Bayes factor for linear gaussian state space models”. Journal of the Royal Statistical Society, series B, 57, 237 - 246.
Fruehwirth-Schnatter, S. (2004). “Efficient Bayesian Parameter Estimation for State Space Models Based on Reparameterizations”. In Harvey, A.C., Koopman, S.J. and Shephard, N. (eds.), State Space and Unobserved Component Models: Theory and Applications, Proceedings of a Conference in Honour of James Durbin, Cambridge University Press, 123–151.
Fruehwirth-Schnatter, S. and Wagner, H. (2009). “Stochastic model specification search for Gaussian and partial non-Gaussian state space models”, Journal of Econometrics(forthcoming).
Gal, G. (1999). “Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?”. American Economic Review, Vol. 89(1), 249 - 271.
Gamerman, D. and Lopes, F. H. (2007). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition (Chapman and Hall/CRC Texts in Statistical Science).
Gelfand, A.E., Sahu, S.K., and Carlin, B.P. (1995), “Efficient Parameterizations for Normal Linear Mixed Models”, Biometrika, 82, 479 - 488.
George, E. I. and McCulloch, R. (1993). “Variable selection via Gibbs sampling”, Journal of the American Statistical Association, 88, 881 - 889.
George, E. I. and McCulloch, R. (1997). “Approaches for Bayesian variable selection”, Statistica Sinica, 7, 339 - 373.
Harvey, A.C. (1989). Forecasting, Structural Time Series and the Kalman Filter, Cambridge University Press, Cambridge, UK.
Koop, G. (1992). “Objective’ Bayesian Unit root tests”, Journal of Applied Econometrics, 7, 65 - 82.
Koop, G. (1994). “Recent progress in applied Bayesian Econometrics”, Journal of Economic Survey, 8, 1 - 34.
Koop, G. (2003). Bayesian Econometrics, John Wiley and Sons Ltd. 21
Koop, G. and Van Dijk, H. (2000). “Testing for integration using evolving trend and seasonals models: A Bayesian approach”, Journal of Econometrics, 97, 261 - 291.
Kwiatkowski, D. , Phillips, P. C. B. , Schmidt, P. and Y. Shin (1992). “Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root”. Journal of Econometrics, 54, 159 - 178.
Leybourne, S. J. and McCabe, B. P. M. (1994). “A Consistent Test for a Unit Root”, Journal of Business and Economic Statistics, Vol. 12(2), 157 - 166.
Nelson, C. R. and Plosser, C. I. (1982). “Trend and Random Walk in Macroeconomic Time Series”, Journal of Monetary Economic, 10, 139 - 162.
Ng, S. and Perron, P. (2001). “Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power”, Econometrica, Vol. 69(6), 1519 - 1554.
Nyblom J. and Makelainen T. (1983). “Comparisons of Tests for the Presence of Random Walk Coefficients in a Simple Linear Model”, Journal of the American Statistical Association, 78, 865 - 864.
Perron, P. (1989). “The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis”, Econometrica, Vol. 57(6), 1361 - 1401.
Phillips, P.C.B. (1990). “To criticize the critics: An objective Bayesian analysis of stochastic trends”, Journal of Applied Econometrics, Vol. 6(4), 333 - 364.
Phillips, P.C.B., and W. Ploberger (1994). “Posterior odds testing for a unit root with databased model selection”, Econometric Theory 10, 774 - 808.
Robert, P.C and Casella, G. (2004). Monte Carlo Statistical Methods, Springer. Schotman, P. C. and van Dijk, H.K. (1991). “On Bayesian Routes to Unit Roots”, Journal of Applied Econometrics, Vol. 6(4), 387 - 401.
Schwert, G. W. (1989). “Tests for Unit Roots: A Monte Carlo Investigation”, Journal of Business and Economic Statistics, Vol. 7(2), 147 - 159.
Sims, C.A. (1988). “Bayesian skepticism on unit root econometrics”, Journal of Economic Dynamic and Control, 12, 463 - 474.
Sims, C.A. and Uhlig, H. (1991). “Understanding unit rooters: A helicopter tour”, Econometrica, 59, 1591 - 1599.
Smith, M. and Kohn, R. (1996). “Nonparametric Regression using Bayesian Variable Selection”, Journal of Econometrics, 75, 317–343.
Stock, J.H. and Watson, M.W. (2007). “Why Has U.S. Inflation Become Harder to Forecast?”, Journal of Money, Credit and Banking, 39, 3 - 34.
Strickland, C. M., Martin, G.M. and Forbes, C.S. (2007). “Parameterisation and efficient MCMC estimation of non-Gaussian state space models”, Computational Statistical and Data Analysis, 52, 2911–2930.
West, M., and Harrison, J. (1997). Bayesian Forecasting and Dynamic Models, 2nd ed., New York, Springer-Verlag.
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