We study alternative models for capturing abrupt structural changes (level shifts) in a times series. The problem is confounded by the presence of transient outliers. We compare the performance of non-Gaussian time-varying parameter models and multiprocess mixture models within a Monte Carlo experimental setup. Our findings suggest that once we incorporate shocks with thick-tailed probability distributions, the superiority of the multiprocess mixture models over the time-varying parameter models, reported in an earlier study, disappears. The behavior of the two models, both in adapting to level shifts and in reacting to transient outliers, is very similar.
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Publisher Info
Paper provided by Florida International University, Department of Economics in its series Working Papers with number
0307.
Length: 18 pages Date of creation: Jul 2003 Date of revision: Publication status: Published in Communications in Statistics: Simulation and Computation, 33(3):661-671, (2004). Handle: RePEc:fiu:wpaper:0307
Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
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