Simulation based estimation of threshold moving average models with contemporaneous shock asymmetry
AbstractPersistence of shocks to macroeconomic time series may differ depending on the sign or on whether a threshold value is crossed. For example, positive shocks to gross domestic product may be more persistent than negative shocks. Threshold (or asymmetric) moving average (TMA) models, by explicitly taking into account threshold behavior, can help discriminate whether there exists persistence asymmetry. Recently, building on the works of Wecker (1981, JASA, 76(373)) and De Gooijer (1998, JTSA, 19(1)) among others, Guay and Scaillet (2003, JBES, 21(1)) proposed TMA model in which both contemporaneous and lagged asymmetric effects are present and provided indirect inference framework for estimation and testing. This paper builds on their work and examines the properties of efficient method of moments (EMM) estimation of TMA class of models using Monte Carlo simulation experiments. The model is also applied to analyze the persistence properties of shocks in Turkish business cycles.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 34302.
Date of creation: 2011
Date of revision:
Threshold moving average models; contemporaneous asymmetry; persistence of shocks; Efficient Method of Moments;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-11-01 (All new papers)
- NEP-ECM-2011-11-01 (Econometrics)
- NEP-ETS-2011-11-01 (Econometric Time Series)
- NEP-ORE-2011-11-01 (Operations Research)
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