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on Econometric Time Series |
| By: | Stéphane Goutte (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS : UMR7599 - Université Paris VI - Pierre et Marie Curie - Université Paris VII - Paris Diderot) |
| Abstract: | In this paper we discuss the calibration issues of regime switching models built on mean-reverting and local volatility processes combined with two Markov regime switch- ing processes. In fact, the volatility structure of this model depends on a first exogenous Markov chain whereas the drift structure depends on a conditional Markov chain with re- spect to the first one. The structure is also assumed to be Markovian and both structure and regime are unobserved. Regarding this construction, we extend the classical Expectation- Maximization (EM) algorithm to be applied to our regime switching model. We apply it to economic datas (Euro-Dollars foreign exchange rate and Brent oil price) to show that this modelling well identifies both mean reverting and volatility regimes switches. More- over, it allows us to give economic interpretations of this regime classification such as some financial crisis or some economic policies. |
| Keywords: | Markov regime switching; Expectation-Maximization algorithm; mean-reverting; local volatility; economics data. |
| Date: | 2012–10–31 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-00747479&r=ets |
| By: | Tomás del Barrio Castro; Denise R. Osborn; A.M. Robert Taylor |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:man:sespap:1228&r=ets |
| By: | Costantini, Mauro (Department of Economics and Finance, Brunel University London, United Kingdom); Gunter, Ulrich (Austrian National Bank, Vienna, Austria); Kunst, Robert M. (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria and Department of Economics, University of Vienna, Austria) |
| Abstract: | We study the benefits of forecast combinations based on forecast-encompassing tests relative to uniformly weighted forecast averages across rival models. For a realistic simulation design, we generate multivariate time-series samples of size 40 to 200 from a macroeconomic DSGE-VAR model. Constituent forecasts of the combinations are formed from four linear autoregressive specifications, one of them a more sophisticated factor-augmented vector autoregression (FAVAR). The forecaster is assumed not to know the true data-generating model. Results depend on the prediction horizon. While one-step prediction fails to support test-based combinations at all sample sizes, the test-based procedure clearly dominates at prediction horizons greater than two. |
| Keywords: | Combining forecasts, encompassing tests, model selection, time series, DGSE-VAR model |
| JEL: | C15 C32 C53 |
| Date: | 2012–10 |
| URL: | http://d.repec.org/n?u=RePEc:ihs:ihsesp:292&r=ets |
| By: | Pilar Poncela; Esther Ruiz |
| Abstract: | In the context of dynamic factor models (DFM), it is known that, if the cross-sectional and time dimensions tend to infinity, the Kalman filter yields consistent smoothed estimates of the underlying factors. When looking at asymptotic properties, the cross- sectional dimension needs to increase for the filter or stochastic error uncertainty to decrease while the time dimension needs to increase for the parameter uncertainty to decrease. ln this paper, assuming that the model specification is known, we separate the finite sample contribution of each of both uncertainties to the total uncertainty associated with the estimation of the underlying factors. Assuming that the parameters are known, we show that, as far as the serial dependence of the idiosyncratic noises is not very persistent and regardless of whether their contemporaneous correlations are weak or strong, the filter un-certainty is a non-increasing function of the cross-sectional dimension. Furthermore, in situations of empirical interest, if the cross-sectional dimension is beyond a relatively small number, the filter uncertainty only decreases marginally. Assuming weak contemporaneous correlations among the serially uncorrelated idiosyncratic noises, we prove the consistency not only of smooth but also of real time filtered estimates of the underlying factors in a simple case, extending the results to non-stationary DFM. In practice, the model parameters are un-known and have to be estimated, adding further uncertainty to the estimated factors. We use simulations to measure this uncertainty in finite samples and show that, for the sample sizes usually encountered in practice when DFM are fitted to macroeconomic variables, the contribution of the parameter uncertainty can represent a large percentage of the total uncertainty involved in factor extraction. All results are illustrated estimating common factors of simulated time series |
| Keywords: | Common factors, Cross-sectional dimension, Filter uncertainty, Parameter uncertainty, Steady-state |
| Date: | 2012–10 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws122317&r=ets |
| By: | Medel, Carlos A.; Salgado, Sergio C. |
| Abstract: | We test two questions: (i) Is the Bayesian Information Criterion (BIC) more parsimonious than Akaike Information Criterion (AIC)?, and (ii) Is BIC better than AIC for forecasting purposes? By using simulated data, we provide statistical inference of both hypotheses individually and then jointly with a multiple hypotheses testing procedure to control better for type-I error. Both testing procedures deliver the same result: The BIC shows an in- and out-of-sample superiority over AIC only in a long-sample context. |
| Keywords: | AIC; BIC; time-series models; overfitting; forecast comparison; joint hypothesis testing |
| JEL: | C51 C53 C52 C22 |
| Date: | 2012–10–25 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:42235&r=ets |