
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 meanreverting 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 (EuroDollars 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; ExpectationMaximization algorithm; meanreverting; local volatility; economics data. 
Date:  2012–10–31 
URL:  http://d.repec.org/n?u=RePEc:hal:wpaper:hal00747479&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 forecastencompassing tests relative to uniformly weighted forecast averages across rival models. For a realistic simulation design, we generate multivariate timeseries samples of size 40 to 200 from a macroeconomic DSGEVAR model. Constituent forecasts of the combinations are formed from four linear autoregressive specifications, one of them a more sophisticated factoraugmented vector autoregression (FAVAR). The forecaster is assumed not to know the true datagenerating model. Results depend on the prediction horizon. While onestep prediction fails to support testbased combinations at all sample sizes, the testbased procedure clearly dominates at prediction horizons greater than two. 
Keywords:  Combining forecasts, encompassing tests, model selection, time series, DGSEVAR 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 crosssectional 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 uncertainty is a nonincreasing function of the crosssectional dimension. Furthermore, in situations of empirical interest, if the crosssectional 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 nonstationary DFM. In practice, the model parameters are unknown 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, Crosssectional dimension, Filter uncertainty, Parameter uncertainty, Steadystate 
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 typeI error. Both testing procedures deliver the same result: The BIC shows an in and outofsample superiority over AIC only in a longsample context. 
Keywords:  AIC; BIC; timeseries 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 