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on Econometric Time Series |
| By: | Dominique Guegan (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris); Philippe De Peretti (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I) |
| Abstract: | In this paper, we present a procedure that tests for the null of time-homogeneity of the first two moments of a time-series. Whereas the literature dedicated to structural breaks testing procedures often focuses on one kind of alternative, i.e. discrete shifts or smooth transition, our procedure is designed to deal with a broader alternative including i) discrete shifts, ii) smooth transition, iii) time-varying moments, iv) probability-driven breaks, v) GARCH or Stochastic Volatility Models for the variance. Our test uses the recently introduced maximum entropy bootstrap, designed to capture both time-dependency and time-heterogeneity. Running simulations, our procedure appears to be quite powerful. To some extent, our paper is an extension of Heracleous, Koutris and Spanos (2008). |
| Keywords: | Time-homogeneity, maximum entropy bootstrap. |
| Date: | 2010–12 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-00560221&r=ets |
| By: | Luc Bauwens; Gary Koop; Dimitris Korobilis; Jeroen Rombouts |
| Abstract: | This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well. <P> |
| Keywords: | Forecasting, change-points, Markov switching, Bayesian inference., |
| JEL: | C11 C22 C53 |
| Date: | 2011–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:cir:cirwor:2011s-13&r=ets |
| By: | Claudio Morana |
| Abstract: | In the paper the fractionally integrated heteroskedastic factor vec- tor autoregressive (FI-HF-VAR) model is introduced. The proposed approach is characterized by minimal pretesting requirements and sim- plicity of implementation also in very large systems, performing well independently of integration properties and sources of persistence, i.e. deterministic or stochastic, accounting for common features of di¤erent kinds, i.e. common integrated (of the fractional or inte- ger type) or non integrated stochastic factors, also featuring condi- tional heteroskedasticity, and common deterministic break processes. The proposed approach allows for accurate investigation of economic time series, from persistence and copersistence analysis to impulse responses and forecast error variance decomposition. Monte Carlo results strongly support the proposed methodology. Key words: long and short memory, structural breaks, fractionally integrated heteroskedastic factor vector autoregressive model. |
| JEL: | C22 |
| Date: | 2010–12 |
| URL: | http://d.repec.org/n?u=RePEc:icr:wpmath:36-2010&r=ets |
| By: | Gerrit Reher; Bernd Wilfling |
| Abstract: | In this paper we develop a unifying Markov-switching GARCH model which enables us (1) to specify complex GARCH equations in two distinct Markov-regimes, and (2) to model GARCH equations of different functional forms across the two Markov-regimes. To give a simple example, our flexible Markov-switching approach is capable of estimating an exponential GARCH (EGARCH) specification in the first and a standard GARCH specification in the second Markov-regime. We derive a maximum likelihood estimation framework and apply our general Markov-switching GARCH model to daily excess returns of the German stock market index DAX. Our empirical study has two major findings. First, our estimation results unambiguously indicate that our general model outperforms all conventional Markov-switching GARCH models hitherto estimated in the financial literature. Second, we find significant Markov-switching in the German stock market with substantially differing volatility structures across the regimes. |
| Keywords: | Markov-switching models; GARCH models; Dynamics of stock index returns |
| JEL: | C5 G10 G15 |
| Date: | 2011–01 |
| URL: | http://d.repec.org/n?u=RePEc:cqe:wpaper:1711&r=ets |
| By: | Chan, F.; McAleer, M.J.; Medeiros, M.C. |
| Abstract: | Nonlinear time series models, especially those with regime-switching and conditionally heteroskedastic errors, have become increasingly popular in the economics and finance literature. However, much of the research has concentrated on the empirical applications of various models, with little theoretical or statistical analysis associated with the structure of the processes or the associated asymptotic theory. In this paper, we first derive necessary conditions for strict stationarity and ergodicity of three different specifications of the first-order smooth transition autoregressions with heteroskedastic errors. This is important, among other reasons, to establish the conditions under which the traditional LMlinearity tests based on Taylor expansions are valid. Second, we provide sufficient conditions for consistency and asymptotic normality of the Quasi- Maximum Likelihood Estimator for a general nonlinear conditional mean model with first-order GARCH errors. |
| Keywords: | nonlinear time series;regime-switching;smooth transition;STAR;GARCH;log-moment;moment conditions;asymptotic theory |
| Date: | 2011–01–24 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureir:1765022216&r=ets |
| By: | Smeekes Stephan (METEOR) |
| Abstract: | We propose an approach to investigate the stationarity properties of individual units in a panel based on testing user-defined increasing proportions of hypothesized stationary units sequentially. Asymptotically valid critical values are obtained using the block bootstrap. This sequential approach has an advantage over multiple testing approaches, in particular if N is large and T is small, as it can exploit the cross-sectional dimension, which the multiple testing approaches cannot do effectively. A simulation study is conducted to analyze the relative performance of the approach in comparison with multiple testing approaches. The method is also illustrated by two empirical applications, in testing for unit roots in real exchange rates and log earnings data of households. The simulation study and applications demonstrate the usefulness of our method, in particular in panels with large N and small T. |
| Keywords: | econometrics; |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:umamet:2011003&r=ets |
| By: | Edward Herbst; Frank Schorfheide |
| Abstract: | This paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. The authors construct posterior predictive checks to evaluate the calibration of conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation DSGE model as well as the Smets and Wouters (2007) model using real-time data. They find that the additional features incorporated into the Smets-Wouters model do not lead to a uniform improvement in the quality of density forecasts and prediction of comovements of output, inflation, and interest rates. |
| Keywords: | Econometric models ; Forecasting |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedpwp:11-5&r=ets |
| By: | St\'ephane Chr\'etien; Juan-Pablo Ortega |
| Abstract: | The estimation of multivariate GARCH time series models is a difficult task mainly due to the significant overparameterization exhibited by the problem and usually referred to as the "curse of dimensionality". For example, in the case of the VEC family, the number of parameters involved in the model grows as a polynomial of order four on the dimensionality of the problem. Moreover, these parameters are subjected to convoluted nonlinear constraints necessary to ensure, for instance, the existence of stationary solutions and the positive semidefinite character of the conditional covariance matrices used in the model design. So far, this problem has been addressed in the literature only in low dimensional cases with strong parsimony constraints. In this paper we propose a general formulation of the estimation problem in any dimension and develop a Bregman-proximal trust-region method for its solution. The Bregman-proximal approach allows us to handle the constraints in a very efficient and natural way by staying in the primal space and the Trust-Region mechanism stabilizes and speeds up the scheme. Preliminary computational experiments are presented and confirm the very good performances of the proposed approach. |
| Date: | 2011–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1101.5475&r=ets |
| By: | Ph. Barbe (CNRS); W. P. McCormick (UGA) |
| Abstract: | Motivated by applications to insurance mathematics, we prove some heavy-traffic limit theorems for process which encompass the fractionally integrated random walk as well as some FARIMA processes, when the innovations are in the domain of attraction of a nonGaussian stable distribution. |
| Date: | 2011–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1101.4437&r=ets |