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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); Zhiping Lu (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, ECNU - East China Normal University) |
| Abstract: | Long memory processes have been extensively studied over the past decades. When dealing with the financial and economic data, seasonality and time-varying long-range dependence can often be observed and thus some kind of non-stationarity can exist inside financial data sets. To take into account this kind of phenomena, we propose a new class of stochastic process : the locally stationary k-factor Gegenbauer process. We describe a procedure of estimating consistently the time-varying parameters by applying the discrete wavelet packet transform (DWPT). The robustness of the algorithm is investigated through simulation study. An application based on the error correction term of fractional cointegration analysis of the Nikkei Stock Average 225 index is proposed. |
| Keywords: | Discrete wavelet packet transform ; Gegenbauer process ; Nikkei Stock Average 225 index ; non-stationarity ; ordinary least square estimation |
| Date: | 2009–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-00375531_v1&r=ets |
| By: | Lanouar Charfeddine (OEP - Université de Marne-la-Vallée); 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) |
| Abstract: | Are structural breaks models true switching models or long memory processes ? The answer to this question remain ambiguous. A lot of papers, in recent years, have dealt with this problem. For instance, Diebold and Inoue (2001) and Granger and Hyung (2004) show, under specific conditions, that switching models and long memory processes can be easily confused. In this paper, using several generating models like the mean-plus-noise model, the STOchastic Permanent BREAK model, the Markov switching model, the TAR model, the sign model and the Structural CHange model (SCH) and several estimation techiques like the GPH technique, the Exact Local Whittle (ELW) and the Wavelet methods, we show that, if the answer is quite simple in some cases, it can be mitigate in other cases. Using French and American inflation rates, we show that these series cannot be characterized by the same class of models. The main result of this study suggests that estimating the long memory parameter without taking account existence of breaks in the data sets may lead to misspecification and to overestimate the true parameter. |
| Keywords: | Structural breaks models, spurious long memory behavior, inflation series. |
| Date: | 2009–04 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-00377485_v1&r=ets |
| By: | Abdou Kâ Diongue (UFR SAT - Université Gaston Berger - Université Gaston Berger de Saint-Louis); 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); Rodney C. Wolff (School of Mathematical Sciences - Queensland University of Technology) |
| Abstract: | In this paper, we discuss the class of Bilinear GATRCH (BL-GARCH) models which are capable of capturing simultaneously two key properties of non-linear time series : volatility clustering and leverage effects. It has been observed often that the marginal distributions of such time series have heavy tails ; thus we examine the BL-GARCH model in a general setting under some non-Normal distributions. We investigate some probabilistic properties of this model and we propose and implement a maximum likelihood estimation (MLE) methodology. To evaluate the small-sample performance of this method for the various models, a Monte Carlo study is conducted. Finally, within-sample estimation properties are studied using S&P 500 daily returns, when the features of interest manifest as volatility clustering and leverage effects. |
| Keywords: | BL-GARCH process - elliptical distribution - leverage effects - Maximum Likelihood - Monte Carlo method - volatility clustering |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-00368340_v1&r=ets |
| By: | Alexander Subbotin (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I); Thierry Chauveau (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I); Kateryna Shapovalova (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I) |
| Abstract: | We overview different methods of modeling volatility of stock prices and exchange rates, focusing on their ability to reproduce the empirical properties in the corresponding time series. The properties of price fluctuations vary across the time scales of observation. The adequacy of different models for describing price dynamics at several time horizons simultaneously is the central topic of this study. We propose a detailed survey of recent volatility models, accounting for multiple horizons. These models are based on different and sometimes competing theoretical concepts. They belong either to GARCH or stochastic volatility model families and often borrow methodological tools from statistical physics. We compare their properties and comment on their pratical usefulness and perspectives. |
| Keywords: | Volatility modeling, GARCH, stochastic volatility, volatility cascade, multiple horizons in volatility. |
| Date: | 2009–05 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-00390636_v1&r=ets |
| By: | Takamitsu Kurita (Faculty of Economics, Fukuoka University); Heino Bohn Nielsen (Department of Economics, University of Copenhagen); Anders Rahbek (Department of Economics, University of Copenhagen) |
| Abstract: | This paper presents likelihood analysis of the I(2) cointegrated vector autoregression with piecewise linear deterministic terms. Limiting behavior of the maximum likelihood estimators are derived, which is used to further derive the limiting distribution of the likelihood ratio statistic for the cointegration ranks, extending the result for I(2) models with a linear trend in Nielsen and Rahbek (2007) and for I(1) models with piecewise linear trends in Johansen, Mosconi, and Nielsen (2000). The provided asymptotic theory extends also the results in Johansen, Juselius, Frydman, and Goldberg (2009) where asymptotic inference is discussed in detail for one of the cointegration parameters. To illustrate, an empirical analysis of US consumption, income and wealth, 1965 - 2008, is performed, emphasizing the importance of a change in nominal price trends after 1980. |
| Keywords: | Cointegration, I(2); piecewise linear trends; likelihood analysis; US consumption |
| JEL: | C32 |
| Date: | 2009–07 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kuiedp:0913&r=ets |
| By: | Simona Delle Chiaie (Oesterreichische Nationalbank, Economic Studies Division, P.O. Box 61, A-1010 Vienna,) |
| Abstract: | This paper aims to shed light on potential pitfalls of di¤erent data filtering and detrending procedures for the estimation of stationary DSGE models. For this purpose, a medium-sized New Keynesian model as the one developed by Smets and Wouters (2003) is used to assess the sensitivity of the structural estimates to preliminary data transformations. To examine the question, we focus on two widely used detrending and filtering methods, the HP filter and linear detrending. After comparing the properties of business cycle components, we estimate the model through Bayesian techniques using in turn the two different sets of transformed data. Empirical findings show that posterior distributions of structural parameters are rather sensitive to the choice of detrending. As a consequence, both the magnitude and the persistence of theoretical responses to shocks depend upon preliminary filtering. |
| Keywords: | DSGE models; Filters; Trends; Bayesian estimates |
| JEL: | E3 |
| Date: | 2009–07–20 |
| URL: | http://d.repec.org/n?u=RePEc:onb:oenbwp:157&r=ets |
| By: | Sylvia Frühwirth-Schnatter (Department of Applied Statistics, Johannes Kepler University Linz, Austria); Christoph Pamminger (Department of Applied Statistics, Johannes Kepler University Linz, Austria) |
| Abstract: | Two approaches for model-based clustering of categorical time series based on time- homogeneous first-order Markov chains are discussed. For Markov chain clustering the in- dividual transition probabilities are fixed to a group-specific transition matrix. In a new approach called Dirichlet multinomial clustering the rows of the individual transition matri- ces deviate from the group mean and follow a Dirichlet distribution with unknown group- specific hyperparameters. Estimation is carried out through Markov chain Monte Carlo. Various well-known clustering criteria are applied to select the number of groups. An appli- cation to a panel of Austrian wage mobility data leads to an interesting segmentation of the Austrian labor market. |
| Keywords: | Markov chain Monte Carlo, model-based clustering, panel data, transition matrices, labor market, wage mobility |
| Date: | 2009–07 |
| URL: | http://d.repec.org/n?u=RePEc:jku:nrnwps:2009_07&r=ets |