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Flexible Seasonal Long Memory and Economic Time Series

Ooms, M. - Erasmus University Rotterdam, Econometric Institute - 1995

We discuss specification, frequency domain estimation and application of flexible fractionally integrated seasonal long memory time series models, which allow for 'chi-squared' (seasonal) unit root testing. We suggest periodogram regression and approximate ML estimation. We successfully apply a...

Persistent link: https://www.econbiz.de/10004972223

Convergence and Persistence of Left-Right Political Orientations in The Netherlands 1978-1995

Eisinga, R.; Franses, Ph.H.B.F.; Ooms, M. - Erasmus University Rotterdam, Econometric Institute - 1997

-Theory- Two theories about trends in left-right political orientations are juxtaposed: the persistence theory claiming that left-right orientations are highly resistant to change versus the irrelevance theory anticipating a move of mass publics towards the center of the left-right continuum....

Persistent link: https://www.econbiz.de/10004972274

Autoregression-Based Estimators for ARFIMA Models

Galbraith, John; Zinde-Walsh, Victoria - Centre Interuniversitaire de Recherche en Analyse des … - 2001

This paper describes a parameter estimation method for both stationary and non-stationary ARFIMA (p,d,q) models, based on autoregressive approximation. We demonstrate consistency of the estimator for -1/2 d 1, and in the stationary case we provide a Normal approximation to the finite-sample...

Persistent link: https://www.econbiz.de/10005100960

A nonlinear long memory model for US unemployment

Dijk, D.J.C. van; Franses, Ph.H.B.F.; Paap, R. - Erasmus University Rotterdam, Econometric Institute - 2000

Two important empirical features of monthly US unemployment are that shocks to the series seem rather persistent and that unemployment seems to rise faster in recessions than that it falls during expansions. To jointly capture these features of long memory and nonlinearity, respectively, we put...

Persistent link: https://www.econbiz.de/10005505011

Modelling Long Memory Volatility in Agricultural Commodity Futures Returns

Tansuchat, R.; Chang, C-L.; McAleer, M.J. - Erasmus University Rotterdam, Econometric Institute - 2009

, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the … FIGARCH model of Baillie et al. (1996), FIEGACH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of Tse (1998 …), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of …

Persistent link: https://www.econbiz.de/10008570627

Ten Things We Should Know About Time Series

McAleer, M.J.; Oxley, L. - Erasmus University Rotterdam, Econometric Institute - 2010

Time series data affect many aspects of our lives. This paper highlights ten things we should all know about time series, namely: a good working knowledge of econometrics and statistics, an awareness of measurement errors, testing for zero frequency, seasonal and periodic unit roots, analysing...

Persistent link: https://www.econbiz.de/10008484072

The volatility of realized volatility

Corsi, Fulvio; Kretschmer, Uta; Mittnik, Stefan; … - Center for Financial Studies - 2005

Using unobservable conditional variance as measure, latentvariable approaches, such as GARCH and stochasticvolatility …

Persistent link: https://www.econbiz.de/10010986437

Mixed normal conditional heteroskedasticity

Haas, Markus; Mittnik, Stefan; Paolella, Marc S. - Center for Financial Studies - 2002

Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been … employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which …

Persistent link: https://www.econbiz.de/10010958539

Forecasting stock market volatility and the informational efficiency of the DAX-index options market

Claessen, Holger; Mittnik, Stefan - Center for Financial Studies - 2002

, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility …

Persistent link: https://www.econbiz.de/10010958558

Modeling and predicting market risk with Laplace-Gaussian mixture distributions

Haas, Markus; Mittnik, Stefan; Paolella, Marc S. - Center for Financial Studies - 2005

While much of classical statistical analysis is based on Gaussian distributional assumptions, statistical modeling with the Laplace distribution has gained importance in many applied fields. This phenomenon is rooted in the fact that, like the Gaussian, the Laplace distribution has many...

Persistent link: https://www.econbiz.de/10010958588