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on Econometric Time Series |
| By: | James Duffy; David Hendry |
| Abstract: | Abstract Data spanning long time periods, such as that over 1860–2012 for the UK, seem likely to have substantial errors of measurement that may even be integrated of order one, but which are probably cointegrated for cognate variables. We analyze and simulate the impacts of such measurement errors on parameter estimates and tests in a bivariate cointegrated system with trends and location shifts which reflect the many major turbulent events that have occurred historically. When trends or shifts therein are large, cointegration analysis is not much affected by such measurement errors, leading to conventional stationary attenuation biases dependent on the measurement-error variance, unlike the outcome when there are no offsetting shifts or trends. |
| Keywords: | Integrated Measurement Errors; Location Shifts; Long-run Data; Cointegration |
| JEL: | C51 C22 |
| Date: | 2017–01–17 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:818&r=ets |
| By: | Rasmus Pedersen (University of Copenhagen, LSTA); Olivier Wintenberger (University of Copenhagen, LSTA) |
| Abstract: | Conditions for geometric ergodicity of multivariate ARCH processes, with the so-called BEKK parametrization, are considered. We show for a class of BEKK-ARCH processes that the invariant distribution is regularly varying. In order to account for the possibility of different tail indices of the marginals, we consider the notion of vector scaling regular variation, in the spirit of Perfekt (1997). The characterization of the tail behavior of the processes is used for deriving the asymptotic distribution of the sample covariance matrices. |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1701.05091&r=ets |
| By: | Dominique Guegan (Centre d'Economie de la Sorbonne); Giovanni De Luca (Parthenope University of Naples); Giorgia Rivieccio (Parthenope University of Naples) |
| Abstract: | We present the three-stage pseudo maximum likelihood estimation in order to reduce the computational burdens when a copula-based model is applied to multiple time-series in high dimensions. The method is applied to general stationary Markov time series, under some assumptions which include a time-invariant copula as well as marginal distributions, extending the results of Yi and Liao [2010]. We explore, via simulated and real data, the performance of the model compared to the classical vectorial autoregressive model, giving the implications of misspecified assumptions for margins and/or joint distribution and providing tail dependence measures of economic variables involved in the analysis |
| Keywords: | Copula function; Three stage estimator; Multiple time series |
| JEL: | C1 |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:17001&r=ets |
| By: | Jean-Marie DUFOUR; Richard LUGER |
| Abstract: | This paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of normal mixtures implied by the regime-switching process and uses Monte Carlo test techniques to deal with the presence of an autoregressive component in the model specification. The proposed tests have very respectable power in comparison to the optimal tests for Markov-switching parameters of Carrasco et al. (2014) and they are also quite attractive owing to their computational simplicity. The new tests are illustrated with an empirical application to an autoregressive model of U.S. output growth. |
| Keywords: | mixture distributions, Markov chains, regime switching, parametric bootstrap, MonteCarlo tests, exact inference |
| JEL: | C12 C15 C22 C52 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:mtl:montec:15-2016&r=ets |
| By: | M. Mucciardi; E. Otranto |
| Abstract: | The Space–Time Autoregressive (STAR) model is one of the most widely used models to represent the dynamics of a certain variable recorded at several locations at the same time, capturing both their temporal and spatial relationships. Its advantages are often discussed in terms of parsimony with respect to space-time VAR structures because it considers a single coefficient for each time and spatial lag for the full time span and the full location set. This hypothesis can be very strong; the presence of groups of locations with similar dynamics makes it more realistic. In this work we add a certain degree of flexibility to the STAR model, providing the possibility for coefficients to vary in groups of locations, proposing a new class of flexible STAR models. Such groups are detected by means of a clustering algorithm. The new class or model is compared to the classical STAR and the space-time VAR by simulation experiments and a practical application. |
| Keywords: | spatial weight matrix,space–time models,forecasting,clustering |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:cns:cnscwp:201608&r=ets |