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on Econometric Time Series |
| By: | Ladislav Kristoufek |
| Abstract: | In the paper, we introduce a new measure of correlation between possibly non-stationary series. As the measure is based on the detrending moving-average cross-correlation analysis (DMCA), we label it as the DMCA coefficient $\rho_{DMCA}(\lambda)$ with a moving average window length $\lambda$. We analytically show that the coefficient ranges between -1 and 1 as a standard correlation does. In the simulation study, we show that the values of $\rho_{DMCA}(\lambda)$ very well correspond to the true correlation between the analyzed series regardless the (non-)stationarity level. Dependence of the newly proposed measure on other parameters -- correlation level, moving average window length and time series length -- is discussed as well. |
| Date: | 2013–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1311.0657&r=ets |
| By: | SILVESTRINI, Andrea; VEREDAS, David |
| URL: | http://d.repec.org/n?u=RePEc:cor:louvrp:-2013&r=ets |
| By: | Nikolay Gospodinov; Damba Lkhagvasuren |
| Abstract: | This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and tends to outperform the existing methods for approximating multivariate processes over a wide range of the parameter space, especially for highly persistent vector autoregressions with roots near the unit circle. |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedawp:2013-05&r=ets |
| By: | Christensen, Bent Jesper; Kruse, Robinson; Sibbertsen, Philipp |
| Abstract: | We consider hypothesis testing in a general linear time series regression framework when the possibly fractional order of integration of the error term is unknown. We show that the approach suggested by Vogelsang (1998a) for the case of integer integration does not apply to the case of fractional integration. We propose a Lagrange Multiplier-type test whose limiting distribution is independent of the order of integration of the errors. Different testing scenarios for the case of deterministic and stochastic regressors are considered. Simulations demonstrate that the proposed test works well for a variety of different cases, thereby emphasizing its generality. |
| Keywords: | Long memory; linear time series regression; Lagrange Multiplier test |
| JEL: | C12 C22 |
| Date: | 2013–10 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-519&r=ets |
| By: | Cagnone, Silvia; Bartolucci, Francesco |
| Abstract: | Maximum likelihood estimation of dynamic latent variable models requires to solve integrals that are not analytically tractable. Numerical approximations represent a possible solution to this problem. We propose to use the Adaptive Gaussian-Hermite (AGH) numerical quadrature approximation for a class of dynamic latent variable models for time-series and panel data. These models are based on continuous time-varying latent variables which follow an autoregressive process of order 1, AR(1). Two examples of such models are the stochastic volatility models for the analysis of financial time-series and the limited dependent variable models for the analysis of panel data. A comparison between the performance of AGH methods and alternative approximation methods proposed in the literature is carried out by simulation. Examples on real data are also used to illustrate the proposed approach. |
| Keywords: | AR(1); categorical longitudinal data; Gaussian-Hermite quadrature; limited dependent variable models; stochastic volatility model |
| JEL: | C13 C32 C33 |
| Date: | 2013–10–29 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:51037&r=ets |