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on Econometric Time Series |
| By: | Enrique Sentana; Gabriele Fiorentini (CEMFI, Centro de Estudios Monetarios y Financieros) |
| Abstract: | We rank the efficiency of several likelihood-based parametric and semiparametric estimators of conditional mean and variance parameters in multivariate dynamic models with i.i.d. spherical innovations, and show that Gaussian pseudo maximum likelihood estimators are inefficient except under normality. We also provide conditions for partial adaptivity of semiparametric procedures, and relate them to the consistency of distributionally misspecified maximum likelihood estimators. We propose Hausman tests that compare Gaussian pseudo maximum likelihood estimators with more efficient but less robust competitors. We also study the efficiency of sequential estimators of the shape parameters. Finally, we provide finite sample results through Monte Carlo simulations. |
| Keywords: | Adaptivity, ARCH, elliptical distributions, financial returns, Hausman tests, semiparametric estimators, sequential estimators. |
| JEL: | C13 C14 C12 C51 C52 |
| Date: | 2007–09 |
| URL: | http://d.repec.org/n?u=RePEc:cmf:wpaper:wp2007_0713&r=ets |
| By: | John M Maheu; Thomas H McCurdy |
| Abstract: | Many finance questions require a full characterization of the distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term structure of density forecasts is used to investigate the importance of: the intraday information embodied in the daily RV estimates; the functional form for log(RV) dynamics; the timing of information availability; and the assumed distributions of both return and log(RV) innovations. We find that a joint model of returns and volatility that features two components for log(RV) provides a good fit to S&P 500 and IBM data, and is a significant improvement over an EGARCH model estimated from daily returns. |
| Keywords: | RV, multiperiod, out-of-sample, term structure of density forecasts, observable SV |
| JEL: | C1 C50 C32 G1 |
| Date: | 2008–08–06 |
| URL: | http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-324&r=ets |
| By: | Andersson, Jonas (Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration); Karlis, Dimitris (Department of Statistics, Athens University of Economics and Business) |
| Abstract: | Time series models for count data have found increased interest in recent days. The existing literature refers to the case of data that have been fully observed. In the present paper, methods for estimating the parameters of the first-order integer-valued autoregressive model in the presence of missing data are proposed. The first method maximizes a conditional likelihood constructed via the observed data based on the k-step-ahead conditional distributions to account for the gaps in the data. The second approach is based on an iterative scheme where missing values are imputed in order to update the estimated parameters. The first method is useful when the predictive distributions have simple forms. We derive in full details this approach when the innovations are assumed to follow a finite mixture of Poisson distributions. The second method is applicable when there are not closed form expressions for the conditional likelihood or they are hard to derive. Simulation results and comparisons of the methods are reported. The proposed methods are applied to a data set concerning syndromic surveillance during the Athens 2004 Olympic Games. |
| Keywords: | Imputation; Markov Chain EM algorithm; mixed Poisson; discrete valued time series |
| JEL: | C32 |
| Date: | 2008–08–13 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhhfms:2008_014&r=ets |
| By: | Christian Bayer; Christoph Hanck |
| Abstract: | This paper suggests a combination procedure to exploit the imperfect correlation of cointegration tests to develop a more powerful meta test.To exemplify, we combine Engle and Granger (1987) and Johansen (1988) tests. Either of these underlying tests can be more powerful than the other one depending on the nature of the data-generating process. The new meta test is at least as powerful as the more powerful one of the underlying tests irrespective of the very nature of the data generating process. At the same time, our new meta test avoids the arbitrary decision which test to use if single test results conflict. Moreover it avoids the size distortion inherent in separately applying multiple tests for cointegration to the same data set. We apply our test to 143 data sets from published cointegration studies. There, in one third of all cases single tests give conflicting results whereas our meta test provides an unambiguous test decision. |
| Keywords: | Cointegration, meta test, multiple testing |
| JEL: | C12 C22 |
| Date: | 2008–05 |
| URL: | http://d.repec.org/n?u=RePEc:rwi:repape:0048&r=ets |
| By: | Mardi Dungey (Univeristy of Cambridge); George Milunovich (Macquarie University); Susan Thorp (University of Technology, Sydney) |
| Abstract: | Markets in financial crisis may experience heightened sensitivity to news from abroad and they may also spread turbulence into foreign markets, creating contagion. We use a structural GARCH model to separate and measure these two parts of crisis transmission. Unobservable structural shocks are named and linked to source markets using variance decompositions, allowing clearer interpretation of impulse response functions. Applying this method to data from the Asian crisis, we find signifcant contagion from Hong Kong to nearby markets but little heightened sensitivity. Impulse response functions for an equally-weighted equity portfolio show the increasing dominance of Korean and Hong Kong shocks during the crisis, whereas Indonesia\'s infuence shrinks. |
| Keywords: | Contagion, Structural GARCH |
| JEL: | F37 C51 |
| Date: | 2008–02–25 |
| URL: | http://d.repec.org/n?u=RePEc:qut:auncer:2008-11&r=ets |
| By: | McCAUSLAND, William |
| Abstract: | I introduce the HESSIAN method for semi-Gaussian state space models with univariate states. The vector of states a=(a^1; ... ; a^n) is Gaussian and the observed vector y= (y^1 ; ... ; y^n )> need not be. I describe a close approximation g(a) to the density f(a|y). It is easy and fast to evaluate g(a) and draw from the approximate distribution. In particular, no simulation is required to approximate normalization constants. Applications include likelihood approximation using importance sampling and posterior simulation using Markov chain Monte Carlo (MCMC). HESSIAN is an acronym but it also refers to the Hessian of log f(a|y), which gures prominently in the derivation. I compute my approximation for a basic stochastic volatility model and compare it with the multivariate Gaussian approximation described in Durbin and Koopman (1997) and Shephard and Pitt (1997). For a wide range of plausible parameter values, I estimate the variance of log f(a|y) - log g(a) with respect to the approximate density g(a). For my approximation, this variance ranges from 330 to 39000 times smaller. |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:mtl:montde:2008-03&r=ets |