nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒11‒20
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Asymptotic theory for M estimators for martingale differences with applications to GARCH models By Tinkl, Fabian
  2. "GH skew Student's t-distribution in stochastic volatility model with application to stock returns" (in Japanese) By Jouchi Nakajima; Yasuhiro Omori
  3. Unobserved Heterogeneity in Multi-Spell Discrete Time Duration Models By Raquel Carrasco; José Ignacio García Pérez
  4. A Cholesky-MIDAS model for predicting stock portfolio volatility By Ralf Becker; Adam Clements; Robert O'Neill
  5. A Kernel Technique for Forecasting the Variance-Covariance Matrix By Ralf Becker; Adam Clements; Robert O'Neill
  6. Sign Restrictions in Structural Vector Autoregressions: A Critical Review By Renee Fry; Adrian Pagan

  1. By: Tinkl, Fabian
    Abstract: We generalize the results for statistical functionals given by [Fernholz, 1983] and [Serfling, 1980] to M estimates for samples drawn for an ergodic and stationary martingale sequence. In a first step, we take advantage of some recent results on the uniform convergency of the empirical distribution given by [Adams & Nobel, 2010] to prove consistency of M estimators, before we assume Hadamard differentiability of our estimators to prove their asymptotic normality. Further we apply the results to the LAD estimator of [Peng & Yao, 2003] and the maximum-likelihood estimator for GARCH processes to show the wide field of possible applications of this method. --
    Keywords: Hadamard differential,M estimator,von Mises Calculus,martingale differences,GARCH models
    Date: 2010
  2. By: Jouchi Nakajima (Department of Statistical Science, Duke University); Yasuhiro Omori (Faculty of Economics, University of Tokyo)
    Abstract: This paper represents empirical studies of SV models with a generalized hyperbolic (GH) skew Student's t-error distribution to embed both asymmetric heavy-tailness and leverage effects for financial time series. An efficient Markov chain Monte Carlo estimation method is described and the model is fit to daily S&P500 stock returns. The practical importance of the proposed model is highlighted through the model comparison based on the marginal likelihood, Value at Risk (VaR) and expected shortfall. The empirical results show that incorporating leverage and asymmetric heavy-tailness contributes to the model fit and predicting the expected shortfall.
    Date: 2010–11
  3. By: Raquel Carrasco (Department of Economics, Universidad Carlos III de Madrid); José Ignacio García Pérez (Department of Economics, Universidad Pablo de Olavide)
    Abstract: This paper considers the estimation of discrete time duration models. We highlight the enhance identification opportunities embedded in multiple spell data to separately identify the effect of duration dependence and individual time invariant unobserved heterogeneity. We consider two types of models: (i) random effects models specifying a mass point distribution for the unobserved heterogeneity; and (ii) fixed effects models in which the distribution of the effects is left unrestricted. The availability of multiple spell data allows us to consider this type of models, in the spirit of fixed effects discrete choice panel data models. We study the finite sample properties of different estimators for previous models by means of Monte Carlo simulations. Finally, as an empirical illustration, we estimate unemployment duration models using Spanish administrative data with information on the entire labor history of the individuals.
    Keywords: Duration models; Discrete choice; Multiple spells; Unobserved heterogeneity; Unemployment.
    JEL: J64 J61 C23 C41 J65
    Date: 2010–11
  4. By: Ralf Becker (University of Manchester); Adam Clements (QUT); Robert O'Neill (University of Manchester)
    Abstract: This paper presents a simple forecasting technique for variance covariance matrices. It relies significantly on the contribution of Chiriac and Voev (2010) who propose to forecast elements of the Cholesky decomposition which recombine to form a positive definite forecast for the variance covariance matrix. The method proposed here combines this methodology with advances made in the MIDAS literature to produce a forecasting methodology that is ‡exible, scales easily with the size of the portfolio and produces superior forecasts in simulation experiments and an empirical application.
    Keywords: Cholesky, Midas, volatility forecasts
    JEL: C22 G00
    Date: 2010–08–31
  5. By: Ralf Becker (University of Manchester); Adam Clements (QUT); Robert O'Neill (University of Manchester)
    Abstract: The forecasting of variance-covariance matrices is an important issue. In recent years an increasing body of literature has focused on multivariate models to forecast this quantity. This paper develops a nonparametric technique for generating multivariate volatility forecasts from a weighted average of historical volatility and a broader set of macroeconomic variables. As opposed to traditional techniques where the weights solely decay as a function of time, this approach employs a kernel weighting scheme where historical periods exhibiting the most similar conditions to the time at which the forecast if formed attract the greatest weight. It is found that the proposed method leads to superior forecasts, with macroeconomic information playing an important role.
    Keywords: Nonparametric, variance-covariance matrix, volatility forecasting, multivariate
    JEL: C14 C32 C53
    Date: 2010–10–28
  6. By: Renee Fry (ANU); Adrian Pagan (QUT/UTS)
    Abstract: The paper provides a review of the estimation of structural VARs with sign restrictions. It is shown how sign restrictions solve the parametric identification problem present in structural systems but leave the model identification problem unresolved. A market and a macro model are used to illustrate these points. Suggestions have been made on how to find a unique model. These are reviewed, along with some of the difficulties that can arise in how one is to use the impulse responses found with sign restrictions.
    Keywords: Structural Vector Autoregressions, New Keynesian Model, Sign Restrictions
    JEL: E32 C51 C32
    Date: 2010–07–28

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