nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒02‒12
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. Multiplicative Conditional Correlation Models for Realized Covariance Matrices By BAUWENS, Luc; BRAIONE, Manuela; STORTI, Giuseppe
  2. Bayesian Semiparametric Forecasts of Real Interest Rate Data By DESCHAMPS, Philippe J.
  3. Estimation of a noisy subordinated Brownian Motion via two-scales power variations By Jose E. Figueroa-Lopez; K. Lee
  4. A forecasting performance comparison of dynamic factor models based on static and dynamic methods By F. Della Marra
  5. Alternative moment conditions and an efficient GMM estimator for dynamic panel data models By Federico Zincenko
  6. Mixture Normal Conditional Correlation Models By Maria Putintseva
  7. Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model By Gianluca Cubadda; Barbara Guardabascio
  8. A multivariate model for financial indices and an algorithm for detection of jumps in the volatility By Mario Bonino; Matteo Camelia; Paolo Pigato
  9. Negative binomial quasi-likelihood inference for general integer-valued time series models By Aknouche, Abdelhakim; Bendjeddou, Sara

  1. By: BAUWENS, Luc (Université catholique de Louvain, CORE, Belgium); BRAIONE, Manuela (Université catholique de Louvain, CORE, Belgium); STORTI, Giuseppe (Università di Salerno)
    Abstract: We introduce a class of multiplicative dynamic models for realized covariance matrices assumed to be conditionally Wishart distributed. The multiplicative structure enables consistent three-step estimation of the parameters, starting by covariance targeting of a scale matrix. The dynamics of conditional variances and correlations are inspired by specifications akin to the consistent dynamic conditional correlation model of the multivariate GARCH literature, and estimation is performed by quasi maximum likelihood. Simulations show that in finite samples the three-step estimator has smaller bias and root mean squared error than the full estimator when the cross-sectional dimension increases. An empirical application illustrates the flexibility of these models in a low-dimensional setting, and another one illustrates their e ectiveness and practical usefulness in high dimensional portfolio allocation strategies.
    Keywords: Dynamic conditional correlations, Wishart distribution, Multiplicative models, Realized covariances
    Date: 2016–11–24
  2. By: DESCHAMPS, Philippe J. (Université catholique de Louvain, CORE, Belgium)
    Abstract: The non-hierarchical Dirichlet process prior has been mainly used for parameters of innovation distributions. It is, however, easy to apply to all the parameters (coefficients of covariates and innovation variance) of more general regression models. This paper investigates the predictive performance of a simple (non-hierarchical) Dirichlet process mixture of Gaussian autoregressions for forecasting monthly US real interest rate data. The results suggest that the number of mixture components increases sharply over time, and the predictive marginal likelihoods strongly dominate those of a benchmark autoregressive model. Unconditional predictive coverage is vastly improved in the mixture model.
    Keywords: Dirichlet process mixture, Bayesian nonparametrics, structural change, real interest rate
    JEL: C11 C14 C22 C53
    Date: 2016–11–01
  3. By: Jose E. Figueroa-Lopez; K. Lee
    Abstract: High frequency based estimation methods for a semiparametric pure-jump subordinated Brownian motion exposed to a small additive microstructure noise are developed building on the two-scales realized variations approach originally developed by Zhang et. al. (2005) for the estimation of the integrated variance of a continuous Ito process. The proposed estimators are shown to be robust against the noise and, surprisingly, to attain better rates of convergence than their precursors, method of moment estimators, even in the absence of microstructure noise. Our main results give approximate optimal values for the number K of regular sparse subsamples to be used, which is an important tune-up parameter of the method. Finally, a data-driven plug-in procedure is devised to implement the proposed estimators with the optimal K-value. The developed estimators exhibit superior performance as illustrated by Monte Carlo simulations and a real high-frequency data application.
    Date: 2017–02
  4. By: F. Della Marra
    Abstract: We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson in [1], [2]. The second is based on generalized principal components and it was introduced by Forni, Hallin, Lippi and Reichlin in [3], [4]. The last model has been recently proposed by Forni, Hallin, Lippi and Zaffaroni in [5], [6]. The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a in-sample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to [7], but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.
    Keywords: Macroeconomic Forecasting, Dynamic Factor Models, Time-domain methods, Frequency-domain methods
    JEL: C0 C01 E01
    Date: 2017
  5. By: Federico Zincenko
    Abstract: This paper proposes a set of moment conditions for the estimation of lineardynamic panel data models. In the spirit of Chamberlain's (1982, 1984)approach, these conditions arise from parameterizing the relationship betweencovariates and unobserved time invariant e ffects. A GMM framework is used toderive an optimal estimator, with no efficiency loss compared to classic alternativeslike Arellano and Bond (1991) and Ahn and Schmidt (1995, 1997). Still,Monte Carlo results suggest that the new procedure peforms better than thesealternatives when covariates are non-stationary. The framework also leads to avery simple test for unobserved eff ects.
    Date: 2017–01
  6. By: Maria Putintseva (University of Zurich, Ecole Polytechnique Fédérale de Lausanne, and Swiss Finance Institute)
    Abstract: I propose a class of hybrid models to describe and predict the dynamics of a multivariate stationary random vector, e.g. a vector of stock returns. These models combine essential features of the multivariate mixture normal distribution and the conditional correlation models. I describe in detail the expectation-maximization algorithm, which makes the parameter estimation feasible and fast virtually for any random vector length. I fit the suggested models to five data sets, consisting of vectors of stock returns, with the maximal vector length of fifteen stocks. The predictive ability of this model class is compared to other widely used multivariate models, and it turns out that my models provide the best forecasts, both on average and for extreme negative returns. All necessary formulas to apply these models for important financial objectives are also provided.
    Keywords: Finite Mixtures, Dynamic Conditional Correlation, Forecasting, Multivariate Modelling, Predictive Ability
    JEL: C51 C53 G17
  7. By: Gianluca Cubadda (DEF and CEIS, University of Rome "Tor Vergata"); Barbara Guardabascio (ISTAT)
    Abstract: We examine the conditions under which each individual series that is generated by a vector autoregressive model can be represented as an autoregressive model that is augmented with the lags of few linear combinations of all the variables in the system. We call this modelling Multivariate Index-Augmented Autoregression (MIAAR). We show that the parameters of the MIAAR can be estimated by a switching algorithm that increases the Gaussian likelihood at each iteration. Since maximum likelihood estimation may perform poorly when the number of parameters gets larger, we propose a regularized version of our algorithm to handle a medium-large number of time series. We illustrate the usefulness of the MIAAR modelling both by empirical applications and simulations.
    Keywords: Multivariate index autoregressive models, reduced rank regression, dimension reduction, shrinkage estimation, macroeconomic forecasting
    JEL: C32
    Date: 2017–02–07
  8. By: Mario Bonino (Dipartimento di Matematica [Padova] - Università degli Studi di Padova [Padova]); Matteo Camelia (Dipartimento di Matematica [Padova] - Università degli Studi di Padova [Padova]); Paolo Pigato (TOSCA - TO Simulate and CAlibrate stochastic models - CRISAM - Inria Sophia Antipolis - Méditerranée - Inria - Institut National de Recherche en Informatique et en Automatique - IECL - Institut Élie Cartan de Lorraine - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, Probabilités et statistiques - IECL - Institut Élie Cartan de Lorraine - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We consider a mean-reverting stochastic volatility model which satisfies some relevant stylized facts of financial markets. We introduce an algorithm for the detection of peaks in the volatility profile, that we apply to the time series of Dow Jones Industrial Average and Financial Times Stock Exchange 100 in the period 1984-2013. Based on empirical results, we propose a bivariate version of the model, for which we find an explicit expression for the decay over time of cross-asset correlations between absolute returns. We compare our theoretical predictions with empirical estimates on the same financial time series, finding an excellent agreement.
    Keywords: Jump Detection,Cross-Correlations, Stochastic Volatility, LongMemory, Financial Time Series
    Date: 2016–12–05
  9. By: Aknouche, Abdelhakim; Bendjeddou, Sara
    Abstract: Two negative binomial quasi-maximum likelihood estimates (NB-QMLE's) for a general class of count time series models are proposed. The first one is the profile NB-QMLE calculated while arbitrarily fixing the dispersion parameter of the negative binomial likelihood. The second one, termed two-stage NB-QMLE, consists of four stages estimating both conditional mean and dispersion parameters. It is shown that the two estimates are consistent and asymptotically Gaussian under mild conditions. Moreover, the two-stage NB-QMLE enjoys a certain asymptotic efficiency property provided that a negative binomial link function relating the conditional mean and conditional variance is specified. The proposed NB-QMLE's are compared with the Poisson QMLE asymptotically and in finite samples for various well-known particular classes of count time series models such as the (Poisson and negative binomial) Integer GARCH model and the INAR(1) model. Applications to two real datasets are given.
    Keywords: Integer-valued time series models, Integer GARCH, Integer AR, Generalized Linear Models, Quasi-likelihood, Geometric QMLE, Negative Binomial QMLE, Poisson QMLE, consistency and asymptotic normality.
    JEL: C01 C13 C18 C51
    Date: 2016–12–06

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