nep-ets New Economics Papers
on Econometric Time Series
Issue of 2011‒05‒14
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. A comparison of forecasting procedures for macroeconomic series: the contribution of structural break models By BAUWENS, Luc; KOOP, Gary; KOROBILIS, Dimitris; ROMBOUTS, Jeroen V. K.
  2. Nonparametric Beta kernel estimator for long memory time series By BOUEZMARNI, Taoufik; VAN BELLEGEM, Sébastien
  3. MCMC Estimation of Extended Hodrick-Prescott (HP) Filtering Models By Wolfgang Polasek
  4. How Computational Statistics Became the Backbone of Modern Data Science By James E. Gentle; Wolfgang Karl Härdle; Yuichi Mori

  1. By: BAUWENS, Luc (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium); KOOP, Gary (University of Strathclyde, U.K); KOROBILIS, Dimitris (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium); ROMBOUTS, Jeroen V. K. (Institute of Applied Economics at HEC Montréal, CIRANO, CIRPEE, Canada; Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium.)
    Abstract: This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.
    Keywords: forecasting, change-points, Markov switching, Bayesian inference
    JEL: C11 C22 C53
    Date: 2010–12–01
  2. By: BOUEZMARNI, Taoufik (University of Sherbrooke, Canada); VAN BELLEGEM, Sébastien (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium; Toulouse School of Economics, France)
    Abstract: The paper introduces a new nonparametric estimator of the spectral density that is given in smoothing the periodogram by the probability density of Beta random variable (Beta kernel). The estimator is proved to be bounded for short memory data, and diverges at the origin for long memory data. The convergence in probability of the relative error and Monte Carlo simulations suggest that the estimator automaticaly adapts to the long- or the short-range dependency of the process. A cross-validation procedure is also studied in order to select the nuisance parameter of the estimator. Illustrations on historical as well as most recent returns and absolute returns of the S&P500 index show the reasonable performance of the estimation, and show that the data-driven estimator is a valuable tool for the detection of long-memory as well as hidden periodicities in stock returns.
    Keywords: spectral density, long range dependence, nonparametric estimation, periodogram, kernel smoothing, Beta kernel, cross-validation
    JEL: C14 C22
    Date: 2011–01–01
  3. By: Wolfgang Polasek (Institute for Advanced Studies, Austria; University of Porto, Portugal; The Rimini Centre for Economic Analysis (RCEA), Italy)
    Abstract: The Hodrick-Prescott (HP) method was originally developed to smooth time series, i.e. to get a smooth (long-term) component. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior for the smoothness component. Extending this Bayesian approach in a linear model set-up is possible by a conjugate and a non-conjugate model using MCMC. The Bayesian HP smoothing model is also extended to a spatial smoothing model. We have to define spatial neighbors for each observation and we can use in a similar way a smoothness prior as for the HP filter in time series. The new smoothing approaches are applied to the (textbook) airline passenger data for time series and to the problem of smoothing spatial regional data. This new approach can be used for a new class of model-based smoothers for time series and spatial models.
    Keywords: Hodrick-Prescott (HP) smoothers, Spatial econometrics, MCMC estimation, Airline passenger time series, Spatial smoothing of regional data, NUTS: nomenclature of territorial units for statistics
    JEL: C11 C15 C52 E17 R12
    Date: 2011–05
  4. By: James E. Gentle; Wolfgang Karl Härdle; Yuichi Mori
    Abstract: This first chapter serves as an introduction and overview for a collection of articles surveying the current state of the science of computational statistics. Earlier versions of most of these articles appeared in the first edition of Handbook of Computational Statistics: Concepts and Methods, published in 2004. There have been advances in all of the areas of computational statistics, so we feel that it is time to revise and update this Handbook. This introduction is a revision of the introductory chapter of the first edition.
    Keywords: Discrete time series models, continuous time diffusion models, models with jumps, stochastic volatility, GARCH
    JEL: C15
    Date: 2011–05

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