nep-ets New Economics Papers
on Econometric Time Series
Issue of 2009‒10‒24
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Nuisance parameters, composite likelihoods and a panel of GARCH models By Cavait Pakel; Neil Shephard; Kevin Sheppard
  2. A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series By George Monokroussos
  3. On the Use of Density Forecasts to Identify Asymmetry in Forecasters¡¯ Loss Functions By Kajal Lahiri; Fushang Liu
  4. Structural Time Series Models and the Kalman Filter: a concise review By Jalles, Joao Tovar
  5. Evaluation of Nonlinear time-series models for real-time business cycle analysis of the Euro. By Monica Billio; Laurent Ferrara; Dominique Guegan; Gian Luigi Mazzi
  6. "Moment-Based Estimation of Smooth Transition Regression Models with Endogenous Variables" By Waldyr Dutra Areosa; Michael McAleer; Marcelo C. Medeiros
  7. Mean Shift detection under long-range dependencies with ART By Willert, Juliane

  1. By: Cavait Pakel; Neil Shephard; Kevin Sheppard
    Abstract: We investigate the properties of the composite likelihood (CL) method for (T x NT) GARCH panels. The defining feature of a GARCH panel with time series length T is that, while nuisance parameters are allowed to vary across NT series, other parameters of interest are assumed to be common. CL pools information across the panel instead of using information available in a single series only. Simulations and empirical analysis illustate that in reasonably large T CL performs well. However, due to the estimation error introduced through nuisance parameter estimation, CL is subject to the “incidental parameter” problem for small T.
    JEL: C14 C32
    Date: 2009
  2. By: George Monokroussos
    Abstract: Estimating Limited Dependent Variable Time Series models through standard extremum methods can be a daunting computational task because of the need for integration of high order multiple integrals and/or numerical optimization of difficult objective functions. This paper proposes a classical Markov Chain Monte Carlo (MCMC) estimation technique with data augmentation that overcomes both of these problems. The asymptotic properties of the proposed estimator are established. Furthermore, a practical and flexible algorithmic framework for this class of models is proposed and is illustrated using simulated data, thus also offering some insight into the small-sample biases of such estimators. Finally, the versatility of the proposed framework is illustrated with an application of a dynamic tobit model for the Open Market Desk's Daily Reaction Function.
    Date: 2009
  3. By: Kajal Lahiri; Fushang Liu
    Abstract: Abstract: We consider how to use information from reported density forecasts from surveys to identify asymmetry in forecasters¡¯ loss functions. We show that, for the three common loss functions - Lin-Lin, Linex, and Quad-Quad - we can infer the direction of loss asymmetry by just comparing point forecasts and the central tendency (mean or median) of the underlying density forecasts. If we know the entire distribution of the density forecast, we can calculate the loss function parameters based on the first order condition of forecast optimality. This method is applied to forecasts for annual real output growth and inflation obtained from the Survey of Professional Forecasters (SPF). We find that forecasters treat underprediction of real output growth more dearly than overprediction, reverse is true for inflation.
    Date: 2009
  4. By: Jalles, Joao Tovar
    Abstract: The continued increase in availability of economic data in recent years and, more impor- tantly, the possibility to construct larger frequency time series, have fostered the use (and development) of statistical and econometric techniques to treat them more accurately. This paper presents an exposition of structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components. In addition to a detailled analysis of univariate speci?cations we also address the SUTSE multivariate case and the issue of cointegration. Finally, the recursive estimation and smoothing by means of the Kalman ?lter algorithm is described taking into account its di¤erent stages, from initialisation to parameter?s estimation. JEL codes: C10, C22, C32
    Date: 2009
  5. By: Monica Billio (University Ca' Foscari of Venice); Laurent Ferrara (Banque de France); Dominique Guegan (Paris School of Economics - Centre d'Economie de la Sorbonne); Gian Luigi Mazzi (Eurostat)
    Abstract: In this paper, we aim at assessing Markov-switching and threshold models in their ability to identify turning points of economic cycles. By using vintage data that are updated on a monthly basis, we compare their ability to detect ex-post the occurrence of turning points of the classical business cycle, we evaluate the stability over time of the signal emitted by the models and assess their ability to detect in real-time recession signals. In this respect, we have built an historical vintage database for the Euro area going back to 1970 for two monthly macroeconomic variables of major importance for short-term economic outlook, namely the Industrial Production Index and the Unemployment Rate.
    Keywords: Business cycle, Euro zone, Markov switching model, SETAR mpdel, unemployment, industrial production.
    JEL: C22 C52
    Date: 2009–08
  6. By: Waldyr Dutra Areosa (Department of Economics, Pontifical Catholic University of Rio de Janeiro and Banco Central do Brasil); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo); Marcelo C. Medeiros (Department of Economics Pontifical Catholic University of Rio de Janeiro)
    Abstract: Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, Smooth Transition Regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discuss moment based methods for estimating STR models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated by a straightforward application of existing results in the literature.
    Date: 2009–09
  7. By: Willert, Juliane
    Abstract: Atheoretical regression trees (ART) are applied to detect changes in the mean of a stationary long memory time series when location and number are unknown. It is shown that the BIC, which is almost always used as a pruning method, does not operate well in the long memory framework. A new method is developed to determine the number of mean shifts. A Monte Carlo Study and an application is given to show the performance of the method.
    Keywords: long memory; mean shift; regression tree; ART; BIC
    JEL: C14 C22
    Date: 2009–07–06

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