nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒12‒04
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Panel Estimation for Worriers By Anindya Banerjee; Markus Eberhardt; J. James Reade
  2. Forecasting with Medium and Large Bayesian VARs By Gary Koop
  3. Forecasting with mixed-frequency data By Elena Andreou; Eric Ghysels; Andros Kourtellos
  4. Weighted trimmed likelihood estimator for GARCH models By Chalabi, Yohan / Y.; Wuertz, Diethelm

  1. By: Anindya Banerjee; Markus Eberhardt; J. James Reade
    Abstract: The recent blossoming of panel econometrics in general and panel time-series methods in particular has enabled many more research questions to be investigated than before. However, this development has not assuaged serious concerns over the lack of diagnostic testing procedures in panel econometrics, in particular vis-a-vis the prominence of such practices in the time-series domain: the recent introduction of residual cross-section independence tests aside, within mainstream panel empirics the combination of ‘model’, ‘spefication’ and ‘testing’ typically refers to the distinction between fixed and random effects, as opposed to a rigorous investigation of residual properties. In this paper we investigate these issues in the context of non-stationary panels with multifactor error structure, employing Monte Carlo simulations to investigate the distributions and rejection frequencies for standard time-series diagnostic procedures, including tests for residual autocorrelation, ARCH, normality, heteroskedasticity and functional form.
    Keywords: Panel time-series, residual diagnostic, common factor model
    JEL: C12 C22 C23
    Date: 2010
  2. By: Gary Koop (University of Strathclyde; The Rimini Centre for Economic Analysis (RCEA))
    Abstract: This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We ?nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.
    Keywords: Bayesian, Minnesota prior, stochastic search variable selection, predictive likelihood
    Date: 2010–01
  3. By: Elena Andreou; Eric Ghysels; Andros Kourtellos
    Date: 2010–11
  4. By: Chalabi, Yohan / Y.; Wuertz, Diethelm
    Abstract: Generalized autoregressive heteroskedasticity (GARCH) models are widely used to reproduce stylized facts of financial time series and today play an essential role in risk management and volatility forecasting. But despite extensive research, problems are still encountered during parameter estimation in the presence of outliers. Here we show how this limitation can be overcome by applying the robust weighted trimmed likelihood estimator (WTLE) to the standard GARCH model. We suggest a fast implementation and explain how the additional robust parameter can be automatically estimated. We compare our approach with other recently introduced robust GARCH estimators and show through the results of an extensive simulation study that the proposed estimator provides robust and reliable estimates with a small computation cost. Moreover, the proposed fully automatic method for selecting the trimming parameter obviates the tedious fine tuning process required by other models to obtain a “robust” parameter, which may be appreciated by practitioners.
    Keywords: GARCH Models; Robust Estimators; Outliers; Weighted Trimmed Likelihood Estimator (WTLE); Quasi Maximum Likelihood Estimator (QMLE)
    JEL: C40
    Date: 2010–10

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