nep-ets New Economics Papers
on Econometric Time Series
Issue of 2009‒07‒11
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Stochastic Volatility and DSGE Models By Martin M. Andreasen
  2. An I(2) Cointegration Model with Piecewise Linear Trends: Likelihood Analysis and Application By Takamitsu Kurita; Heino Bohn Nielsen; Anders Rahbek
  3. Long memory in stock market volatility and the volatility-in-mean effect: the FIEGARCH-M model By Bent Jesper Christensen; Morten Ørregaard Nielsen; Jie Zhu
  4. Forecasting with Factor-augmented Error Correction Models By Igor Masten; Massimiliano Marcellino; Anindya Banerjeey
  5. Real-time density forecasts from VARs with stochastic volatility By Todd E. Clark

  1. By: Martin M. Andreasen (Bank of England and CREATES)
    Abstract: This paper argues that a specification of stochastic volatility commonly used to analyze the Great Moderation in DSGE models may not be appropriate, because the level of a process with this specification does not have conditional or unconditional moments. This is unfortunate because agents may as a result expect productivity and hence consumption to be inifinite in all future periods. This observation is followed by three ways to overcome the problem.
    Keywords: Great Moderation, Productivity shocks, and Time-varying coe¢ cients
    JEL: E10 E30
    Date: 2009–07–07
  2. By: Takamitsu Kurita (Faculty of Economics, Fukuoka University); Heino Bohn Nielsen (Department of Economics, University of Copenhagen); Anders Rahbek (Department of Economics, University of Copenhagen and CREATES)
    Abstract: This paper presents likelihood analysis of the I(2) cointegrated vector autoregression with piecewise linear deterministic terms. Limiting behavior of the maximum likelihood estimators are derived, which is used to further derive the limiting distribution of the likelihood ratio statistic for the cointegration ranks, extending the result for I(2) models with a linear trend in Nielsen and Rahbek (2007) and for I(1) models with piecewise linear trends in Johansen, Mosconi, and Nielsen (2000). The provided asymptotic theory extends also the results in Johansen, Juselius, Frydman, and Goldberg (2009) where asymptotic inference is discussed in detail for one of the cointegration parameters. To illustrate, an empirical analysis of US consumption, income and wealth, 1965 - 2008, is performed, emphasizing the importance of a change in nominal price trends after 1980.
    Keywords: Cointegration, I(2), Piecewise linear trends, Likelihood analysis, US consumption
    JEL: C32
    Date: 2009–07–06
  3. By: Bent Jesper Christensen (University of Aarhus and CREATES); Morten Ørregaard Nielsen (Queen's University and CREATES); Jie Zhu (University of Aarhus and CREATES)
    Abstract: We extend the fractionally integrated exponential GARCH (FIEGARCH) model for daily stock return data with long memory in return volatility of Bollerslev and Mikkelsen (1996) by introducing a possible volatility-in-mean effect. To avoid that the long memory property of volatility carries over to returns, we consider a filtered FIEGARCH-in-mean (FIEGARCH-M) effect in the return equation. The filtering of the volatility-in-mean component thus allows the co-existence of long memory in volatility and short memory in returns. We present an application to the daily CRSP value-weighted cum-dividend stock index return series from 1926 through 2006 which documents the empirical relevance of our model. The volatility-in-mean effect is significant, and the FIEGARCH-M model outperforms the original FIEGARCH model and alternative GARCH-type specifications according to standard criteria.
    Keywords: FIEGARCH, financial leverage, GARCH, long memory, risk-return tradeoff, stock returns, volatility feedback
    JEL: C22
    Date: 2009–06
  4. By: Igor Masten; Massimiliano Marcellino; Anindya Banerjeey
    Abstract: As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter's specification in di¤erences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that relative to the FAVAR, FECM generally o¤ers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.
    Keywords: Forecasting, Dynamic Factor Models, Error Correction Models, Cointegration, Factor-augmented Error Correction Models, FAVAR
    Date: 2009–06–25
  5. By: Todd E. Clark
    Abstract: Central banks and other forecasters have become increasingly interested in various aspects of density forecasts. However, recent sharp changes in macroeconomic volatility such as the Great Moderation and the more recent sharp rise in volatility associated with greater variation in energy prices and the deep global recession pose significant challenges to density forecasting. Accordingly, this paper examines, with real-time data, density forecasts of U.S. GDP growth, unemployment, inflation, and the federal funds rate from VAR models with stochastic volatility. The model of interest extends the steady state prior BVAR of Villani (2009) to include stochastic volatility, because, as found in some prior work and this paper, incorporating informative priors on the steady states of the model variables often improves the accuracy of point forecasts. The evidence presented in the paper shows that adding stochastic volatility to the BVAR with a steady state prior materially improves the real-time accuracy of point and density forecasts.
    Date: 2009

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