nep-ets New Economics Papers
on Econometric Time Series
Issue of 2009‒07‒17
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. A Sequential Procedure to Determine the Number of Breaks in Trend with an Integrated or Stationary Noise Component By Mohitosh Kejriwal; Pierre Perron
  2. Forecasting with Factor-Augmented Error Correction Models By Anindya Banerjee; Massimiliano Marcellino; Igor Masten
  3. New Information Response Functions. By Jardet, C.; Monfort, A.; Pegoraro, F.
  4. Testing for a break in persistence under long-range dependencies and mean shifts By Sibbertsen, Philipp; Willert, Juliane

  1. By: Mohitosh Kejriwal (Krannert School of Management, Purdue University); Pierre Perron (Economics Department, Boston University)
    Abstract: Perron and Yabu (2008) consider the problem of testing for a break occuring at an unknown date in the trend function of a univariate time series when the noise component can be either stationary or integrated. This paper extends their work by proposing a sequential test that allows one to test the null hypothesis of, say, l breaks, versus the alternative hypothesis of (l+1) breaks. The test enables consistent estimation of the number of breaks. In both stationary and integrated cases, it is shown that asymptotic critical values can be obtained from the relevant quantiles of the limit distribution of the test for a single break. Monte Carlo simulations suggest that the procedure works well in finite samples.
    Keywords: Structural Change, Sequential Procedure, Feasible GLS, Unit Root, Structural Breaks
    JEL: C22
    Date: 2009–02
  2. By: Anindya Banerjee; Massimiliano Marcellino; Igor Masten
    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 differences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simula- tions and several empirical applications. We show that relative to the FAVAR, FECM generally offers 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
    JEL: C32 E17
    Date: 2009–06
  3. By: Jardet, C.; Monfort, A.; Pegoraro, F.
    Abstract: We propose a new methodology for the analysis of impulse response functions in VAR or VARMA models. More precisely, we build our results on the non ambiguous notion of innovation of a stochastic process and we consider the impact of any kind of new information at a given date $t$ on the future values of the process. This methodology allows to take into account qualitative or quantitative information, either on the innovation or on the future responses, as well as informations on filters. We show, among other results, that our approach encompasses several standard methodologies found in the literature, such as the orthogonalization of shocks (Sims (1980)), the "structural" identification of shocks (Blanchard and Quah (1989)), the "generalized" impulse responses (Pesaran and Shin (1998)) or the impulse vectors (Uhlig (2005)).
    Keywords: Impulse response functions ; innovation ; new information.
    JEL: C10 C32
    Date: 2009
  4. By: Sibbertsen, Philipp; Willert, Juliane
    Abstract: We show that the CUSUM-squared based test for a change in persistence by Leybourne et al. (2007) is not robust against shifts in the mean. A mean shift leads to serious size distortions. Therefore, adjusted critical values are needed when it is known that the data generating process has a mean shift. These are given for the case of one mean break. Response curves for the critical values are derived and a Monte Carlo study showing the size and power properties under this general de-trending is given
    Keywords: Break in persistence, long memory, structural break, level shift
    JEL: C12 C22
    Date: 2009–07

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