nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒01‒01
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Panel Cointegration Testing in the Presence of Common Factors By Gengenbach,Christian; Palm,Franz C.; Urbain,Jean-Pierre
  2. Markov-switching structural vector autoregressions: theory and application By Juan Francisco Rubio-Ramírez; Daniel Waggoner; Tao Zha
  3. One-sided test for an unknown breakpoint: theory, computation, and application to monetary theory By Arturo Estrella; Anthony P. Rodrigues
  4. Tests Against Stationary and Explosive Alternatives in Vector Autoregressive Models By Ahlgren, Niklas; Nyblom, Jukka
  5. Panel Data Unit Roots Tests: The Role of Serial Correlation and the Time Dimension By Stefan De Wachter; Richard D.F. Harris; Elias Tzavalis
  6. A Testing Procedure for Determining the Number of Factors in Approximate Factor Models with Large Datasets By George Kapetanios

  1. By: Gengenbach,Christian; Palm,Franz C.; Urbain,Jean-Pierre (METEOR)
    Abstract: Panel unit root and no-cointegration tests that rely on cross-sectional independence of the panel unit experience severe size distortions when this assumption is violated, as has e.g. been shown by Banerjee, Marcellino and Osbat (2004, 2005) via Monte Carlo simulations. Several studies have recently addressed this issue for panel unit root test using a common factor structure to model the cross-sectional dependence, but not much work has been done yet for panel no-cointegration tests. This paper proposes a model for panel no-cointegration using an unobserved common factor structure, following the work on Bai and Ng (2004) for panel unit roots. The model enables us to distinguish two important cases: (i) the case when the non-stationarity in the data is driven by a reduced number of common stochastic trends, and (ii) the case where we have common and idiosyncratic stochastic trends present in the data. We study the asymptotic behavior of some existing, residual-based panel no-cointegration, as suggested by Kao (1999) and Pedroni (1999, 2004). Under the DGP used, the test statistics are no longer asymptotically normal, and convergence occurs at rate T rather than sqrt(N)T as for independent panels. We then examine the properties of residual-based tests for no-cointegration applied to defactored data from which the common factors and individual components have been extracted.
    Keywords: econometrics;
    Date: 2005
  2. By: Juan Francisco Rubio-Ramírez; Daniel Waggoner; Tao Zha
    Abstract: This paper develops a new and easily implementable necessary and sufficient condition for the exact identification of a Markov-switching structural vector autoregression (SVAR) model. The theorem applies to models with both linear and some nonlinear restrictions on the structural parameters. We also derive efficient MCMC algorithms to implement sign and long-run restrictions in Markov-switching SVARs. Using our methods, four well-known identification schemes are used to study whether monetary policy has changed in the euro area since the introduction of the European Monetary Union. We find that models restricted to only time-varying shock variances dominate the other models. We find a persistent post-1993 regime that is associated with low volatility of shocks to output, prices, and interest rates. Finally, the output effects of monetary policy shocks are small and uncertain across regimes and models. These results are robust to the four identification schemes studied in this paper.
    Date: 2005
  3. By: Arturo Estrella; Anthony P. Rodrigues
    Abstract: The econometrics literature contains a variety of two-sided tests for unknown breakpoints in time-series models with one or more parameters. This paper derives an analogous one-sided test that takes into account the direction of the change for a single parameter. In particular, we propose a sup t statistic, which is distributed as a normalized Brownian bridge. The method is illustrated by testing whether the reaction of monetary policy to inflation has increased since 1959.
    Keywords: Time-series analysis ; Monetary policy ; Inflation (Finance)
    Date: 2005
  4. By: Ahlgren, Niklas (Swedish School of Economics and Business Administration); Nyblom, Jukka (University of Joensuu)
    Abstract: The article proposes new tests for the number of unit roots in vector autoregressive models based on the eigenvalues of the companion matrix. Both stationary and explosive alternatives are considered. The limiting distributions of test statistics depend only on the number of unit roots. Size and power are investigated and it is found that the new test against stationary alternatives compares favorably with the widely used likelihood ratio test for the cointegrating rank. The powers are prominently higher against explosive than stationary alternatives. Some empirical examples are provided to show how to use the new tests with real data.
    Keywords: Asymptotic local power; Cointegration; Companion matrix; Unit root
    Date: 2005–12–14
  5. By: Stefan De Wachter (University of Oxford); Richard D.F. Harris (University of Exeter); Elias Tzavalis (Queen Mary, University of London)
    Abstract: We investigate the influence of residual serial correlation and of the time dimension on statistical inference for a unit root in dynamic longitudinal data, known as panel data in econometrics. To this end, we introduce two test statistics based on method of moments estimators. The first is based on the generalised method of moments estimators, while the second is based on the instrumental variables estimator. Analytical results for the IV based test in a simplified setting show that (i) large time dimension panel unit root tests will suffer from serious size distortions in finite samples, even for samples that would normally be considered large in practice, and (ii) negative serial correlation in the error terms of the panel reduces the power of the unit root tests, possibly up to a point where the test becomes biased. However, near the unit root the test is shown to have power against a wide range of alternatives. These findings are confirmed in a more general set-up through a series of Monte Carlo experiments.
    Keywords: Dynamic longitudinal (panel) data, Generalized method of moments, Instrumental variables, Unit roots, Moving average errors
    JEL: C22 C23
    Date: 2005–12
  6. By: George Kapetanios (Queen Mary, University of London)
    Abstract: The paradigm of a factor model is very appealing and has been used extensively in economic analyses. Underlying the factor model is the idea that a large number of economic variables can be adequately modelled by a small number of indicator variables. Throughout this extensive research activity on large dimensional factor models a major preoccupation has been the development of tools for determining the number of factors needed for modelling. This paper provides builds on the work of Kapetanios (2004) to provide an alternative method to information criteria as a tool for estimating the number of factors in large dimensional factor models. The new method is robust to considerable cross-sectional and temporal dependence. The theoretical properties of the method are explored and an extensive Monte Carlo study is undertaken. Results are favourable for the new method and suggest that it is a reasonable alternative to existing methods.
    Keywords: Factor models, Large sample covariance matrix, Maximum eigenvalue
    JEL: C12 C15 C23
    Date: 2005–12
  7. By: Tony Guida (Université de Savoie); Olivier Matringe (UNCTAD)
    Abstract: This paper examines the forecasting performance of GARCH’s models used with agricultural commodities data. We compare different possible sources of forecasting improvement, using various statistical distributions and models. We have chosen to confine our analysis on four indices which are the cocoa LIFFE continuous futures, the cocoa NYBOT continuous futures, the coffee NYBOT continuous futures and the CAC 40, the French major stock index. As one may see the sample of indices is containing a genuine stock index also. The implied goal is to find out if the GARCH models are more fitted for stock indices than for agricultural commodities. The forecasts and the predictive power are evaluated using traditional methods such as the coefficient of determination in the regression of the true variance on the predicted one. We find that agricultural commodities time series could not be used with the same methodology than the financial series. Moreover it is interesting to point out that no real “model leader” was found in this sample of commodities. Finally increased forecast performance is not solely observed using non-gaussian distribution in commodities.
    Keywords: GARCH, commodities, volatility, forecasting, risk management
    JEL: C13 C32 C53 G15
    Date: 2005–12–20

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