nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒10‒14
eleven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Cointegration Tests of PPP: Do they also Exhibit Erratic Behaviour? By Guglielmo Maria Caporale; Christoph Hanck
  2. Analysis of panel vector error correction models using maximum likelihood, the bootstrap, and canonical-correlation estimators By Richard G. Anderson; Hailong Qian; Robert H. Rasche
  3. Forecasting inflation and output: comparing data-rich models with simple rules By William T. Gavin; Kevin L. Kliesen
  4. How to advance theory with structural VARs: use the Sims-Cogley-Nason approach By Patrick J. Kehoe
  5. Subsampling realised kernels By Ole E. Barndorff-Nielsen; Peter Reinhard Hansen; Asger Lunde; Neil Shephard
  6. A New Approach to Factor Vector Autoregressive Estimation with an Application to Large-Scale Macroeconometric Modelling By Fabio C. Bagliano; Claudio Morana
  7. Dynamic Factor GARCH: Multivariate Volatility Forecast for a Large Number of Series By Lucia Alessi; Matteo Barigozzi; Marco Capasso
  8. Constructing Optimal Tests on a Lagged Dependent Variable By Patrick Marsh
  9. Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison By Jun Yu; Renate Meyer
  10. Finite sample effects of pure seasonal mean shifts on Dickey-Fuller tests By B. da Silva Lopes, Artur C.
  11. Is there Really a Unit Root in the Inflation Rate? More Evidence from Panel Data Models By Basher, Syed A.; Westerlund, Joakim

  1. By: Guglielmo Maria Caporale; Christoph Hanck
    Abstract: We analyse whether tests of PPP exhibit erratic behaviour (as previously reported by Caporale et al., 2003) even when (possibly unwarranted) homogeneity and proportionality restrictions are not imposed, and trivariate cointegration (stage-three) tests between the nominal exchange rate, domestic and foreign price levels are carried out (instead of stationarity tests on the real exchange rate, as in stage-two tests). We examine the US dollar real exchange rate vis-à-vis 21 other currencies over a period of more than a century, and find that stage-three tests produce similar results to those for stage-two tests, namely the former also behave erratically. This confirms that neither of these traditional approaches to testing for PPP can solve the issue of PPP.
    Keywords: Purchasing Power Parity (PPP), real exchange rate, cointegration, stationarity, parameter instability
    JEL: C12 C22 F31
    Date: 2006
  2. By: Richard G. Anderson; Hailong Qian; Robert H. Rasche
    Abstract: In this paper, we examine the use of Box-Tiao*s (1977) canonical correlation method as an alternative to likelihood-based inferences for vector error-correction models. It is now well-known that testing of cointegration ranks based on Johansen*s (1995) ML-based method suffers from severe small sample size distortions. Furthermore, the distributions of empirical economic and financial time series tend to display fat tails, heteroskedasticity and skewness that are inconsistent with the usual distributional assumptions of likelihood-based approach. The testing statistic based on Box-Tiao*s canonical correlations shows promise as an alternative to Johansen*s ML-based approach for testing of cointegration rank in VECM models.
    Keywords: Econometric models ; Panel analysis
    Date: 2006
  3. By: William T. Gavin; Kevin L. Kliesen
    Abstract: Decision makers, both public and private, use forecasts of economic growth and inflation to make plans and implement policies. In many situations, reasonably good forecasts can be made with simple rules of thumb that are extrapolations of a single data series. In principle, information about other economic indicators should be useful in forecasting a particular series like inflation or output. Including too many variables makes a model unwieldy and not including enough can increase forecast error. A key problem is deciding which other series to include. Recently, studies have shown that Dynamic Factor Models (DFMs) may provide a general solution to this problem. The key is that these models use a large data set to extract a few common factors (thus, the term #data-rich*). This paper uses a monthly DFM model to forecast inflation and output growth at horizons of 3, 12 and 24 months ahead. These forecasts are then compared to simple forecasting rules.
    Keywords: Inflation (Finance) ; Forecasting
    Date: 2006
  4. By: Patrick J. Kehoe
    Abstract: The common approach to evaluating a model in the structural VAR literature is to compare the impulse responses from structural VARs run on the data to the theoretical impulse responses from the model. The Sims-Cogley-Nason approach instead compares the structural VARs run on the data to identical structural VARs run on data from the model of the same length as the actual data. Chari, Kehoe, and McGrattan (2006) argue that the inappropriate comparison made by the common approach is the root of the problems in the SVAR literature. In practice, the problems can be solved simply. Switching from the common approach to the Sims-Cogley-Nason ap-proach basically involves changing a few lines of computer code and a few lines of text. This switch will vastly increase the value of the structural VAR literature for economic theory.
    Date: 2006
  5. By: Ole E. Barndorff-Nielsen (University of Aarhus); Peter Reinhard Hansen (Stanford University); Asger Lunde (Aarhus School of Business); Neil Shephard (Nuffield College, University of Oxford)
    Abstract: In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our analysis, looking at the class of subsampled realised kernels and we derive the limit theory for this class of estimators. We find that subsampling is highly advantageous for estimators based on discontinuous kernels, such as the truncated kernel. For kinked kernels, such as the Bartlett kernel, we show that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled realised kernels in simulations and in empirical work.
    Keywords: Bipower variation; Long run variance estimator; Market frictions; Quadratic variation; Realised kernel; Realised variance; Subsampling.
    JEL: C13 C22
    Date: 2006–08–20
  6. By: Fabio C. Bagliano; Claudio Morana
    Abstract: In this paper a new approach to factor vector autoregressive estimation, based on Stock and Watson (2005), is introduced. Relative to the Stock-Watson approach, the proposed method has the advantage of allowing for a more clear-cut interpretation of the global factors, as well as for the identi.cation of all idiosyncratic shocks. Moreover, it shares with the Stock-Watson approach the advantage of using an iterated procedure in estimation, recovering, asymptotically, full effciency, and also allowing the imposition of appropriate restrictions concerning the lack of Granger causality of the variables versus the factors. Finally, relative to other available methods, our modelling approach has the advantage of allowing for the joint modelling of all variables, without resorting to long-run forcing hypotheses. An application to large-scale macroeconometric modelling is also provided.
    Keywords: dynamic factor models, vector autoregressions, principal components analysis.
    JEL: C32 G1 G15
    Date: 2006
  7. By: Lucia Alessi; Matteo Barigozzi; Marco Capasso
    Abstract: We propose a new method for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model. We assume that the dynamic common factors are conditionally heteroskedastic. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series; it also provides a first identification and estimation of the dynamic factors governing the data set. A time-varying correlation GARCH model applied on the estimated dynamic factors finds the parameters governing their covariances’ evolution. Then a modified version of the Kalman filter gets a more precise estimation of the static and dynamic factors’ in-sample levels and covariances. A method is suggested for predicting conditional out-of-sample variances and covariances of the original data series. Finally, we carry out an empirical application aiming at comparing volatility forecasting results of our Dynamic Factor GARCH model against the univariate GARCH.
    Keywords: Dynamic Factors, Multivariate GARCH, Covolatility Forecasting
    Date: 2006–10–02
  8. By: Patrick Marsh
    Abstract: Via the leading unit root case, the problem of testing on a lagged dependent variable is characterized by a nuisance parameter which is present only under the alternative (see Andrews and Ploberger (1994)). This has proven a barrier to the construction of optimal tests. Moreover, in their absence it is impossible to objectively assess the absolute power properties of existing tests. Indeed, feasible tests based upon the optimality criteria used here are found to have numerically superior power properties to both the original Dickey and Fuller (1981) statistics and the efficient detrended versions suggested by Elliott, Rothenberg and Stock (1996) and analysed in Burridge and Taylor (2000).
    Keywords: Nuisance parameter, invariant test, unit root
    Date: 2006–10
  9. By: Jun Yu (School of Economics and Social Sciences, Singapore Management University); Renate Meyer (University of Auckland)
    Abstract: In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS. Moreover, we introduce to the literature several new specifications which are natural extensions to certain existing models, one of which allows for time varying correlation coefficients. Ideas are illustrated by fitting, to a bivariate time series data of weekly exchange rates, nine multivariate SV models, including the specifications with Granger causality in volatility, time varying correlations, heavytailed error distributions, additive factor structure, and multiplicative factor structure. Empirical results suggest that the most adequate specifications are those that allow for time varying correlation coefficients.
    Keywords: Multivariate stochastic volatility; Granger causality in volatility; Heavy-tailed distributions; Time varying correlations; Factors; MCMC; DIC.
    JEL: C11 C15 C30 G12
    Date: 2004–11
  10. By: B. da Silva Lopes, Artur C.
    Abstract: In this paper it is demonstrated by simulation that, contrary to a widely held belief, pure seasonal mean shifts - i.e., seasonal structural breaks which affect only the deterministic seasonal cycle - really do matter for Dickey-Fuller long-run unit root tests.
    Keywords: unit roots; seasonality; Dickey-Fuller tests; structural breaks
    JEL: C5 C22
    Date: 2005–10–15
  11. By: Basher, Syed A.; Westerlund, Joakim
    Abstract: Time series unit root evidence suggests that inflation is nonstationary. By contrast, when using more powerful panel unit root tests, Culver and Papell (1997) find that inflation is stationary. In this paper, we test the robustness of this result by applying a battery of recent panel unit root tests. The results suggest that the stationarity of inflation holds even after controlling for crosssectional dependence and structural change.
    Keywords: Unit Root; Inflation; Cross-Sectional Dependence; Structural Change.
    JEL: C32 E31 C33
    Date: 2006

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