nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒08‒21
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Linking Granger Causality and the Pearl Causal Model with Settable Systems By Halbert White; Karim Chalak; Xun Lu
  2. Ten Things We Should Know About Time Series By Michael McAleer; Les Oxley
  3. A Class of Simple Distribution-Free Rank-Based Unit Root Tests (Revision of DP 2009-02) By Hallin, M.; Akker, R. van den; Werker, B.J.M.
  4. On the Design of Data Sets for Forecasting with Dynamic Factor Models By Gerhard Rünstler
  5. "Bayesian Estimation and Particle Filter for Max-Stable Processes" By Tsuyoshi Kunihama; Yasuhiro Omori; Zhengjun Zhang

  1. By: Halbert White (University of California-San Diego); Karim Chalak (Boston College); Xun Lu (Hong Kong University of Science and Technology)
    Abstract: The causal notions embodied in the concept of Granger causality have been argued to belong to a different category than those of Judea Pearl's Causal Model, and so far their relation has remained obscure. Here, we demonstrate that these concepts are in fact closely linked by showing how each relates to straightforward notions of direct causality embodied in settable systems, an extension and refinement of the Pearl Causal Model designed to accommodate optimization, equilibrium, and learning. We then provide straightforward practical methods to test for direct causality using tests for Granger causality.
    Keywords: Causal Models, Conditional Exogeneity, Conditional Independence, Granger Non-causality
    JEL: C12 C22 C32
    Date: 2010–08–01
  2. By: Michael McAleer (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University); Les Oxley (Department of Economics and Finance, University of Canterbury)
    Abstract: Time series data affect many aspects of our lives. This paper highlights ten things we should all know about time series, namely: a good working knowledge of econometrics and statistics, an awareness of measurement errors, testing for zero frequency, seasonal and periodic unit roots, analysing fractionally integrated and long memory processes, estimating VARFIMA models, using and interpreting cointegrating models carefully, choosing sensibly among univariate conditional, stochastic and realized volatility models, not confusing thresholds, asymmetry and leverage, not underestimating the complexity of multivariate volatility models, and thinking carefully about forecasting models and expertise.
    Keywords: Unit roots, fractional integration, long memory, VARFIMA, cointegration, volatility, thresholds, asymmetry, leverage, forecasting models and expertise.
    JEL: C22 C32
    Date: 2010–08
  3. By: Hallin, M.; Akker, R. van den; Werker, B.J.M. (Tilburg University, Center for Economic Research)
    Abstract: AMS 1980 subject classification : 62G10 and 62G20.
    Keywords: Unit root;Dickey-Fuller test;Local Asymptotic Normality;Rank test
    JEL: C12 C22
    Date: 2010
  4. By: Gerhard Rünstler (WIFO)
    Abstract: Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. The paper proposes to use forecast weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to forecasting euro area, German, and French GDP growth from unbalanced monthly data suggest that both forecast weights and least angle regressions result in improved forecasts. Overall, forecast weights provide yet more robust results.
    Date: 2010–07–13
  5. By: Tsuyoshi Kunihama (Graduate School of Economics, University of Tokyo); Yasuhiro Omori (Faculty of Economics, University of Tokyo); Zhengjun Zhang (Department of Statistics, University of Wisconsin Madison)
    Abstract: Extreme values are often correlated over time, for example, in a financial time series, and these values carry various risks. Max-stable processes such as maxima of moving maxima (M3) processes have been recently considered in the literature to describe timedependent dynamics, which have been difficult to estimate. This paper first proposes a feasible and efficient Bayesian estimation method for nonlinear and non-Gaussian state space models based on these processes and describes a Markov chain Monte Carlo algorithm where the sampling efficiency is improved by the normal mixture sampler. Furthermore, a unique particle filter that adapts to extreme observations is proposed and shown to be highly accurate in comparison with other well-known filters. Our proposed algorithms were applied to daily minima of high-frequency stock return data, and a model comparison was conducted using marginal likelihoods to investigate the time-dependent dynamics in extreme stock returns for financial risk management.
    Date: 2010–08

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