nep-ets New Economics Papers
on Econometric Time Series
Issue of 2008‒12‒01
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood By Per Frederiksen; Frank S. Nielsen
  2. Fractional integration and data frequency By Luis A. Gil-Alana; Guglielmo M. Caporale
  3. Regime switching models of hedge fund returns By Szabolcs Blazsek; Anna Downarowitz
  4. Long run and cyclical strong dependence in macroeconomic time series. Nelson and Plosser revisited By Luis A. Gil-Alana
  5. Time trend estimation with breaks in temperature time series By Luis A. Gil-Alana
  6. Optimizing Time-series Forecasts for Inflation and Interest Rates Using Simulation and Model Averaging By Jumah, Adusei; Kunst, Robert M.
  7. Large Bayesian VARs. By Marta Bańbura; Domenico Giannone; Lucrezia Reichlin
  8. Modeling autoregressive conditional skewness and kurtosis with Multi-quantile CAViaR. By Halbert White; Tae-Hwan Kim; Simone Manganelli

  1. By: Per Frederiksen; Frank S. Nielsen (School of Economics and Management, University of Aarhus, Denmark)
    Abstract: In this paper, we propose new tests for long memory in stationary and nonstationary time series possibly perturbed by short-run noise which may be serially correlated. The tests are all based on semiparametric estimators and exploit the self-similarity property of long memory processes. We o¤er simulation results that show good size properties of the tests, with power against spurious long memory. An empirical study of daily log-squared returns series of exchange rates and DJIA30 stocks shows that indeed there is long memory in exchange rate volatility and stock return volatility.
    Keywords: Temporal aggregation, semiparametric estimation, fractional integration, self-similarity, perturbed fractional processes.
    JEL: C14 C22 C43
    Date: 2008–11–25
  2. By: Luis A. Gil-Alana (Facultad de Ciencias Económicas y Empresariales, Universidad de Navarra); Guglielmo M. Caporale (Brunel University, London, England)
    Abstract: This paper examines the robustness of fractional integration estimates to different data frequencies. We show by means of Monte Carlo experiments that if the number of differences is an integer value (e.g., 0 or 1) there is no distortion when data are collected at wider intervals; however, if it is a fractional value, the distortion increases as the number of periods between the observations increases, which results in lower orders of integration than those of the true DGP. An empirical application using the S&P500 index is also carried out.
    Date: 2008–11–20
  3. By: Szabolcs Blazsek (Department of Business, Universidad de Navarra); Anna Downarowitz (Instituto de Empresa)
    Abstract: We estimate and compare the forecasting performance of several dynamic models of returns of different hedge fund strategies. The conditional mean of return is an ARMA process while its conditional volatility is modeled according to the GARCH specification. In order to take into account the high level of risk of these strategies, we also consider a Markov switching structure of the parameters in both equations to cap ture jumps. Finally, the one-step-ahead out-of-sample forecast performance of different models is compared.
    Keywords: Markov switching ARMA-GARCH, forecasting performance
    JEL: C13 C15 G32
    Date: 2008–11–26
  4. By: Luis A. Gil-Alana (Facultad de Ciencias Económicas y Empresariales, Universidad de Navarra)
    Abstract: This paper deals with the presence of long range dependence at the long run and the cyclical frequencies in macroeconomic time series. We use a procedure that allows us to test unit roots with fractional orders of integration in raw time series. The tests are applied to an extended version of Nelson and Plosser’s (1982) dataset, and the results show that, though the classic unit root hypothesis cannot be rejected in most of the series, fractional degrees of integration at both the zero and the cyclical frequencies are plausible alternatives in some cases. Additionally, the root at the zero frequency seems to be more important than the cyclical one for all series, implying that shocks affecting the long run are more persistent than those affecting the cyclical part. The results are consistent with the empirical fact observed in many macroeconomic series that the long-term evolution is nonstationary, while the cyclical component is stationary.
    Date: 2008–11–25
  5. By: Luis A. Gil-Alana (Facultad de Ciencias Económicas y Empresariales, Universidad de Navarra)
    Abstract: This paper deals with the modelling of the global and northern and southern hemispheric anomaly temperature time series using a novel technique based on segmented trends and fractional integration. We use a procedure that permits us to estimate linear time trends and orders of integration at various subsamples, where the periods for the changing trends are endogenously determined by the model. Moreover, we use a non-parametric approach (Bloomfield, 1973) for modelling the I(0) deviation term. The results show that the three series (global, northern and southern temperatures) can be well described in terms of fractional integration with the orders of integration around 0.5 in the three cases. The coefficients associated to the time trends are statistically significant in all subsamples for the three series, especially during the final part of the sample, giving then some support to the global warming theories.
    Date: 2008–11–20
  6. By: Jumah, Adusei (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria, and Department of Economics, University of Vienna, Vienna, Austria); Kunst, Robert M. (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria, and Department of Economics, University of Vienna, Vienna, Austria)
    Abstract: Motivated by economic-theory concepts—the Fisher hypothesis and the theory of the term structure—we consider a small set of simple bivariate closed-loop time-series models for the prediction of price inflation and of long- and short-term interest rates. The set includes vector autoregressions (VAR) in levels and in differences, a cointegrated VAR, and a non-linear VAR with threshold cointegration based on data from Germany, Japan, UK, and the U.S. Following a traditional comparative evaluation of predictive accuracy, we subject all structures to a mutual validation using parametric bootstrapping. Ultimately, we utilize the recently developed technique of Mallows model averaging to explore the potential of improving upon the predictions through combinations. While the simulations confirm the traded wisdom that VARs in differences optimize one-step prediction and that error correction helps at larger horizons, the model-averaging experiments point at problems in allotting an adequate penalty for the complexity of candidate models.
    Keywords: Threshold cointegration, Parametric bootstrap, Model averaging
    JEL: C32 C52 E43 E47
    Date: 2008–11
  7. By: Marta Bańbura (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.); Domenico Giannone (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.); Lucrezia Reichlin (London Business School, Regents Park, London NW1 4SA, United Kingdom.)
    Abstract: This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting performance of small monetary VARs can be improved by adding additional macroeconomic variables and sectoral information. In addition, we show that large VARs with shrinkage produce credible impulse responses and are suitable for structural analysis. JEL Classification: C11, C13, C33, C53.
    Keywords: Bayesian VAR, Forecasting, Monetary VAR, large cross-sections.
    Date: 2008–11
  8. By: Halbert White (Department of Economics, University of California, San Diego 9500 Gilman Drive 0508, La Jolla, California 92093-0508, USA.); Tae-Hwan Kim (School of Economics, University of Nottingham, University Park Nottingham NG7 2RD, U.K..); Simone Manganelli (European Central Bank, DG-Research, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Abstract: Engle and Manganelli (2004) propose CAViaR, a class of models suitable for estimating conditional quantiles in dynamic settings. Engle and Manganelli apply their approach to the estimation of Value at Risk, but this is only one of many possible applications. Here we extend CAViaR models to permit joint modeling of multiple quantiles, Multi-Quantile (MQ) CAViaR. We apply our new methods to estimate measures of conditional skewness and kurtosis defined in terms of conditional quantiles, analogous to the unconditional quantile-based measures of skewness and kurtosis studied by Kim and White (2004). We investigate the performance of our methods by simulation, and we apply MQ-CAViaR to study conditional skewness and kurtosis of S&P 500 daily returns. JEL Classification: C13, C32.
    Keywords: Asset returns, CAViaR, conditional quantiles, dynamic quantiles, kurtosis, skewness.
    Date: 2008–11

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