nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒07‒15
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Estimating Stochastic Volatility Models using Prediction-based Estimating Functions By Asger Lunde; Anne Floor Brix
  3. Co-summability from linear to non-linear cointegration By Vanessa Berenguer Rico; Jesús Gonzalo
  4. Outliers & predicting time series: A comparative study By Ardelean, Vlad; Pleier, Thomas

  1. By: Asger Lunde (Aarhus University and CREATES); Anne Floor Brix (Aarhus University and CREATES)
    Abstract: In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared to the performance of the GMM estimator based on conditional moments of integrated volatility from Bollerslev and Zhou (2002). The case where the observed log-price process is contaminated by i.i.d. market microstructure (MMS) noise is also investigated. First, the impact of MMS noise on the parameter estimates from the two estimation methods without noise correction are studied. Second, a noise robust GMM estimator is constructed by approximating integrated volatility by a realized kernel instead of realized variance. The PBEFs are also recalculated in the noise setting, and the two estimation methods ability to correctly account for the noise are investigated. Our Monte Carlo study shows that the estimator based on PBEFs outperforms the GMM estimator, both in the setting with and without MMS noise. Finally, an empirical application investigates the possible challenges and general performance of applying the PBEF based estimator in practice.
    Keywords: GMMestimation, Heston model, high-frequency data, integrated volatility, market microstructure noise, prediction-based estimating functions, realized variance, realized kernel
    JEL: C13 C22 C51
    Date: 2013–02–07
  2. By: Gabriele Fiorentini (Università di Firenze); Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros)
    Abstract: We derive computationally simple and intuitive expressions for score tests of neglected serial correlation in common and idiosyncratic factors in dynamic factor models using frequency domain techniques. The implied time domain orthogonality conditions are analogous to the conditions obtained by treating the smoothed estimators of the innovations in the latent factors as if they were observed, but they account for their final estimation errors. Monte Carlo exercises confirm the finite sample reliability and power of our proposed tests. Finally, we illustrate their empirical usefulness in an application that constructs a monthly coincident indicator for the US from four macro series.
    Keywords: Kalman filter, LM tests, Spectral maximum likelihood, Wiener-Kolmogorov filter.
    JEL: C32 C38 C52 C12 C13
    Date: 2013–06
  3. By: Vanessa Berenguer Rico; Jesús Gonzalo
    Abstract: While co-integration theory is an ideal framework to study linear relationships among persistent economic time series, the intrinsic linearity in the concepts of integration and co-integration makes it unsuitable to study non-linear long run relations among persistent processes. This drawback hinders the empirical analysis of modern macroeconomics, which often addresses asymmetric responses to policy interventions, multiplicity of equilibria, transitions between regimes or polynomial approximations to unknown functions. In this paper, to cope with non-linear relations and consequently to generalise co-integration, we formalise the idea of co-summability. It is built upon the concept order of summability developed by Berenguer-Rico and Gonzalo (2013), which, in turn, was conceived to address non-linear transformations of persistent processes. Theoretically, a co-summable relationship is balanced -in terms of the variables involved having the same order of summability- and describes a long run equilibrium that can be non-linear -in the sense that the errors have a lower order of summability. To test for these types of equilibria, inference tools for balancedness and cosummability are designed and their asymptotic properties are analysed. Their finite sample performance is studied via Monte Carlo experiments. The practical strength of co-summability theory is shown through two empirical applications. Specifically, asymmetric preferences of central bankers and the environmental Kuznets curve hypothesis are studied through the lens of co-summability.
    Keywords: Balancedness, Co-integration, Co-summability, Non-linear co-integration, Non-linear processes, Persistence
    JEL: C01 C22
    Date: 2013–06
  4. By: Ardelean, Vlad; Pleier, Thomas
    Abstract: Nonparametric prediction of time series is a viable alternative to parametric prediction, since parametric prediction relies on the correct specification of the process, its order and the distribution of the innovations. Often these are not known and have to be estimated from the data. Another source of nuisance can be the occurrence of outliers. By using nonparametric methods we circumvent both problems, the specification of the processes and the occurrence of outliers. In this article we compare the prediction power for parametric prediction, semiparametric prediction and nonparamatric methods such as support vector machines and pattern recognition. To measure the prediction power we use the MSE. Furthermore we test if the increase in prediction power is statistically significant. --
    Keywords: Parametric prediction,Nonparametric prediction,Support Vector Regression,Outliers
    Date: 2013

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