nep-ets New Economics Papers
on Econometric Time Series
Issue of 2012‒02‒27
eleven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Some mixing properties of conditionally independent processes By Manel Kacem; Stéphane Loisel; Véronique Maume-Deschamps
  2. Are Forecast Combinations Efficient? By Pablo Pincheira
  3. A Nonlinear Panel Data Model of Cross-Sectional Dependence By James Mitchell; George Kapetanios; Yongcheol Shin
  4. U-MIDAS: MIDAS regressions with unrestricted lag polynomials By Foroni, Claudia; Marcellino, Massimiliano; Schumacher, Christian
  5. Discriminant analysis of multivariate time series using wavelets By Ann Elizabeth Maharaj; M. Andrés Alonso
  6. Robust estimation of the scale and of the autocovariance function of Gaussian shortand long-range dependent processes. By Boistard, Hélène; Levy-Leduc, Céline; Moulines, Eric; Reisen, Valdério Anselmo; Taqqu, Murad
  7. Large sample behaviour of some well-known robust estimators under longrange dependence. By Boistard, Hélène; Levy-Leduc, Céline; Moulines, Eric; Reisen, Valdério Anselmo; Taqqu, Murad
  8. Asymptotic properties of u-processes under long-range dependence. By Boistard, Hélène; Levy-Leduc, Céline; Moulines, Eric; Reisen, Valdério Anselmo; Taqqu, Murad
  9. Comparaison of several estimation procedures for long term behavior. By Dominique Guegan; Zhiping Lu; BeiJia Zhu
  10. Sieve Inference on Semi-nonparametric Time Series Models By Xiaohong Chen; Zhipeng Liao; Yixiao Sun
  11. Nonparametric adaptive estimation of linear functionals for low frequency observed Lévy processes By Johanna Kappus

  1. By: Manel Kacem (SAF - Laboratoire de Sciences Actuarielle et Financière - Université Claude Bernard - Lyon I : EA2429); Stéphane Loisel (SAF - Laboratoire de Sciences Actuarielle et Financière - Université Claude Bernard - Lyon I : EA2429); Véronique Maume-Deschamps (SAF - Laboratoire de Sciences Actuarielle et Financière - Université Claude Bernard - Lyon I : EA2429)
    Abstract: In this paper we consider conditionally independent processes with respect to some dynamic factor. We derive some mixing properties for random processes when conditioning is given with respect to unbounded memory of the factor. Our work is motivated by some real examples related to risk theory.
    Keywords: Conditional independence ; risk processes ; mixing properties
    Date: 2012–02–15
  2. By: Pablo Pincheira
    Abstract: It is well known that weighted averages of two competing forecasts may reduce Mean Squared Prediction Errors (MSPE) and may also introduce certain inefficiencies. In this paper we take an in-depth view of one particular type of inefficiency stemming from simple combination schemes. We identify testable conditions under which every linear convex combination of two forecasts displays this type of inefficiency. In particular, we show that the process of taking averages of forecasts may induce inefficiencies in the combination, even when the individual forecasts are efficient. Furthermore, we show that the so-called "optimal weighted average" traditionally presented in the literature may indeed be suboptimal. We propose a simple testable condition to detect if this traditional weighted factor is optimal in a broader sense. An optimal "recombination weight" is introduced. Finally, we illustrate our findings with simulations and an empirical application in the context of the combination of inflation forecasts.
    Date: 2012–01
  3. By: James Mitchell; George Kapetanios; Yongcheol Shin
    Abstract: This paper proposes a nonlinear panel data model which can generate endogenously both `weak' and `strong' cross-sectional dependence. The model's distinguishing characteristic is that a given agent's behaviour is influenced by an aggregation of the views or actions of those around them. The model allows for considerable flexibility in terms of the genesis of this herding or clustering type behaviour. At an econometric level, the model is shown to nest various extant dynamic panel data models. These include panel AR models, spatial models, which accommodate weak dependence only, and panel models where cross-sectional averages or factors exogenously generate strong, but not weak, cross sectional dependence. An important implication is that the appropriate model for the aggregate series becomes intrinsically nonlinear, due to the clustering behaviour, and thus requires the disaggregates to be simultaneously considered with the aggregate. We provide the associated asymptotic theory for estimation and inference. This is supplemented with Monte Carlo studies and two empirical applications which indicate the utility of our proposed model as both a structural and reduced form vehicle to model different types of cross-sectional dependence, including evolving clusters.
    Keywords: Nonlinear Panel Data Model; Clustering; Cross-section Dependence; Factor Models; Monte Carlo Simulations; Application to Stock Returns and Inflation Expectations
    JEL: C31 C33 C51 E31 G14
    Date: 2012–01
  4. By: Foroni, Claudia; Marcellino, Massimiliano; Schumacher, Christian
    Abstract: Mixed-data sampling (MIDAS) regressions allow to estimate dynamic equations that explain a low-frequency variable by high-frequency variables and their lags. When the difference in sampling frequencies between the regressand and the regressors is large, distributed lag functions are typically employed to model dynamics avoiding parameter proliferation. In macroeconomic applications, however, differences in sampling frequencies are often small. In such a case, it might not be necessary to employ distributed lag functions. In this paper, we discuss the pros and cons of unrestricted lag polynomials in MIDAS regressions. We derive unrestricted MIDAS regressions (U-MIDAS) from linear high-frequency models, discuss identification issues, and show that their parameters can be estimated by OLS. In Monte Carlo experiments, we compare U-MIDAS to MIDAS with functional distributed lags estimated by NLS. We show that U-MIDAS generally performs better than MIDAS when mixing quarterly and monthly data. On the other hand, with larger differences in sampling frequencies, distributed lag-functions outperform unrestricted polynomials. In an empirical application on out-of-sample nowcasting GDP in the US and the Euro area using monthly predictors, we find a good performance of U-MIDAS for a number of indicators, albeit the results depend on the evaluation sample. We suggest to consider U-MIDAS as a potential alternative to the existing MIDAS approach in particular for mixing monthly and quarterly variables. In practice, the choice between the two approaches should be made on a case-by-case basis, depending on their relative performance. --
    Keywords: mixed data sampling,distributed lag polynomals,time aggregation,now-casting
    JEL: E37 C53
    Date: 2011
  5. By: Ann Elizabeth Maharaj; M. Andrés Alonso
    Abstract: In analyzing ECG data, the main aim is to differentiate between the signal patterns of those of healthy subjects and those of individuals with specific heart conditions. We propose an approach for classifying multivariate ECG signals based on discriminant and wavelet analyzes. For this purpose we use multiple-scale wavelet variances and wavelet correlations to distinguish between the patterns of multivariate ECG signals based on the variability of the individual components of each ECG signal and the relationships between every pair of these components. Using the results of other ECG classification studies in the literature as references, we demonstrate that our approach applied to 12-lead ECG signals from a particular database, displays quite favourable performance. We also demonstrate with real and synthetic ECG data that our approach to classifying multivariate time series out performs other well-known approaches for classifying multivariate time series. In simulation studies using multivariate time series that have patterns that are different from that of the ECG signals, we also demonstrate very favourably performance of this approach when compared to these other approaches.
    Keywords: Time series, Wavelet Variances, Wavelet Correlations, Discriminant Analysis
    JEL: C38 C22
    Date: 2012–02
  6. By: Boistard, Hélène; Levy-Leduc, Céline; Moulines, Eric; Reisen, Valdério Anselmo; Taqqu, Murad
    Date: 2011
  7. By: Boistard, Hélène; Levy-Leduc, Céline; Moulines, Eric; Reisen, Valdério Anselmo; Taqqu, Murad
    Date: 2011
  8. By: Boistard, Hélène; Levy-Leduc, Céline; Moulines, Eric; Reisen, Valdério Anselmo; Taqqu, Murad
    Date: 2011
  9. By: Dominique Guegan (Centre d'Economie de la Sorbonne); Zhiping Lu (East China Normal University (ECNU)); BeiJia Zhu (Centre d'Economie de la Sorbonne et East China Normal University (ECNU))
    Abstract: In this paper, nine memory parameter estimation procedures for the fractionally integrated I(d) process, semi-parametric and parametric, which prevail in the existing literature are reviewed ; through the simulation study under the ARFIMA (p,d,q) setting we cast a light on the finite sample performance of these estimation procedures for the non-stationary long memory time series. As a by-product of this study, we provide a bandwidth parameter selection strategy for the frequency domain estimation and an upper-and-lower scale trimming strategy for the wavelet domain estimation from a practical stand-point. The other objective of this paper is to give a useful reference to the applied reserachers and practitioners.
    Keywords: Finite sample performance comparaison, Fourier frequency, GDP, non-stationary long memory time series, wavelet.
    JEL: C12 C15 C22
    Date: 2012–02
  10. By: Xiaohong Chen (Cowles Foundation, Yale University); Zhipeng Liao (Dept. of Economics, UC Los Angeles); Yixiao Sun (Dept. of Economics, UC San Diego)
    Abstract: The method of sieves has been widely used in estimating semiparametric and nonparametric models. In this paper, we first provide a general theory on the asymptotic normality of plug-in sieve M estimators of possibly irregular functionals of semi/nonparametric time series models. Next, we establish a surprising result that the asymptotic variances of plug-in sieve M estimators of irregular (i.e., slower than root-T estimable) functionals do not depend on temporal dependence. Nevertheless, ignoring the temporal dependence in small samples may not lead to accurate inference. We then propose an easy-to-compute and more accurate inference procedure based on a "pre-asymptotic" sieve variance estimator that captures temporal dependence. We construct a "pre-asymptotic" Wald statistic using an orthonormal series long run variance (OS-LRV) estimator. For sieve M estimators of both regular (i.e., root-T estimable) and irregular functionals, a scaled "pre-asymptotic" Wald statistic is asymptotically F distributed when the series number of terms in the OS-LRV estimator is held fixed. Simulations indicate that our scaled "pre-asymptotic" Wald test with F critical values has more accurate size in finite samples than the usual Wald test with chi-square critical values.
    Keywords: Weak dependence, Sieve M estimation, Sieve Riesz representor, Irregular functional, Misspecification, Pre-asymptotic variance, Orthogonal series long run variance estimation, F distribution
    JEL: C12 C14 C32
    Date: 2012–02
  11. By: Johanna Kappus
    Abstract: For a Lévy process X having finite variation on compact sets and finite first moments, µ( dx) = xv( dx) is a finite signed measure which completely describes the jump dynamics. We construct kernel estimators for linear functionals of µ and provide rates of convergence under regularity assumptions. Moreover, we consider adaptive estimation via model selection and propose a new strategy for the data driven choice of the smoothing parameter.
    Keywords: Statistics of stochastic processes, Low frequency observed Lévy processes, Nonparametric statistics, Adaptive estimation, Model selection with unknown variance
    JEL: C14
    Date: 2012–02

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