nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒03‒06
eleven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Power-law cross-correlations estimation under heavy tails By Ladislav Kristoufek
  2. Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data By Pierre Guérin; Danilo Leiva-Leon
  3. The Effects of Sampling Frequency on Detrending Methods for Unit Root Tests By Chambers, Marcus J
  4. Estimation of Panel Vector Autoregression in Stata: a Package of Programs By Michael R.M. Abrigo; Inessa Love
  5. Multivariate variance ratio statistics By Seok Young Hong; Oliver Linton; Hui Jun Zhang
  6. Note on a new Seasonal Fractionally Integrated Separable Spatial Autoregressive Model By Papa Ousmane Cissé; Abdou Kâ Diongue; Dominique Guegan
  7. Testing for Noncausal Vector Autoregressive Representation By Hamidi Sahneh, Mehdi
  8. Plausibility of big shocks within a linear state space setting with skewness By Koloch, Grzegorz
  9. Complex Exponential Smoothing By Svetunkov, Ivan; Kourentzes, Nikolaos
  10. Forecasting with VAR Models: Fat Tails and Stochastic Volatility By Ching-Wai (Jeremy) Chiu; Haroon Mumtaz; Gabor Pinter
  11. Testing for Granger causality in large mixed-frequency VARs By Götz, Thomas B.; Hecq, Alain; Smeekes, Stephan

  1. By: Ladislav Kristoufek
    Abstract: We examine the performance of six estimators of the power-law cross-correlations -- the detrended cross-correlation analysis, the detrending moving-average cross-correlation analysis, the height cross-correlation analysis, the averaged periodogram estimator, the cross-periodogram estimator and the local cross-Whittle estimator -- under heavy-tailed distributions. The selection of estimators allows to separate these into the time and frequency domain estimators. By varying the characteristic exponent of the $\alpha$-stable distributions which controls the tails behavior, we report several interesting findings. First, the frequency domain estimators are practically unaffected by heavy tails bias-wise. Second, the time domain estimators are upward biased for heavy tails but they have lower estimator variance than the other group for short series. Third, specific estimators are more appropriate depending on distributional properties and length of the analyzed series. In addition, we provide a discussion of implications of these results for empirical applications as well as theoretical explanations.
    Date: 2016–02
  2. By: Pierre Guérin; Danilo Leiva-Leon
    Abstract: This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In particular, we extend two existing classes of combination schemes – Bayesian (static) model averaging and dynamic model averaging – so as to explicitly reflect the objective of forecasting a discrete outcome. Both simulation and empirical exercises show that our new combination schemes outperform competing combination schemes in terms of forecasting accuracy. In the empirical application, we estimate and forecast U.S. business cycle turning points with state-level employment data. We find that forecasts obtained with our best combination scheme provide timely updates of the U.S. business cycles.
    Keywords: Business fluctuations and cycles, Econometric and statistical methods
    JEL: C53 E32 E37
    Date: 2015
  3. By: Chambers, Marcus J
    Abstract: This paper analyses the effects of sampling frequency on detrending methods based on an underlying continuous time representation of the process of interest. Such an approach has the advantage of allowing for the explicit - and different - treatment of the ways in which stock and flow variables are actually observed. Some general results are provided before the focus turns to three particular detrending methods that have found widespread use in the conduct of tests for a unit root, these being GLS detrending, OLS detrending, and first differencing, and which correspond to particular values of the generic detrending parameter. In addition, three different scenarios concerning sampling frequency and data span, in each of which the number of observations increases, are considered for each detrending method. The limit properties of the detrending coeffcient estimates, as well as an invariance principle for the detrended variable, are derived. An example of the application of the techniques to testing for a unit root, using GLS detrending on an intercept, is provided and the results of a simulation exercise to analyse the size and power properties of the test in the three different sampling scenarios are reported.
    Keywords: Continuous time; detrending; sampling frequency
    Date: 2016
  4. By: Michael R.M. Abrigo (University of Hawaii at Manoa and Philippine Institute for Development Studies); Inessa Love (University of Hawaii at Manoa)
    Abstract: Panel vector autoregression (VAR) models have been increasingly used in applied research. While programs specifically designed to estimate time-series VAR models are often included as standard features in most statistical packages, panel VAR model estimation and inference are often implemented with general-use routines that require some programming dexterity. In this paper, we briefly discuss model selection, estimation and inference of homogeneous panel VAR models in a generalized method of moments (GMM) framework, and present a set of Stata programs to conveniently execute them. We illustrate the pvar package of programs by using standard Stata datasets.
    Date: 2016–01
  5. By: Seok Young Hong (Institute for Fiscal Studies); Oliver Linton (Institute for Fiscal Studies and University of Cambridge); Hui Jun Zhang (Institute for Fiscal Studies)
    Abstract: We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the Efficient Market Hypothesis). We do not impose the no leverage assumption of Lo and MacKinlay (1988) but our asymptotic standard errors are relatively simple and in particular do not require the selection of a bandwidth parameter. We extend the framework to allow for a smoothly varying risk premium in calendar time, and show that the limiting distribution is the same as in the constant mean adjustment case. We show the limiting behaviour of the statistic under a multivariate fads model and under a moderately explosive bubble process: these alternative hypotheses give opposite predictions with regards to the long run value of the statistics. We apply the methodology to three weekly size-sorted CRSP portfolio returns from 1962 to 2013 in three subperiods. We ?find evidence of a reduction of linear predictability in the most recent period, for small and medium cap stocks. We ?find similar results for the main UK stock indexes. The main findings are not substantially affected by allowing for a slowly varying risk premium.
    Date: 2014–06
  6. By: Papa Ousmane Cissé (Centre d'Economie de la Sorbonne et Université Gaston Berger - LERSTAD); Abdou Kâ Diongue (Université Gaston Berger - LERSTAD); Dominique Guegan (Centre d'Economie de la Sorbonne)
    Abstract: In this paper, we introduce a new model called Fractionally Integrated Separable Spatial Autoregressive processes with Seasonality and denoted Seasonal FISSAR for two-dimensional spatial data. We focus on the class of separable spatial models whose correlation structure can be expressed as a product of correlations. This new modelling allows taking into account the seasonality patterns observed in spatial data. We investigate the properties of this new model providing stationary conditions, some explicit expressions form of the autocovariance function and the spectral density function. We establish the asymptotic behaviour of the spectral density function near the seasonal frequencies and perform some simulations to illustrate the behaviour of the model
    Keywords: seasonality; spatial short memory; seasonal long memory; two-dimensional data; separable process; spatial stationary process; spatial autocovariance
    JEL: C02 C21 C51 C52
    Date: 2016–01
  7. By: Hamidi Sahneh, Mehdi
    Abstract: We propose a test for noncausal vector autoregressive representation generated by non-Gaussian shocks. We prove that in these models the Wold innovations are martingale difference if and only if the model is correctly specified. We propose a test based on a generalized spectral density to check for martingale difference property of the Wold innovations. Our approach does not require to identify and estimate the noncausal models. No specific estimation method is required, and the test has the appealing nuisance parameter free property. The test statistic uses all lags in the sample and it has a convenient asymptotic standard normal distribution under the null hypothesis. A Monte Carlo study is conducted to examine the �finite-sample performance of our test.
    Keywords: Explosive Bubble; Identification; Noncausal Process; Vector Autoregressive.
    JEL: C32 C5
    Date: 2013–08–05
  8. By: Koloch, Grzegorz
    Abstract: In this paper we provide formulae for likelihood function, filtration densities and prediction densities of a linear state space model in which shocks are allowed to be skewed. In particular we work with the closed skew normal distribution, see González-Farías et al. (2004), which nests a normal distribution as a special case. Closure of the csn distribution with respect to all necessary transformations in the state space setting is guaranteed by a simple state dimension reduction procedure which does not influence the value of the likelihood function. Presented formulae allow for estimation, filtration and prediction of vector autoregressions and first order perturbations of DSGE models with skewed shocks. This allows to assess asymmetries in shocks, observed data, impulse responses and forecasts confidence intervals. Some of the advantages of using the outlined approach may involve capturing asymmetric inflation risks in central banks forecasts or producing more plausible probabilities of deep but rare recessionary episodes with DSGE/VAR filtration. Exemplary estimation results are provided which show that within a linear setting with skewness frequency of big shocks can be rather plausibly identifed.
    Keywords: Maximum likelihood estimation, state space models, closed skew-normal distribution, DSGE, VAR
    JEL: C13 C51 E32
    Date: 2016–01–25
  9. By: Svetunkov, Ivan; Kourentzes, Nikolaos
    Abstract: Exponential smoothing has been one of the most popular forecasting methods for business and industry. Its simplicity and transparency have made it very attractive. Nonetheless, modelling and identifying trends has been met with mixed success, resulting in the development of various modifications of trend models. We present a new approach to time series modelling, using the notion of ``information potential" and the theory of functions of complex variables. A new exponential smoothing method that uses this approach, ``Complex exponential smoothing" (CES), is proposed. It has an underlying statistical model described here and has several advantages over the conventional exponential smoothing models: it allows modelling and forecasting both trended and level time series, effectively sidestepping the model selection problem. CES is evaluated on real data demonstrating better performance than established benchmarks and other exponential smoothing methods.
    Keywords: Forecasting, exponential smoothing, ETS, model selection, information potential, complex variables
    JEL: C5 C53
    Date: 2015–05–01
  10. By: Ching-Wai (Jeremy) Chiu (Bank of England); Haroon Mumtaz (Queen Mary University of London); Gabor Pinter (Bank of England)
    Abstract: In this paper, we provide evidence that fat tails and stochastic volatility can be important in improving in-sample fit and out-of-sample forecasting performance. Specifically, we construct a VAR model where the orthogonalised shocks feature Student’s t distribution and time-varying variance. We estimate this model using US data on output growth, inflation, interest rates and stock returns. In terms of in-sample fit, the VAR model that features both stochastic volatility and Student’s t-distributed disturbances outperforms restricted alternatives that feature either attributes. The VAR model with Student’s t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity, and this difference appears to be especially stark over the recent Great Recession. Further international evidence confirms that accounting for both stochastic volatility and Student’s t-distributed disturbances may lead to improved forecast accuracy.
    Keywords: Financial Frictions, Predictive Densities, Great Recession, Threshold VAR
    JEL: C11 C32 C52
    Date: 2015–02
  11. By: Götz, Thomas B.; Hecq, Alain; Smeekes, Stephan
    Abstract: We analyze Granger causality testing in a mixed-frequency VAR, where the difference in sampling frequencies of the variables is large. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose several tests based on reduced rank restrictions, and implement bootstrap versions to account for the uncertainty when estimating factors and to improve the finite sample properties of these tests. We also consider a Bayesian VAR that we carefully extend to the presence of mixed frequencies. We compare these methods to an aggregated model, the max-test approach introduced by Ghysels et al. (2015a) as well as to the unrestricted VAR using Monte Carlo simulations. The techniques are illustrated in an empirical application involving daily realized volatility and monthly business cycle fluctuations.
    Keywords: Granger Causality,Mixed Frequency VAR,Bayesian VAR,Reduced Rank Model,Bootstrap Test
    JEL: C11 C12 C32
    Date: 2015

This nep-ets issue is ©2016 by Yong Yin. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.