nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒03‒13
eleven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Quantile Cointegration in the Autoregressive Distributed-Lag Modelling Framework By TAE-HWAN KIM; YONGCHEOL SHIN; JIN SEO CHO
  3. Testing for Autocorrelation in Quantile Regression Models By Lijuan Huo; Tae-Hwan Kim; Yunmi Kim; Dong Jin Lee
  4. Non-Stationary Stochastic Volatility Model for Dynamic Feedback and Skewness By Mukhoti, Sujay
  5. The Out-of-sample Performance of an Exact Median-Unbiased Estimator for the Near-Unity AR(1) Model By Medel, Carlos; Pincheira, Pablo
  6. A test of the long memory hypothesis based on self-similarity By James Davidson; Dooruj Rambaccussing
  7. Bayesian nonparametric calibration and combination of predictive distributions By Federico Bassetti; Roberto Casarin; Francesco Ravazzolo
  8. Dynamic Factor Models for the Volatility Surface By Michel van der Wel; Sait R. Ozturk; Dick van Dijk
  9. Compounding approach for univariate time series with non-stationary variances By Rudi Sch\"afer; Sonja Barkhofen; Thomas Guhr; Hans-J\"urgen St\"ockmann; Ulrich Kuhl
  10. Detecting and interpreting distortions in hierarchical organization of complex time series By Stanis{\l}aw Dro\.zd\.z; Pawe{\l} O\'swi\k{e}cimka
  11. A Multivariate Test Against Spurious Long Memory By Sibbertsen, Philipp; Leschinski, Christian; Holzhausen, Marie

  1. By: TAE-HWAN KIM (Yonsei University); YONGCHEOL SHIN (University of York); JIN SEO CHO (Yonsei University)
    Abstract: Xiao (2009) develops a novel estimation technique for quantile cointegrated time series by extending Phillips and Hansen¡¯s (1990) semiparametric approach and Saikkonen¡¯s (1991) parametrically augmented approach. This paper extends Pesaran and Shin¡¯s (1998) autoregressive distributed-lag approach into quantile regression by jointly analysing short-run dynamics and long-run cointegrating relationships across a range of quantiles. We derive the asymptotic theory and provide a general package in which the model can be estimated and tested within and across quantiles. We further affirm our theoretical results by Monte Carlo simulations. Main utilities of this analysis are demonstrated through the empirical application to the dividend policy in the U.S.
    Keywords: QARDL, Quantile Regression, Long-run Cointegrating Relationship, Dividend Smoothing, Time-varying Rolling Estimation.
    JEL: C22 G35
    Date: 2014–11
  2. By: TAE-HWAN KIM (Yonsei University); SOO-BIN JEONG (Yonsei University); BONG-HWAN KIM (University of California, San Diego); HYUNG-HO MOON (University of California, San Diego)
    Abstract: Spurious rejections of the standard Dickey-Fuller (DF) test caused by a single variance break have been reported and some solutions to correct the problem have been proposed in the literature. Kim et al. (2002) put forward a correctly-sized unit root test robust to a single variance break, called the KLN test. However, there can be more than one break in variance in time-series data as documented in Zhou and Perron (2008), so allowing only one break can be too restrictive. In this paper, we show that multiple breaks in variance can generate spurious rejections not only by the standard DF test but also by the KLN test. We then propose a bootstrap-based unit root test that is correctly-sized in the presence of multiple breaks in variance. Simulation experiments demonstrate that the proposed test performs well regardless of the number of breaks and the location of the breaks in innovation variance.
    Keywords: Dickey-Fuller test; variance break; wild bootstrap.
    JEL: C12 C15
    Date: 2014–11
  3. By: Lijuan Huo (Beijing Institute of Technology); Tae-Hwan Kim (Yonsei University); Yunmi Kim (University of Seoul); Dong Jin Lee (University of Connecticut)
    Abstract: Quantile regression (QR) models have been increasingly employed in many applied areas in economics. At the early stage, applications in the quantile regression literature have usually used cross-sectional data, but the recent development has seen an increase in the use of quantile regression in both time-series and panel datasets. However, testing for possible autocorrelation, especially in the context of time-series models, has received little attention. As a rule of thumb, one might attempt to apply the usual Breusch-Godfrey LM test to the residuals of a baseline quantile regression. In this paper, we demonstrate analytically and by Monte Carlo simulations that such an application of the LM test can result in potentially large size distortions, especially in either low or high quantiles. We then propose a correct test (named the QF test) for autocorrelation in quantile regression models, which does not suffer from size distortion. Monte Carlo simulations demonstrate that the proposed test performs fairly well in finite samples, across either different quantiles or different underlying error distributions.
    Keywords: Quantile regression, autocorrelation, LM test.
    JEL: C12 C22
    Date: 2014–12
  4. By: Mukhoti, Sujay
    Abstract: In this paper I present a new single factor stochastic volatility model for asset return observed in discrete time and its latent volatility. This model unites the feedback effect and return skewness using a common factor for return and its volatility. Further, it generalizes the existing stochastic volatility framework with constant feedback to one with time varying feedback and as a consequence time varying skewness. However, presence of dynamic feedback effect violates the weak-stationarity assumption usually considered for the latent volatility process. The concept of bounded stationarity has been proposed in this paper to address the issue of non-stationarity. A characterization of the error distributions for returns and volatility is provided on the basis of existence of conditional moments. Finally, an application of the model has been explained using S&P100 daily returns under the assumption of Normal error and half Normal common factor distribution.
    Keywords: Stochastic volatility, Bounded stationarity, Leverage, Feedback, Skewness, Single factor model
    JEL: C11 C58
    Date: 2014–06–28
  5. By: Medel, Carlos; Pincheira, Pablo
    Abstract: We analyse the multihorizon forecasting performance of several strategies to estimate the stationary AR(1) model in a near-unity context. We focus on the Andrews' (1993) exact median-unbiased estimator (BC), the OLS estimator, and the driftless random walk (RW). In addition, we explore the forecasting performance of pairwise combinations between these individual strategies. We do this to investigate whether the Andrews' (1993) correction of the OLS downward bias helps in reducing mean squared forecast errors. Via simulations, we find that BC forecasts typically outperform OLS forecasts. When BC is compared to the RW we obtain mixed results, favouring the latter as the persistence of the true process increases. Interestingly, we also find that the combination of BC and RW performs well when the persistence of the process is high.
    Keywords: Near-unity autoregression; median-unbiased estimation; unbiasedness; unit root model; forecasting; forecast combinations
    JEL: C22 C52 C53 C63
    Date: 2015–03–04
  6. By: James Davidson; Dooruj Rambaccussing
    Abstract: This paper develops a new test of true versus spurious long memory, based on log-periodogram estimation of the long memory parameter using skip-sampled data. A correction factor is derived to overcome the bias in this estimator due to aliasing. The procedure is designed to be used in the context of a conventional test of signi…cance of the long memory parameter, and a composite test procedure is described that has the properties of known asymptotic size and consistency. The test is implemented using the bootstrap, with the distribution under the null hypothesis being approximated using a dependent-sample bootstrap technique to approximate short-run dependence following fractional di¤erencing. The properties of the test are investigated in a set of Monte Carlo experiments. The procedure is illustrated by applications to exchange rate volatility and dividend growth series.
    Keywords: Long Memory, Self-similarity, Bootstrap
    JEL: C12 C14
    Date: 2015–02
  7. By: Federico Bassetti (University of Pavia); Roberto Casarin (University of Venice); Francesco Ravazzolo (Norges Bank (Central Bank of Norway)and BI Norwegian Business School)
    Abstract: We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting (2010) and Gneiting and Ranjan (2013), we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly combined predictive distributions. The inference procedure is based on Gibbs sampling and allows accounting for uncertainty in the number of mixture components, mixture weights, and calibration parameters. The weak posterior consistency of the Bayesian nonparametric calibration is provided under suitable conditions for unknown true density. We study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of daily S&P returns and daily maximum wind speed at the Frankfurt airport.
    Keywords: Forecast calibration, Forecast combination, Density forecast, Beta mixtures, Bayesian nonparametrics, Slice sampling.
    JEL: C13 C14 C51 C53
    Date: 2015–02–26
  8. By: Michel van der Wel (Erasmus University Rotterdam and CREATES); Sait R. Ozturk (Erasmus University Rotterdam and the Tinbergen Institute); Dick van Dijk (Erasmus University Rotterdam, the Tinbergen Institute and ERIM)
    Abstract: The implied volatility surface is the collection of volatilities implied by option contracts for different strike prices and time-to-maturity. We study factor models to capture the dynamics of this three-dimensional implied volatility surface. Three model types are considered to examine desirable features for representing the surface and its dynamics: a general dynamic factor model, restricted factor models designed to capture the key features of the surface along the moneyness and maturity dimensions, and in-between spline-based methods. Key findings are that: (i) the restricted and spline-based models are both rejected against the general dynamic factor model, (ii) the factors driving the surface are highly persistent, (iii) for the restricted models option Delta is preferred over the more often used strike relative to spot price as measure for moneyness.
    Keywords: Dynamic Factor Models, Implied Volatility Surface, Kalman filter, Max-imum likelihood
    JEL: C32 C58 G13
    Date: 2015–01–30
  9. By: Rudi Sch\"afer; Sonja Barkhofen; Thomas Guhr; Hans-J\"urgen St\"ockmann; Ulrich Kuhl
    Abstract: A defining feature of non-stationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent parameters. To model the long-term statistical behavior, we compound the local distribution with the distribution of its parameters. Here we consider two concrete, but diverse examples of such non-stationary systems, the turbulent air flow of a fan and a time series of foreign exchange rates. Our main focus is to empirically determine the appropriate parameter distribution for the compounding approach. To this end we have to estimate the parameter distribution for univariate time series in a highly non-stationary situation.
    Date: 2015–03
  10. By: Stanis{\l}aw Dro\.zd\.z; Pawe{\l} O\'swi\k{e}cimka
    Abstract: Hierarchical organization is a cornerstone of complexity and multifractality constitutes its central quantifying concept. For model uniform cascades the corresponding singularity spectra are symmetric while those extracted from empirical data are often asymmetric. Using the selected time series representing such diverse phenomena like price changes and inter-transaction times in the financial markets, sentence length variability in the narrative texts, Missouri River discharge and Sunspot Number variability as examples, we show that the resulting singularity spectra appear strongly asymmetric, more often left-sided but in some cases also right-sided. We present a unified view on the origin of such effects and indicate that they may be crucially informative for identifying composition of the time series. One particularly intriguing case of this later kind of asymmetry is detected in the daily reported Sunspot Number variability. This signals that either the commonly used famous Wolf formula distorts the real dynamics in expressing the largest Sunspot Numbers or, if not, that their dynamics is governed by a somewhat different mechanism.
    Date: 2015–03
  11. By: Sibbertsen, Philipp; Leschinski, Christian; Holzhausen, Marie
    Abstract: This paper provides a multivariate score-type test to distinguish between true and spurious long memory. The test is based on the weighted sum of the partial derivatives of the multivariate local Whittle likelihood function. This approach takes phase shifts in the multivariate spectrum into account. The resulting pivotal limiting distribution is independent of the dimension of the process, which makes it easy to apply in practice. We prove the consistency of our test against the alternative of random level shifts or monotonic trends. A Monte Carlo analysis shows good finite sample properties of the test in terms of size and power. Additionally, we apply our test to the log-absolute returns of the S\&P 500, DAX, FTSE, and the NIKKEI. The multivariate test gives formal evidence that these series are contaminated by level shifts.
    Keywords: Multivariate Long Memory, Semiparametric Estimation, Spurious Long Memory, Volatility
    JEL: C12 C32
    Date: 2015–03

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