nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒11‒16
eighteen papers chosen by
Yong Yin
SUNY at Buffalo

  1. Generalizing smooth transition autoregressions By Emilio Zanetti Chini
  2. Edgeworth expansion for functionals of continuous diffusion processes By Mark Podolskij; Nakahiro Yoshida
  3. The Exponential Model for the Spectrum of a Time Series: Extensions and Applications By Tommaso Proietti; Alessandra Luati
  4. A unified framework for testing in the linear regression model under unknown order of fractional integration By Bent Jesper Christensen; Robinson Kruse; Philipp Sibbertsen
  5. Performance of multifractal detrended fluctuation analysis on short time series By Juan Luis Lopez; Jesus Guillermo Contreras
  6. Short-term forecasting for empirical economists. A survey of the recently proposed algorithms By Maximo Camacho; Gabriel Perez-Quiros; Pilar Poncela
  7. Forecasting multivariate time series with the Theta Method By Dimitrios D. Thomakos; Konstantinos Nikolopoulos
  8. Factor-Based Time Changes: Properties and Fit By Elisa Luciano; Marina Marena; Patrizia Semeraro
  9. Granger-causal-priority and choice of variables in vector autoregressions By Jarociński, Marek; Maćkowiak, Bartosz
  10. A Note on Wavelet Correlation and Cointegration By Fernández Macho, Francisco Javier
  11. Gaussian kernel GARCH models By Xibin Zhang; Maxwell L. King
  12. Nonparametric Estimation and Parametric Calibration of Time-Varying Coefficient Realized Volatility Models By Xiangjin B. Chen; Jiti Gao; Degui Li; Param Silvapulle
  13. Estimating Smooth Structural Change in Cointegration Models By Peter C. B. Phillips; Degui Li; Jiti Gao
  14. Bayesian Inference in Regime-Switching ARMA Models with Absorbing States: The Dynamics of the Ex-Ante Real Interest Rate Under Structural Breaks By Kim, Chang-Jin; Kim, Jaeho
  15. The `Pile-up Problem' in Trend-Cycle Decomposition of Real GDP: Classical and Bayesian Perspectives By Kim, Chang-Jin; Kim, Jaeho
  16. "Dynamic Equicorrelation Stochastic Volatility" By Yuta Kurose; Yasuhiro Omori
  17. Additive modeling of realized variance: tests for parametric specifications and structural breaks By Fengler, Matthias R.; Mammen, Enno; Vogt, Michael
  18. “Markov Switching Models for Volatility: Filtering, Approximation and Duality” By Monica Billio; Maddalena Cavicchioli

  1. By: Emilio Zanetti Chini (University of Rome "Tor Vergata")
    Abstract: This paper introduces a variant of the smooth transition autoregression (STAR). The proposed model is able to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. The null hypothesis of symmetric adjustment toward a new regime is tested by building two different LM-type tests. The first one maintains the original parametrization, while the second one is based on a third-order expanded auxiliary regression. Three diagnostic tests for no error autocorrelation, no additive asymmetry and parameter constancy are also discussed. The empirical size and power of the new symmetry as well as diagnostic tests are investigated by an extensive Monte Carlo experiment. An empirical application of the so generalized STAR (GSTAR) model to four economic time series reveals that the asymmetry in the transition between two regimes is a feature to be considered for economic analysis.
    Keywords: Dynamic Asymmetry, GSTAR, LM-type Tests, Business Cycle, Long-Term Interest Spread, CO2 Emissions
    JEL: C22 C51 C52
    Date: 2013–04–10
  2. By: Mark Podolskij (Heidelberg University and CREATES); Nakahiro Yoshida (Graduate School of Mathematical Science)
    Abstract: This paper presents new results on the Edgeworth expansion for high frequency functionals of continuous diffusion processes. We derive asymptotic expansions for weighted functionals of the Brownian motion and apply them to provide the Edgeworth expansion for power variation of diffusion processes. Our methodology relies on martingale embedding, Malliavin calculus and stable central limit theorems for semimartingales. Finally, we demonstrate the density expansion for studentized statistics of power variations.
    Keywords: diffusion processes, Edgeworth expansion, high frequency observations, power variation.
    JEL: C10 C13 C14
    Date: 2013–10–21
  3. By: Tommaso Proietti (University of Rome “Tor Vergata” and Creates); Alessandra Luati (University of Bologna)
    Abstract: The exponential model for the spectrum of a time series and its fractional extensions are based on the Fourier series expansion of the logarithm of the spectral density. The coefficients of the expansion form the cepstrum of the time series. After deriving the cepstrum of important classes of time series processes, also featuring long memory, we discuss likelihood inferences based on the periodogram, for which the estimation of the cepstrum yields a generalized linear model for exponential data with logarithmic link, focusing on the issue of separating the contribution of the long memory component to the log-spectrum. We then propose two extensions. The first deals with replacing the logarithmic link with a more general Box-Cox link, which encompasses also the identity and the inverse links: this enables nesting alternative spectral estimation methods (autoregressive, exponential, etc.) under the same likelihood-based framework. Secondly, we propose a gradient boosting algorithm for the estimation of the log-spectrum and illustrate its potential for distilling the long memory component of the log-spectrum.
    Keywords: Frequency Domain Methods, Generalized linear models, Long Memory, Boosting.
    JEL: C22 C52
    Date: 2013–10–16
  4. By: Bent Jesper Christensen (Aarhus University and CREATES); Robinson Kruse (Leibniz University Hannover and CREATES); Philipp Sibbertsen (Leibniz University Hannover)
    Abstract: We consider hypothesis testing in a general linear time series regression framework when the possibly fractional order of integration of the error term is unknown. We show that the approach suggested by Vogelsang (1998a) for the case of integer integration does not apply to the case of fractional integration. We propose a Lagrange Multiplier-type test whose limiting distribution is independent of the order of integration of the errors. Different testing scenarios for the case of deterministic and stochastic regressors are considered. Simulations demonstrate that the proposed test works well for a variety of different cases, thereby emphasizing its generality.
    Keywords: Long memory, linear time series regression, Lagrange Multiplier test
    JEL: C12 C22
    Date: 2013–05–24
  5. By: Juan Luis Lopez; Jesus Guillermo Contreras
    Abstract: The performance of the multifractal detrended analysis on short time series is evaluated for synthetic samples of several mono- and multifractal models. The reconstruction of the generalized Hurst exponents is used to determine the range of applicability of the method and the precision of its results as a function of the decreasing length of the series. As an application the series of the daily exchange rate between the U.S. dollar and the euro is studied.
    Date: 2013–11
  6. By: Maximo Camacho (Universidad de Murcia); Gabriel Perez-Quiros (Banco de España); Pilar Poncela (Universidad Autónoma de MAdrid)
    Abstract: Practitioners do not always use research findings, as the research is not always conducted in a manner relevant to real-world practice. This survey seeks to close the gap between research and practice in respect of short-term forecasting in real time. To this end, we review the most relevant recent contributions to the literature, examining their pros and cons, and we take the liberty of proposing some avenues of future research. We include bridge equations, MIDAS, VARs, factor models and Markov-switching factor models, all allowing for mixed-frequency and ragged ends. Using the four constituent monthly series of the Stock-Watson coincident index, industrial production, employment, income and sales, we evaluate their empirical performance to forecast quarterly US GDP growth rates in real time. Finally, we review the main results having regard to the number of predictors in factorbased forecasts and how the selection of the more informative or representative variables can be made.
    Keywords: Forecasting, GDP growth, time series
    JEL: E32 C22 E27
    Date: 2013–11
  7. By: Dimitrios D. Thomakos (University of Peloponnese); Konstantinos Nikolopoulos (Bangor Business School)
    Abstract: In this study building on earlier work on the properties and performance of the univariate Theta method for a unit root data generating process we: (a) derive new theoretical formulations for the application of the method on multivariate time series, (b) investigate the conditions for which the multivariate Theta method is expected to forecast better than the univariate one, (c) evaluate through simulations the bivariate form of the method, (d) evaluate this latter model in real macroeconomic and financial time series. The study provides sufficient empirical evidence to illustrate the suitability of the method for vector forecasting; furthermore it provides the motivation for further investigation of the multivariate Theta method for higher dimensions.
    Keywords: Theta method; univariate; multivariate time series; unit roots; vector forecasting
    Date: 2013–07
  8. By: Elisa Luciano; Marina Marena; Patrizia Semeraro
    Abstract: The paper explores the theoretical and fit properties of a class of multivariate Lévy processes, which are characterized as time-changed correlated Brownian motions. The time change has a common and an idiosyncratic component, thus re ecting the properties of trade, which it represents. The resulting process is still Lévy; it may provide Variance- Gamma, Normal-Inverse-Gaussian or Generalized-Hyperbolic margins. Linear and nonlinear dependence measures are studied. A non-pairwise calibration to a portfolio of ten US daily stock-market returns over the period 2009-2013 shows that the fit of the Hyperbolic specification is very good, both in terms of margins and overall correlation matrix.
    Keywords: Lévy processes, multivariate subordinators, dependence, correlation, multivariate asset modelling, multivariate time-changed processes, factor-based time changes.
    JEL: G12 G13
    Date: 2013
  9. By: Jarociński, Marek; Maćkowiak, Bartosz
    Abstract: A researcher is interested in a set of variables that he wants to model with a vector auto-regression and he has a dataset with more variables. Which variables from the dataset to include in the VAR, in addition to the variables of interest? This question arises in many applications of VARs, in prediction and impulse response analysis. We develop a Bayesian methodology to answer this question. We rely on the idea of Granger-causal-priority, related to the well-known concept of Granger-non-causality. The methodology is simple to use, because we provide closed-form expressions for the relevant posterior probabilities. Applying the methodology to the case when the variables of interest are output, the price level, and the short-term interest rate, we find remarkably similar results for the United States and the euro area. JEL Classification: C32, C52, E32
    Keywords: Bayesian model choice, granger-causal-priority, granger-noncausality, structural vector autoregression, Vector autoregression
    Date: 2013–10
  10. By: Fernández Macho, Francisco Javier
    Abstract: In a recent paper Leong-Huang:2010 {Journal of Applied Statistics 37, 215–233} proposed a wavelet-correlation-based approach to test for cointegration between two time series. However, correlation and cointegration are two different concepts even when wavelet analysis is used. It is known that statistics based on nonstationary integrated variables have non-standard asymptotic distributions. However, wavelet analysis offsets the integrating order of nonstationary series so that traditional asymptotics on stationary variables suffices to ascertain the statistical properties of wavelet-based statistics. Based on this, this note shows that wavelet correlations cannot be used as a test of cointegration.
    Keywords: econometric methods, spectral analysis, integrated process, time series models, unit roots, wavelet analysis.
    JEL: C22 C12
  11. By: Xibin Zhang; Maxwell L. King
    Abstract: This paper aims to investigate a Bayesian sampling approach to parameter estimation in the GARCH model with an unknown conditional error density, which we approximate by a mixture of Gaussian densities centered at individual errors and scaled by a common standard deviation. This mixture density has the form of a kernel density estimator of the errors with its bandwidth being the standard deviation. This study is motivated by the lack of robustness in GARCH models with a parametric assumption for the error density when used for error-density based inference such as value-at-risk (VaR) estimation. A contribution of the paper is to construct the likelihood and posterior of the model and bandwidth parameters under the kernel-form error density, and to derive the one-step-ahead posterior predictive density of asset returns. We also investigate the use and benefit of localized bandwidths in the kernel-form error density. A Monte Carlo simulation study reveals that the robustness of the kernel-form error density compensates for the loss of accuracy when using this density. Applying this GARCH model to daily return series of 42 assets in stock, commodity and currency markets, we find that this GARCH model is favored against the GARCH model with a skewed Student t error density for all stock indices, two out of 11 currencies and nearly half of the commodities. This provides an empirical justification for the value of the proposed GARCH model.
    Keywords: Bayes factors, Gaussian kernel error density, localized bandwidths, Markov chain Monte Carlo, value-at-risk
    Date: 2013
  12. By: Xiangjin B. Chen; Jiti Gao; Degui Li; Param Silvapulle
    Abstract: This paper introduces a new specification for the heterogeneous autoregressive (HAR) model for the realized volatility of S&P500 index returns. In this new model, the coeffcients of the HAR are allowed to be time-varying with unknown functional forms. We propose a local linear method for estimating this TVC-HAR model as well as a bootstrap method for constructing confidence intervals for the time varying coefficient functions. In addition, the estimated nonparametric TVC-HAR was calibrated by fitting parametric polynomial functions by minimising the L2-type criterion. The calibrated TVC-HAR and the simple HAR models were tested separately against the nonparametric TVC-HAR model. The test statistics constructed based on the generalised likelihood ratio method augmented with bootstrap method provide evidence in favour of calibrated TVC-HAR model. More importantly, the results of conditional predictive ability test developed by Giacomini and White (2006) indicate that the non-parametric TVC-HAR model consistently outperforms its calibrated counterpart as well as the simple HAR and the HAR-GARCH models in out-of-sample forecasting.
    Keywords: Bootstrap method, heterogeneous autoregressive model, locally stationary process, nonparametric method
    Date: 2013
  13. By: Peter C. B. Phillips; Degui Li; Jiti Gao
    Abstract: This paper studies nonlinear cointegration models in which the structural coefficients may evolve smoothly over time. These time-varying coefficient functions are well-suited to many practical applications and can be estimated conveniently by nonparametric kernel methods. It is shown that the usual asymptotic methods of kernel estimation completely break down in this setting when the functional coefficients are multivariate. The reason for this breakdown is a kernel-induced degeneracy in the weighted signal matrix associated with the nonstationary regressors, a new phenomenon in the kernel regression literature. Some new techniques are developed to address the degeneracy and resolve the asymptotics, using a path-dependent local coordinate transformation to re-orient coordinates and accommodate the degeneracy. The resulting asymptotic theory is fundamentally different from the existing kernel literature, giving two different limit distributions with different convergence rates in the different directions (or combinations) of the (functional) parameter space. Both rates are faster than the usual (√nh) rate for nonlinear models with smoothly changing coefficients and local stationarity. Hence two types of super-consistency apply in nonparametric kernel estimation of time-varying coefficient cointegration models. The higher rate of convergence (n√h) lies in the direction of the nonstationary regressor vector at the local coordinate point. The lower rate (nh) lies in the degenerate directions but is still super-consistent for nonparametric estimators. In addition, local linear methods are used to reduce asymptotic bias and a fully modified kernel regression method is proposed to deal with the general endogenous nonstationary regressor case. Simulations are conducted to explore the finite sample properties of the methods and a practical application is given to examine time varying empirical relationships involving consumption, disposable income, investment and real interest rates.
    Keywords: Cointegration, Endogeneity, Kernel degeneracy, Nonparametric regression, Super-consistency, Time varying coefficients
    Date: 2013
  14. By: Kim, Chang-Jin; Kim, Jaeho
    Abstract: One goal of this paper is to develop an efficient Markov-Chain Monte Carlo (MCMC) algorithm for estimating an ARMA model with a regime-switching mean, based on a multi-move sampler. Unlike the existing algorithm of Billio et al. (1999) based on a single-move sampler, our algorithm can achieve reasonably fast convergence to the posterior distribution even when the latent regime indicator variable is highly persistent or when there exist absorbing states. Another goal is to appropriately investigate the dynamics of the latent ex-ante real interest rate (EARR) in the presence of structural breaks, by employing the econometric tool developed. We argue Garcia and Perron's (1996) conclusion that the EARR rate is a constant subject to occasional jumps may be sample-specific. For an extended sample that includes recent data, Garcia and Perron's (1996) AR(2) model of EPRR may be misspecified, and we show that excluding the theory-implied moving-average terms may understate the persistence of the observed ex-post real interest rate (EPRR) dynamics. Our empirical results suggest that, even though we rule out the possibility of a unit root in the EARR, it may be more persistent and volatile than has been documented in some of the literature including Garcia and Perron (1996).
    Keywords: ARMA model with Regime Switching, Multi-move Sampler, Single-Move Sampler, Metropolis-Hastings Algorithm, Absorbing State, Ex-Ante Real Interest Rate.
    JEL: C11 E4
    Date: 2013–08
  15. By: Kim, Chang-Jin; Kim, Jaeho
    Abstract: In the case of a flat prior, a conventional wisdom is that Bayesian inference may not be very different from classical inference, as the likelihood dominates the posterior density. This paper shows that there are cases in which this conventional wisdom does not apply. An ARMA model of real GDP growth estimated by Perron and Wada (2009) is an example. While their maximum likelihood estimation of the model implies that real GDP may be a trend stationary process, Bayesian estimation of the same model implies that most of the variations in real GDP can be explained by the stochastic trend component, as in Nelson and Plosser (1982) and Morley et al. (2003). We show such dramatically different results stem from the differences in how the nuisance parameters are handled between the two approaches, especially when the parameter estimate of interest is dependent upon the estimates of the nuisance parameters for small samples. For the maximum likelihood approach, as the number of the nuisance parameters increases, we have higher probability that the moving-average root may be estimated to be one even when its true value is less than one, spuriously indicating that the data is `over-differenced.' However, the Bayesian approach is relatively free from this pile-up problem, as the posterior distribution is not dependent upon the nuisance parameters.
    Keywords: pile-up problem, ARMA model, Unobserved-Components Model, Profile likelihood, marginal powterior density, Trend-Cycle decomposition
    JEL: C11 E32
    Date: 2013–10
  16. By: Yuta Kurose (Center for the Study of Finance and Insurance, Osaka University,); Yasuhiro Omori (Faculty of Economics, University of Tokyo)
    Abstract:    A multivariate stochastic volatility model with dynamic equicorrelation and cross leverage ef- fect is proposed and estimated. Using a Bayesian approach, an ecient Markov chain Monte Carlo algorithm is described where we use the multi-move sampler, which generates multiple latent variables simultaneously. Numerical examples are provided to show its sampling e- ciency in comparison with the simple algorithm that generates one latent variable at a time given other latent variables. Furthermore, the proposed model is applied to the multivariate daily stock price index data. The empirical study shows that our novel model provides a substantial improvement in forecasting with respect to out-of-sample hedging performances
    Date: 2013–11
  17. By: Fengler, Matthias R.; Mammen, Enno; Vogt, Michael
    Abstract: For an additive autoregression model, we study two types of testing problems. First, a parametric specification of a component function is compared against a nonparametric fit. Second, two nonparametric fits of two different time periods are tested for equality. We apply the theory to a nonparametric extension of the linear heterogeneous autoregressive (HAR) model. The linear HAR model is widely employed to describe realized variance data. We find that the linearity assumption is often rejected, in particular on equity, fixed income, and currency futures data; in the presence of a structural break, nonlinearity appears to prevail on the sample before the outbreak of the financial crisis in mid-2007.
    Keywords: Additive models; Backfitting; Nonparametric time series analysis; Specification tests; Realized variance; Heterogeneous autoregressive model.
    JEL: C14 C58
    Date: 2013–11
  18. By: Monica Billio (Department of Economics, University Of Venice Cà Foscari, Italy); Maddalena Cavicchioli (Department of Economics, University Of Venice Cà Foscari, Italy)
    Abstract: This paper is devoted to show duality in the estimation of Markov Switching (MS) processes for volatility. It is well-known that MS-GARCH models suffer of path dependence which makes the estimation step unfeasible with usual Maximum Likelihood procedure. However, by rewriting the MS-GARCH model in a suitable linear State Space representation, we are able to give a unique framework to reconcile the estimation obtained by the Kalman Filter and with some auxiliary models proposed in the literature. Reasoning in the same way, we present a linear Filter for MS-Stochastic Volatility (MS-SV) models on which different conditioning sets yield more flexibility in the estimation. Estimation on simulated data and on short-term interest rates shows the feasibility of the proposed approach.
    Keywords: Markov Switching, MS-GARCH model, MS-SV model, estimation, auxiliary model, Kalman Filter.
    JEL: C01 C13 C58
    Date: 2013

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