nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒04‒20
twelve papers chosen by
Yong Yin
SUNY at Buffalo

  1. Bias-corrected estimation in potentially mildly explosive autoregressive models By Hendrik Kaufmannz; Robinson Kruse
  2. Changes in persistence, spurious regressions and the Fisher hypothesis By Robinson Kruse; Daniel Ventosa-Santaulària; Antonio E. Noriega
  3. The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications By Martin M. Andreasen; Jesús Fernández-Villaverde; Juan F. Rubio-Ramírez
  4. “GLS based unit root tests for bounded processes” By Josep Lluís Carrion-i-Silvestre; María Dolores Gadea
  5. On the accurate characterization of business cycles in nonlinear dynamic financial and economic systems By Dimitri O. Ledenyov; Viktor O. Ledenyov
  6. Non-Stationarity in Financial Time Series and Generic Features By Thilo A. Schmitt; Desislava Chetalova; Rudi Sch\"afer; Thomas Guhr
  7. Measuring Persistence in Volatility Spillovers By Conrad, Christian; Weber, Enzo
  8. Long memory via networking By Susanne Schennach
  9. The seasonal KPSS test when neglecting seasonal dummies: a Monte Carlo analysis By El Montasser, Ghassen; Boufateh, Talel; Issaoui, Fakhri
  10. Relevant States and Memory in Markov Chain Bootstrapping and Simulation By Cerqueti, Roy; Falbo, Paolo; Pelizzari, Cristian
  11. "Realized Stochastic Volatility with Leverage and Long Memory" By Shinichiro Shirota; Takayuki Hizu; Yasuhiro Omori
  12. Quasi-maximum likelihood estimation in generalized polynomial autoregressive conditional heteroscedasticity models By Tinkl, Fabian

  1. By: Hendrik Kaufmannz (Leibniz University Hannover); Robinson Kruse (Leibniz University Hannover and CREATES)
    Abstract: This paper provides a comprehensive Monte Carlo comparison of different finite-sample bias-correction methods for autoregressive processes. We consider classic situations where the process is either stationary or exhibits a unit root. Importantly, the case of mildly explosive behaviour is studied as well. We compare the empirical performance of an indirect inference estimator (Phillips, Wu, and Yu, 2011), a jackknife approach (Chambers, 2013), the approximately median-unbiased estimator by Roy and Fuller (2001) and the bootstrap- aided estimator by Kim (2003). Our findings suggest that the indirect inference approach o ers a valuable alternative to other existing techniques. Its performance (measured by its bias and root mean squared error) is balanced and highly competitive across many different settings. A clear advantage is its applicability for mildly explosive processes. In an empirical application to a long annual US Debt/GDP series we consider rolling window estimation of autoregressive models. We find substantial evidence for time-varying persistence and periods of explosiveness during the Civil War and World War II. During the recent years, the series is nearly explosive again. Further applications to commodity and interest rate series are considered as well.
    Keywords: Bias-correction, Explosive behavior, Rolling window estimation
    JEL: C13 C22 H62
    Date: 2013–04–15
  2. By: Robinson Kruse (Leibniz University Hannover and CREATES); Daniel Ventosa-Santaulària (Centro de Investigación y Docencia Económicas, CIDE); Antonio E. Noriega (Banco de México)
    Abstract: Declining inflation persistence has been documented in numerous studies. When such series are analyzed in a regression framework in conjunction with other persistent time series, spurious regressions are likely to occur. We propose to use the coefficient of determination R2 as a test statistic to distinguish between spurious and genuine regressions in situations where time series possibly (but not necessarily) exhibit changes in persistence. To this end, we establish some limit theory for the R2 statistic and conduct a Monte Carlo study where we investigate its finite-sample properties. Finally, we apply the test to the Fisher equation for the U.S. and Mexico. Contrary to a rejection using cointegration techniques, the R2-based test offers strong evidence favourable to the Fisher hypothesis.
    Keywords: Changes in persistence, Spurious regression, Fisher hypothesis.
    JEL: C12 C22 E31 E43
    Date: 2013–11–04
  3. By: Martin M. Andreasen (Aarhus University and CREATES); Jesús Fernández-Villaverde (University of Pennsylvania, FEDEA, NBER, and CEPR); Juan F. Rubio-Ramírez (Duke University, Federal Reserve Bank of Atlanta, and FEDEA)
    Abstract: This paper studies the pruned state-space system for higher-order approximations to the solutions of DSGE models. For second- and third-order approximations, we derive the statistical properties of this system and provide closed-form expressions for ?first and second unconditional moments and impulse response functions. Thus, our analysis introduces GMM estimation for DSGE models approximated up to third-order and provides the foundation for indirect inference and SMM when simulation is required. We illustrate the usefulness of our approach by estimating a New Keynesian model with habits and Epstein-Zin preferences by GMM when using ?rst and second unconditional moments of macroeconomic and ?nancial data and by SMM when using additional third and fourth unconditional moments and non-Gaussian innovations.
    JEL: C15 C53 E30
    Date: 2013–11–04
  4. By: Josep Lluís Carrion-i-Silvestre (Faculty of Economics, University of Barcelona); María Dolores Gadea (Department of Applied Economics, University of Zaragoza)
    Abstract: We show that the use of generalized least squares (GLS) detrending procedures leads to important empirical power gains compared to ordinary least squares (OLS) detrend- ing method when testing the null hypothesis of unit root for bounded processes. The non-centrality parameter that is used in the GLS-detrending depends on the bounds, so that improvements on the statistical inference are to be expected if a case-specific parameter is used. This initial hypothesis is supported by the simulation experiment that has been conducted.
    Keywords: Unit root, bounded process, quasi GLS-detrending. JEL classification: C12, C22
    Date: 2013–04
  5. By: Dimitri O. Ledenyov; Viktor O. Ledenyov
    Abstract: The accurate characterization of the business cycles in the nonlinear dynamic financial and economic systems in the time of globalization represents a formidable research problem. The central banks and other financial institutions make their decisions on the minimum capital requirements, countercyclical capital buffer allocation and capital investments, going from the precise data on the business cycles. We consider the two possible interaction scenarios, when there are: the linear interactions, and the non-linear interactions. In our opinion, the main parameters of the business cycle may deviate during the business cycle nonlinear interaction with the nonlinear dynamic financial and economic systems, because of the origination of the nonlinear effects such as the Four Waves Mixing (FWM), Stimulated Brillouin Scattering (SBS), Stimulated Raman Scattering (SRS), Carrier Induced Phase Modulation.
    Date: 2013–04
  6. By: Thilo A. Schmitt; Desislava Chetalova; Rudi Sch\"afer; Thomas Guhr
    Abstract: Financial markets are prominent examples for highly non-stationary systems. Sample averaged observables such as variances and correlation coefficients strongly depend on the time window in which they are evaluated. This implies severe limitations for approaches in the spirit of standard equilibrium statistical mechanics and thermodynamics. Nevertheless, we show that there are similar generic features which we uncover in the empirical return distributions for whole markets. We explain our findings by setting up a random matrix model.
    Date: 2013–04
  7. By: Conrad, Christian; Weber, Enzo
    Abstract: This paper analyzes volatility spillovers in multivariate GARCH-type models. We show that the cross-effects between the conditional variances determine the persistence of the transmitted volatility innovations. In particular, the effect of a foreign volatility innovation on a conditional variance is even more persistent than the effect of an own innovation unless it is offset by an accompanying negative variance spillover of sufficient size. Moreover, ignoring a negative variance spillover causes a downward bias in the estimate of the initial impact of the foreign volatility innovation. Applying the concept to portfolios of small and large firms, we find that shocks to small firm returns affect the large firm conditional variance once we allow for (negative) spillovers between the conditional variances themselves.
    Keywords: Multivariate GARCH; spillover; persistence; small and large firms.
    Date: 2013–04–12
  8. By: Susanne Schennach (Institute for Fiscal Studies and Brown University)
    Abstract: Many time-series data are known to exhibit 'long memory', that is, they have an autocorrelation function that decays very slowly with lag. This behaviour has traditionally been attributed to either aggregation of heterogenous processes, nonlinearity, learning dynamics, regime switching, structural breaks, unit roots or fractional Brownian motion. This paper identifies an entirely different mechanism for long memmory generation by showing that it can naturally arise when a large number of simply linear homogenous economic subsystems with a short memory are interconnected to form a network such that the outputs of each of the subsystem are fed into the inputs of others. This networking picture yields a type of aggregation that is not merely additive, resulting in a collective behaviour that is richer than that of individual subsystems. Interestingly, the long memory behaviour is found to be almost entirely determined by the geometry of the network while being relatively insensitive to the specific behaviour of individual agents.
    Date: 2013–04
  9. By: El Montasser, Ghassen; Boufateh, Talel; Issaoui, Fakhri
    Abstract: This paper shows through a Monte Carlo analysis the effect of neglecting seasonal deterministics on the seasonal KPSS test. We found that the test is most of the time heavily oversized and not convergent in this case. In addition, Bartlett-type non-parametric correction of error variances did not signally change the test's rejection frequencies.
    Keywords: Deterministic seasonality, Seasonal KPSS Test, Monte Carlo Simulations.
    JEL: C22 C53
    Date: 2013–04–15
  10. By: Cerqueti, Roy; Falbo, Paolo; Pelizzari, Cristian
    Abstract: Markov chain theory is proving to be a powerful approach to bootstrap highly nonlinear time series. In this work we provide a method to estimate the memory of a Markov chain (i.e. its order) and to identify its relevant states. In particular the choice of memory lags and the aggregation of irrelevant states are obtained by looking for regularities in the transition probabilities. Our approach is based on an optimization model. More specifically we consider two competing objectives that a researcher will in general pursue when dealing with bootstrapping: preserving the “structural” similarity between the original and the simulated series and assuring a controlled diversification of the latter. A discussion based on information theory is developed to define the desirable properties for such optimal criteria. Two numerical tests are developed to verify the effectiveness of the method proposed here.
    Keywords: Bootstrapping; Information Theory; Markov chains; Optimization; Simulation.
    JEL: C15 C61 C63 C65
    Date: 2013
  11. By: Shinichiro Shirota (Graduate School of Economics, University of Tokyo); Takayuki Hizu (Mitsubishi UFJ Trust and Banking); Yasuhiro Omori (Faculty of Economics, University of Tokyo)
    Abstract: The daily return and the realized volatility are simultaneously modeled in the stochastic volatility model with leverage and long memory. The dependent variable in the stochastic volatility model is the logarithm of the squared return, and its error distribution is approximated by a mixture of normals. In addition, we incorporate the logarithm of the realized volatility into the measurement equation, assuming that the latent log volatility follows an Autoregressive Fractionally Integrated Moving Average (ARFIMA) process to describe its long memory property. Using a state space representation, we propose an efficient Bayesian estimation method implemented using Markov chain Monte Carlo method (MCMC). Model comparisons are performed based on the marginal likelihood, and the volatility forecasting performances are investigated using S&P500 stock index returns.
    Date: 2013–03
  12. By: Tinkl, Fabian
    Abstract: In this article consistency and asymptotic normality of the quasi-maximum likelihood esti- mator (QMLE) in the class of polynomial augmented generalized autoregressive conditional heteroscedasticity models (GARCH) is proven. The result extend the results of (Berkes et al., 2003) and (Francq and Zaköian, 2004) of the standard GARCH model to augmented GARCH models introduced by (Duan, 1997) which contains many commonly employed GARCH models as special cases. The conditions for consistency and asymptotic normality are more tractable than the ones discussed in (Straumann and Mikosch, 2006). --
    Keywords: asymptotic normality,consistency,polynomial augmented GARCH models,quasi-maximum likelihood estimation
    Date: 2013

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