nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒04‒16
ten papers chosen by
Yong Yin
SUNY at Buffalo

  1. Panel Data Analysis with Heterogeneous Dynamics By Ryo Okui; Takahide Yanagi
  2. Identification of Structural Vector Autoregressions by Stochastic Volatility By Dominik Bertsche; Robin Braun
  3. Does the choice of estimator matter for forecasting? A revisit By Afees A. Salisu; Ahamuefula Ephraim Ogbonna; Paul Adeoye Omosebi
  4. On Bootstrap Implementation of Likelihood Ratio Test for a Unit Root By Skrobotov Anton
  5. On the robustness of the principal volatility components By Trucíos Maza, Carlos César; Hotta, Luiz Koodi; Pereira, Pedro L. Valls
  6. Continuous Record Asymptotics for Structural Change Models By Alessandro Casini; Pierre Perron
  7. Generalized Laplace Inference in Multiple Change-Points Models By Alessandro Casini; Pierre Perron
  8. Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework By Alessandro Casini
  9. DSGE Models with Observation-Driven Time-Varying parameters By Giovanni Angelini; Paolo Gorgi
  10. Modelling multivariate volatilities via latent common factors By Li, Weiming; Gao, Jing; Li, Kunpeng; Yao, Qiwei

  1. By: Ryo Okui; Takahide Yanagi
    Abstract: This paper proposes a model-free approach to analyze panel data with heterogeneous dynamic structures across observational units. We first compute the sample mean, autocovariances, and/or autocorrelations for each unit, and then estimate the parameter of interest based on their empirical distributions. We illustrate the usefulness of our procedures by studying the law of one price deviation dynamics. We investigate the asymptotic properties of our estimators using double asymptotics. We propose split-panel jackknife bias correction and an inference procedure based on the cross-sectional bootstrap. The results of Monte Carlo simulations confirm the usefulness of our procedures in finite samples.
    Date: 2018–03
  2. By: Dominik Bertsche (University of Konstanz, Department of Economics, Box 129, 78457 Konstanz, Germany); Robin Braun (University of Konstanz, Graduate School of Decision Science, Department of Economics, Box 129, 78457 Konstanz, Germany)
    Abstract: We propose to exploit stochastic volatility for statistical identification of Structural Vector Autoregressive models (SV-SVAR). We discuss full and partial identification of the model and develop efficient EM algorithms for Maximum Likelihood inference. Simulation evidence suggests that the SV-SVAR works well in identifying structural parameters also under misspecification of the variance process, particularly if compared to alternative heteroskedastic SVARs. We apply the model to study the interdependence between monetary policy and stock markets. Since shocks identified by heteroskedasticity may not be economically meaningful, we exploit the framework to test conventional exclusion restrictions as well as Proxy SVAR restrictions which are overidentifying in the heteroskedastic model.
    Keywords: Structural Vector Autoregression (SVAR), Identification via heteroskedasticity, Stochastic Volatility, Proxy SVAR
    JEL: C32
    Date: 2018–04–05
  3. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan); Ahamuefula Ephraim Ogbonna (Centre for Econometric and Allied Research, University of Ibadan Department of Statistics, University of Ibadan, Ibadan, Nigeria); Paul Adeoye Omosebi (Centre for Econometric and Allied Research, University of Ibadan. Department of Computer Sciences, University of Lagos, Akoka, Nigeria.)
    Abstract: In this study, we further examine whether the choice of estimator matters for forecasting based on the conclusion of Westerlund and Narayan [WN, hereafter] (2012, 2015). A similar but small simulation study was conducted by WN (2012, 2015) to validate the need to account for salient features of predictors such as persistence, endogeneity and conditional heteroscedasticity in a forecast model. In addition to considering a more representative number of observations for high frequency, extensive replications and four competing estimators, we offer alternative functions for these effects and thereafter, we test whether the conclusion of WN (2012, 2015) will still hold. Our results further lend support to the WN (2012, 2015) findings and thus suggest that the choice of estimator matters for forecasting notwithstanding the alternative functions and scenarios considered in our study. Thus, pre-testing the predictors in a forecast model for the mentioned features is required to identify the appropriate estimator to apply.
    Keywords: Endogeneity, Heteroscedasticity, Persistence, Forecast evaluation
    JEL: C15 C52 C53
    Date: 2018–04
  4. By: Skrobotov Anton (RANEPA)
    Abstract: In this paper we investigate the bootstrap implementation of the likelihood ratio test for a unit root recently proposed by Jansson and Nielsen (2012). We demonstrate that the likelihood ratio test shows poor finite sample properties under strongly autocorrelated errors, i.e. if the autoregressive or moving average roots are close to -1. The size distortions in these case are more pronounced in comparison to the bootstrap M and ADF tests. We found that the bootstrap version of likelihood ratio test (with autoregressive recolouring) demonstrates better performance than bootstrap M tests. Moreover, the bootstrap likelihood ratio test show better finite sample properties in comparison to the bootstrap ADF in some cases.
    Keywords: likelihood ratio test, unit root test, bootstrap.
    JEL: C12 C22
    Date: 2018
  5. By: Trucíos Maza, Carlos César; Hotta, Luiz Koodi; Pereira, Pedro L. Valls
    Abstract: In this paper, we analyse the recent principal volatility components analysis procedure. The procedure overcomes several diculties in modelling and forecasting the conditional covariance matrix in large dimensions arising from the curse of dimensionality. We show that outliers have a devastating e↵ect on the construction of the principal volatility components and on the forecast of the conditional covariance matrix and consequently in economic and financial applications based on this forecast. We propose a robust procedure and analyse its finite sample properties by means of Monte Carlo experiments and also illustrate it using empirical data. The robust procedure outperforms the classical method in simulated and empirical data.
    Date: 2018–03
  6. By: Alessandro Casini; Pierre Perron
    Abstract: For a partial structural change in a linear regression model with a single break, we develop a continuous record asymptotic framework to build inference methods for the break date. We have T observations with a sampling frequency h over a fixed time horizon [0, N] , and let T with h 0 while keeping the time span N fixed. We impose very mild regularity conditions on an underlying continuous-time model assumed to generate the data. We consider the least-squares estimate of the break date and establish consistency and convergence rate. We provide a limit theory for shrinking magnitudes of shifts and locally increasing variances. The asymptotic distribution corresponds to the location of the extremum of a function of the quadratic variation of the regressors and of a Gaussian centered martingale process over a certain time interval. We can account for the asymmetric informational content provided by the pre- and post-break regimes and show how the location of the break and shift magnitude are key ingredients in shaping the distribution. We consider a feasible version based on plug-in estimates, which provides a very good approximation to the finite sample distribution. We use the concept of Highest Density Region to construct confidence sets. Overall, our method is reliable and delivers accurate coverage probabilities and relatively short average length of the confidence sets. Importantly, it does so irrespective of the size of the break.
    Date: 2018–03
  7. By: Alessandro Casini; Pierre Perron
    Abstract: Under the classical long-span asymptotic framework we develop a class of Generalized Laplace (GL) inference methods for the change-point dates in a linear time series regression model with multiple structural changes analyzed in, e.g., Bai and Perron (1998). The GL estimator is defined by an integration rather than optimization-based method and relies on the least-squares criterion function. It is interpreted as a classical (non-Bayesian) estimator and the inference methods proposed retain a frequentist interpretation. Since inference about the change-point dates is a nonstandard statistical problem, the original insight of Laplace to interpret a certain transformation of a least-squares criterion function as a statistical belief over the parameter space provides a better approximation about the uncertainty in the data about the change-points relative to existing methods. Simulations show that the GL estimator is in general more precise than the OLS estimator. On the theoretical side, depending on some input (smoothing) parameter, the class of GL estimators exhibits a dual limiting distribution; namely, the classical shrinkage asymptotic distribution of Bai and Perron (1998), or a Bayes-type asymptotic distribution.
    Date: 2018–03
  8. By: Alessandro Casini
    Abstract: We develop a novel continuous-time asymptotic framework for inference on whether the predictive ability of a given forecast model remains stable over time. We formally define forecast instability from the economic forecaster's perspective and highlight that the time duration of the instability bears no relationship with stable period. Our approach is applicable in forecasting environment involving low-frequency as well as high-frequency macroeconomic and financial variables. As the sampling interval between observations shrinks to zero the sequence of forecast losses is approximated by a continuous-time stochastic process (i.e., an Ito semimartingale) possessing certain pathwise properties. We build an hypotheses testing problem based on the local properties of the continuous-time limit counterpart of the sequence of losses. The null distribution follows an extreme value distribution. While controlling the statistical size well, our class of test statistics feature uniform power over the location of the forecast failure in the sample. The test statistics are designed to have power against general form of insatiability and are robust to common forms of non-stationarity such as heteroskedasticty and serial correlation. The gains in power are substantial relative to extant methods, especially when the instability is short-lasting and when occurs toward the tail of the sample.
    Date: 2018–03
  9. By: Giovanni Angelini (University of Bologna, Italy); Paolo Gorgi (VU Amsterdam, The Netherlands)
    Abstract: This paper proposes a novel approach to introduce time-variation in structural parameters of DSGE models. Structural parameters are allowed to evolve over time via an observation-driven updating equation. The estimation of the resulting DSGE model can be easily performed by maximum likelihood without the need of time-consuming simulation-based methods. An application to a DSGE model with time varying volatility for structural shocks is presented. The results indicate a significant improvement in forecasting performance.
    Keywords: DSGE models; score-driven models; time-varying parameters
    JEL: C32 C5
    Date: 2018–03–30
  10. By: Li, Weiming; Gao, Jing; Li, Kunpeng; Yao, Qiwei
    Abstract: Volatility, represented in the form of conditional heteroscedasticity, plays an impor- tant role in controlling and forecasting risks in various financial operations including asset pricing, portfolio allocation, and hedging futures. However, modeling and fore- casting multi-dimensional conditional heteroscedasticity are technically challenging. As the volatilities of many financial assets are often driven by a few common and latent factors, we propose in this paper a dimension reduction method to model a multivariate volatility process and to estimate a lower-dimensional space, to be called the volatility space, within which the dynamics of the multivariate volatility process is confined. The new method is simple to use, as technically it boils down to an eigenanalysis for a non- negative definite matrix. Hence it is applicable to the cases when the number of assets concerned is in the order of thousands (using an ordinary PC/laptop). On the other hand, the model has the capability to cater for complex conditional heteroscedastic- ity behavior for multi-dimensional processes. Some asymptotic properties for the new method are established. We further illustrate the new method using both simulated and real data examples.
    Keywords: Eigenanalysis; latent factors; multi-dimensional volatility process; volatility space
    JEL: C1 L81
    Date: 2016–10–01

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