nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒04‒02
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Sparse Graphical Vector Autoregression: A Bayesian Approach By Roberto Casarin; Daniel Felix Ahelegbey; Monica Billio
  2. The Asymptotic Behaviour of the Residual Sum of Squares in Models with Multiple Break Points By Alastair R. Hall; Denise R. Osborn; Nikolaos Sakkas
  3. Maximum Likelihood Estimation of Dynamic Panel Threshold Models By Nelson Ramírez-Rondán
  4. Testing for Flexible Nonlinear Trends with an Integrated or Stationary Noise Component By Pierre Perron; Mototsugu Shintani; Tomoyoshi Yabu
  5. Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix) By Anne Péguin-Feissolle; Bilel Sanhaji
  6. Exploiting the Errors: A Simple Approach for Improved Volatility Forecasting By Tim Bollerslev; Andrew J. Patton; Rogier Quaedvlieg
  7. Information Criteria for Nonlinear Time Series Models By Rinke, Saskia; Sibbertsen, Philipp

  1. By: Roberto Casarin (Department of Economics, University of Venice Cà Foscari); Daniel Felix Ahelegbey (Department of Economics, University of Venice Cà Foscari); Monica Billio (Department of Economics, University of Venice Cà Foscari)
    Abstract: In high-dimensional vector autoregressive (VAR) models, it is natural to have large number of predictors relative to the number of observations, and a lack of efficiency in estimation and forecasting. In this context, model selection is a difficult issue and standard procedures may often be inefficient. In this paper we aim to provide a solution to these problems. We introduce sparsity on the structure of temporal dependence of a graphical VAR and develop an efficient model selection approach. We follow a Bayesian approach and introduce prior restrictions to control the maximal number of explanatory variables for VAR models. We discuss the joint inference of the temporal dependence, the maximum lag order and the parameters of the model, and provide an efficient Markov chain Monte Carlo procedure. The efficiency of the proposed approach is showed on simulated experiments and real data to model and forecast selected US macroeconomic variables with many predictors.
    Keywords: High-dimensional Models, Large Vector Autoregression, Model Selection, Prior Distribution, Sparse Graphical Models.
    JEL: C11 C15 C52 E17 G17
    Date: 2014
  2. By: Alastair R. Hall; Denise R. Osborn; Nikolaos Sakkas
    Date: 2015
  3. By: Nelson Ramírez-Rondán (Central Bank of Peru)
    Abstract: Threshold estimation methods are developed for dynamic panels with individual fixed specific effects covering short time periods. Maximum likelihood estimation of the threshold and the slope parameters is proposed using first difference transformations. Threshold estimate is shown to be consistent and it converges to a double-sided standard Brownian motion distribution, when the number of individuals grows to infinity for a fixed time period; and the slope estimates are consistent and asymptotically normally distributed. The method is applied to a sample of 72 countries and 8 periods of 5-year averages to determine the effect of inflation rate on long-run economic growth.
    Keywords: Threshold Models, Dynamic Panel Data, Maximum Likelihood Estimation, Inflation, Economic Growth
    JEL: C13 C23
    Date: 2015–03
  4. By: Pierre Perron (Boston University); Mototsugu Shintani (University of Tokyo and Vanderbilt University); Tomoyoshi Yabu (Keio University)
    Abstract: This paper proposes a new test for the presence of a nonlinear deterministic trend approximated by a Fourier expansion in a univariate time series for which there is no prior knowledge as to whether the noise component is stationary or contains an autoregressive unit root. Our approach builds on the work of Perron and Yabu (2009a) and is based on a Feasible Generalized Least Squares procedure that uses a super-efficient estimator of the sum of the autoregressive coefficients α when α=1. The resulting Wald test statistic asymptotically follows a chi-square limit distribution in both the I(0) and I(1) cases. To improve the finite sample properties of the test, we use a bias corrected version of the OLS estimator of α proposed by Roy and Fuller (2001). We show that our procedure is substantially more powerful than currently available alternatives. We illustrate the usefulness of our method via an application to modeling the trend of global and hemispheric temperatures.
    Keywords: nonlinear trends, unit root, median-unbiased estimator, GLS procedure, super-efficient estimator
    JEL: C2
    Date: 2015–02–27
  5. By: Anne Péguin-Feissolle (Aix Marseille University (Aix-Marseille School of Economics), CNRS & EHESS, Aix-Marseille); Bilel Sanhaji (Aix Marseille University (Aix-Marseille School of Economics), CNRS & EHESS, Aix-Marseille)
    Abstract: We introduce two multivariate constant conditional correlation tests that require little knowledge of the functional relationship determining the conditional correlations. The first test is based on artificial neural networks and the second one is based on a Taylor expansion of each unknown conditional correlation. These new tests can be seen as general misspecification tests of a large set of multivariate GARCH-type models. We investigate the size and the power of these tests through Monte Carlo experiments. Moreover, we study their robustness to non-normality by simulating some models such as the GARCH?t and Beta?t?EGARCH models. We give some illustrative empirical examples based on financial data.
    Keywords: multivariate GARCH, neural network, Taylor expansion
    JEL: C22 C45 C58
    Date: 2015–03–10
  6. By: Tim Bollerslev (Duke University, NBER and CREATES); Andrew J. Patton (Duke University); Rogier Quaedvlieg (Maastricht University)
    Abstract: We propose a new family of easy-to-implement realized volatility based forecasting models. The models exploit the asymptotic theory for high-frequency realized volatility estimation to improve the accuracy of the forecasts. By allowing the parameters of the models to vary explicitly with the (estimated) degree of measurement error, the models exhibit stronger persistence, and in turn generate more responsive forecasts, when the measurement error is relatively low. Implementing the new class of models for the S&P500 equity index and the individual constituents of the Dow Jones Industrial Average, we document significant improvements in the accuracy of the resulting forecasts compared to the forecasts from some of the most popular existing models that implicitly ignore the temporal variation in the magnitude of the realized volatility measurement errors.
    Keywords: Realized volatility, Forecasting, Measurement Errors, HAR, HARQ
    JEL: C22 C51 C53 C58
    Date: 2015–03–10
  7. By: Rinke, Saskia; Sibbertsen, Philipp
    Abstract: In this paper the performance of different information criteria for simultaneous model class and lag order selection is evaluated using simulation studies. We focus on the ability of the criteria to distinguish linear and nonlinear models. In the simulation studies, we consider three different versions of the commonly known criteria AIC, SIC and AICc. In addition, we also assess the performance of WIC and evaluate the impact of the error term variance estimator. Our results confirm the findings of different authors that AIC and AICc favor nonlinear over linear models, whereas weighted versions of WIC and all versions of SIC are able to successfully distinguish linear and nonlinear models. However, the discrimination between different nonlinear model classes is more difficult. Nevertheless, the lag order selection is reliable. In general, information criteria involving the unbiased error term variance estimator overfit less and should be preferred to using the usual ML estimator of the error term variance.
    Keywords: Information Criteria, Nonlinear Time Series, Threshold Models,Monte Carlo
    JEL: C15 C22
    Date: 2015–03

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