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on Econometric Time Series |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2014:29&r=ets |
| By: | Alastair R. Hall; Denise R. Osborn; Nikolaos Sakkas |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:man:sespap:1504&r=ets |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:apc:wpaper:2015-032&r=ets |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:van:wpaper:vuecon-sub-15-00001&r=ets |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:aim:wpaimx:1516&r=ets |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2015-14&r=ets |
| 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 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-548&r=ets |