forecast performance of nonlinear error correction models with multiple regimes Plattsburg Missouri

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forecast performance of nonlinear error correction models with multiple regimes Plattsburg, Missouri

Furthermore, the new model, when combined with the linear HAR model, is viable and flexible for purposes of forecasting volatility. de Freitas (forthcoming). C. Tests of forecast accuracy in the case where the models generating the forecasts are nested are discussed.

In some cases, even when the data-generating process is nonlinear and the model is correctly speci…ed, the linear model yields more accurate forecasts than the correct nonlinear one with estimated parameters. The whole procedure is illustrated with two real time-series. We consider Lasso-type estimators to reduce the dimensionality to a manageable one and provide strong theoretical guarantees on the forecast capability of our procedure. Two different cases are considered as we investigate the statistical properties of the LPE.

Several real examples are also addressed. Medeiros (2016). Asai, Manabu, Michael McAleer, and Marcelo C. It also allows you to accept potential citations to this item that we are uncertain about.

The model represents the standard new-Keynesian Phillips curve (NKPC) and the hybrid curve proposed by Woodford (2003) and Galí and Gertler (1999) as special cases. Daniel Klapper ist Inhaber des Lehrstuhls für Konsumgütermarketing an der Johann Wolfgang Goethe-Universität Frankfurt/Main.Bibliografische InformationenTitelMethodik der empirischen ForschungHerausgeberSönke Albers, Daniel Klapper, Udo Konradt, Achim Walter, Joachim WolfAusgabeillustriertVerlagSpringer-Verlag, 2009ISBN3834917036, 9783834917034Länge578 Seiten  Zitat exportierenBiBTeXEndNoteRefManÜber Louis Fed About RePEc RePEc home FAQ Blog Help! The model is a generalization of both the Smooth Transition AutoRegressive (STAR) and the AutoRegressive Artificial Neural Network (AR-ANN) models.

The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We consider bootstrap aggregation (bagging) to smooth parameter restrictions. Journal of Econometrics, 147, 372-383. (PDF version) In this paper we provide an alternative approach to analyze the demand for international tourism in the Balearic Islands, Spain, by using a neural Garratt and M.

The simulations show that our approach is less sensitive to increases in the dimensionality and complexity of the problem. Statistical Methods for Modelling Neural Networks. Continue reading full article Enhanced PDFStandard PDF (154.0 KB) AncillaryArticle InformationDOI10.1002/for.946View/save citationFormat AvailableFull text: PDFCopyright © 2005 John Wiley & Sons, Ltd. Quantity: Total Price = $9.99 plus shipping (U.S.

Testing for Remaining Autocorrelation of the Residuals in the Framework of Fuzzy Rule-based Time Series Modelling. The estimation of such models is addressed and the asymptotic properties of the quasi-maximum likelihood estimator are derived. Mendes (forthcoming). Adaptive Lasso estimation for ARDL models with GARCH innovations. Econometric Reviews. (PDF version). See general information about how to correct material in RePEc.

Results are based on numerical simulations and are consistent with universal exponents for random, bootstrap and diffusion percolation. The problems of selecting the variables and the number of hidden units are solved by using statistical model selection criteria and tests. The motivation lies on the fact that the latter yields a universal approximation if one lets the number of regimes grows without bound. Keywords: Demand systems, neural networks, EASI demand system, Engel curves.

On the other hand, variables such as unemployment and production were rarely selected by the LASSO. After presenting a general univariate framework for estimating realized volatilities, a simple discrete time model is presented in order to motivate the main results. Our results showed that a good selection of the adaptive LASSO hyperparameters also reduces forecast errors.Keywords: big data, forecasting, LASSO, shrinkage, model selection.doi: 10.1016/j.econlet.2015.11.01735. We estimated all the models for the two most important Brazilian inflation measures, the IPCA and the IGP-M indexes.

Sola and F. The LPE is interesting because it can be superconsistent in the presence of an endogenous regressor and, hence, preferable to the ordinary least squares estimator (LSE). If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation. Using intraday data from October 2008 to January 2011, we find that external macroeconomic announcements dominate price changes in the Foreign Exchange and Ibovespa markets, while the impact of the domestic

repec:hal:journl:halshs-00658540 is not listed on IDEAS Lists This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS. Keywords: Nonlinear models, smooth transition, mixture of models, multiple-regime, asymptotic theory. Keywords: Tourist arrivals, forecasting, neural networks, GARCH models, asymptotic theory. Keywords: Piecewise-linear models, SETAR models, nonlinearity, GRASP, combinatorial optimization.

Available at SSRN: Contact Information Zacharias Psaradakis (Contact Author) University of London - Economics, Mathematics and Statistics ( email )7-15 Gresse StreetLondon, WC1E 7HXUnited Kingdom+44 20 7631 6415 (Phone)+44 20 Medeiros, Marcelo C. Spagnolo), Journal of Time Series Analysis 24 (2003), 237-252. "A Bootstrap Test for Symmetry of Dependent Data Based on a Kolmogorov-Smirnov Type Statistic", Communications in Statistics - Simulation and Computation 32 Inflation Dynamics in Brazil: The Case of a Small Open Economy.

Monte Carlo simulations show that forecast gains can be achieved in realistic sample sizes for the stock return problem. If this variable is a lagged value of the time series, the model is called a self-exciting threshold autoregressive (SETAR) model. These form a part of model specification: the remaining steps of nonlinear model building are parameter estimation and evaluation that are also briefly considered. Equity Market" (with A.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. This Introduction gives an overview of the papers published in this special issue.doi: 10.1080/07474938.2015.10711571. doi: 10.1023/A:1011238718363   2. Model Selection and Shrinkage: An Overview.

According to the recent literature, the model contemplates indexation to past inflation and a measure of marginal cost as relevant inflation indicators. We formulate the task of finding multivariate thresholds as a combinatorial optimization problem. Local-Global Neural Networks: A New Approach for Nonlinear Time Series Modelling. Joyce, and Lucio Sarno (eds.).