Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Estimation And Forecasting In Vector Autoregressive Moving Average Models For Rich Datasets
Download Estimation And Forecasting In Vector Autoregressive Moving Average Models For Rich Datasets full books in PDF, epub, and Kindle. Read online Estimation And Forecasting In Vector Autoregressive Moving Average Models For Rich Datasets ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets by : Gustavo Fruet Dias
Download or read book Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets written by Gustavo Fruet Dias and published by . This book was released on 2017 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: We address the issue of modelling and forecasting macroeconomic variables using rich datasets by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (IOLS) estimator. We establish the consistency and asymptotic distribution of the estimator for weak and strong VARMA(p,q) models. Monte Carlo results show that IOLS is consistent and feasible for large systems, outperforming the MLE and other linear regression based efficient estimators under alternative scenarios. Our empirical application shows that VARMA models are feasible alternatives when forecasting with many predictors. We show that VARMA models outperform the AR(1), ARMA(1,1), Bayesian VAR, and factor models, considering different model dimensions.Supplement is available at: 'https://ssrn.com/abstract=2830838' https://ssrn.com/abstract=2830838.
Book Synopsis Supplement To 'Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets' by : Gustavo Fruet Dias
Download or read book Supplement To 'Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets' written by Gustavo Fruet Dias and published by . This book was released on 2017 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: The online Supplement presents the proof the auxiliary Lemmas 1-6, the entire set of tables with results from the Monte Carlo and the empirical studies, and further discussion on selected topics.Full paper is available at: 'https://ssrn.com/abstract=2707176' https://ssrn.com/abstract=2707176.
Book Synopsis Service Research and Innovation by : Ho-Pun Lam
Download or read book Service Research and Innovation written by Ho-Pun Lam and published by Springer Nature. This book was released on 2019-10-05 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the Australasian Symposium on Service Research and Innovation, ASSRI 2018. The conference was held in two parts on September 6, 2018, in Sydney, Australia, and on December 14, 2018, in Wollongong, Australia. The 9 full and 2 short papers included in this volume were carefully reviewed and selected from a total of 26 submissions, covering a variety of topics related to service-oriented computing and service science. The book also includes 3 keynote papers.
Book Synopsis The Effect of Misspecification in Vector Autoregressive Moving Average Models on Parameter Estimation and Forecasting by : Ken Hung
Download or read book The Effect of Misspecification in Vector Autoregressive Moving Average Models on Parameter Estimation and Forecasting written by Ken Hung and published by . This book was released on 1986 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Comparison of Estimation Methods for Vector Autoregressive Moving-average Models by : Christian Kascha
Download or read book A Comparison of Estimation Methods for Vector Autoregressive Moving-average Models written by Christian Kascha and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation for Vector Autoregressive Moving Average Models by : STANFORD UNIV CALIF DEPT OF STATISTICS.
Download or read book Maximum Likelihood Estimation for Vector Autoregressive Moving Average Models written by STANFORD UNIV CALIF DEPT OF STATISTICS. and published by . This book was released on 1978 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: The vector autoregressive moving average model is a multivariate stationary stochastic process where the unobservable multivariate process consists of independently identically distributed random vectors. The coefficient matrices and the covariance matrix are to be estimated from an observed sequence. Under the assumption of normality the method of maximum likelihood is applied to likelihoods suitably modified for techniques in the frequency and time domains. Newton-Raphson and scoring iterative methods are presented.
Book Synopsis Maximum Likelihood Estimation of the Autoregressive Coefficients and Moving Average Covariances of Vector Autoregressive Moving Average Models by : Fereydoon Ahrabi
Download or read book Maximum Likelihood Estimation of the Autoregressive Coefficients and Moving Average Covariances of Vector Autoregressive Moving Average Models written by Fereydoon Ahrabi and published by . This book was released on 1979 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this paper is to derive asymptotically efficient estimates for the autoregressive matrix coefficients and moving average covariance matrices of the vector autoregressive moving average (VARMA) models in both time and frequency domains. To do this we shall apply the Newton-Raphson and scoring methods to the maximum likelihood equations derived from modified likelihood functions under the Gaussian Assumption.
Book Synopsis Maximum Likelihood Estimation of Vector Autoregressive Moving Average Models by : Greg Reinsel
Download or read book Maximum Likelihood Estimation of Vector Autoregressive Moving Average Models written by Greg Reinsel and published by . This book was released on 1976 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: A method is presented for the estimation of the parameters in the vector autoregressive moving average time series model. The estimation procedure is derived from the maximum likelihood approach and is based on Newton-Raphson techniques applied to the likelihood equations. The resulting two-step Newton-Raphson procedure is computationally simple, involving only generalized least squares estimation in the second step. This Newton-Raphson estimator is shown to be asymptotically efficient and to possess a limiting multivariate normal distribution. (Author).
Book Synopsis Essays on Alternative Methods of Identification and Estimation of Vector Autoregressive Moving Average Models by : George Athanasopoulos
Download or read book Essays on Alternative Methods of Identification and Estimation of Vector Autoregressive Moving Average Models written by George Athanasopoulos and published by . This book was released on 2005 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Testing the Fit of a Vector Autoregressive Moving Average Model by : Efstathios Paparoditis
Download or read book Testing the Fit of a Vector Autoregressive Moving Average Model written by Efstathios Paparoditis and published by . This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new procedure for testing the fit of multivariate time series model is proposed. The method evaluates in a certain way the closeness of the sample spectral density matrix of the observed process to the spectral density matrix of the parametric model postulated under the null and uses for this purpose nonparametric estimation techniques. The asymptotic distribution of the test statistic is established and an alternative, bootstrap-based method is developed in order to estimate more accurately this distribution under the null hypothesis. Goodness-of-fit diagnostics useful in understanding the test results and identifying sources of model inadequacy are introduced. The applicability of the testing procedure and its capability to detect lacks of fit is demonstrated by means of some real data examples.
Book Synopsis Lasso for Autoregressive and Moving Average Coeffi[ci]ents Via Residuals of Unobservable Time Series by : Hanh Nguyen
Download or read book Lasso for Autoregressive and Moving Average Coeffi[ci]ents Via Residuals of Unobservable Time Series written by Hanh Nguyen and published by . This book was released on 2018 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation contains four topics in time series data analysis. First, we propose the oracle model selection for autoregressive time series when the observations are contaminated with trend. An adaptive least absolute shrinkage and selection operator (LASSO) type model selection method is used after the trend is estimated by non-parametric B-splines method. The first step is to estimate the trend by B-splines method and then calculate the detrended residuals. The second step is to use the residuals as if they were observations to optimize an adaptive LASSO type objective function. The oracle properties of such an Adaptive Lasso model selection procedure are established; that is, the proposed method can identify the true model with probability approaching one as the sample size increases, and the asymptotic properties of estimators are not affected by the replacement of observations with detrended residuals. The extensive simulation studies of several constrained and unconstrained autoregressive models also confirm the theoretical results. The method is illustrated by two time series data sets, the annual U.S. tobacco production and annual tree ring width measurements. Second, we generalize our first topic to a more general class of time series using the autoregressive and moving-average (ARMA) model. The ARMA model class is the building block for stationary time series analysis. We adopt the two-step method non-parametric trend estimation with B-spline and model selection and model estimation with the adaptive LASSO. We prove that such model selection and model estimation procedure possesses the oracle properties. Another important objective of this topic is forecasting time series with trend. We approach the forecasting problem by two methods: the empirical method by using the one-step ahead prediction in time series and the bagging method. Our simulation studies show that both methods are efficient with the decreased mean square error when the sample size increases. Simulation studies are adopted to illustrate the asymptotic result of our proposed method for model selection and model estimation with twelve ARMA(p, q) models, in which p an q are in the range from 1 to 15. The method is also illustrated by two time series data sets from the New York State Energy Research and Development Authority (NYSERDA), a public benefit corporation which offers data and analysis to help New Yorkers increase energy efficiency. Third, we propose a new model class, which is motivated by lag effects of covariates on the dependent variable. Our paper aims at providing more accurate statistical analysis for the relationship, for example, between the outcome of an event that occurs once every several years and the covariates that have observations every year. Lag effects have received a great deal of attention since Almon (1965) proposed linear distributed lag models to model the dependence of time series on several regressors from a correlated sequence. Motivated by the linear distributed lag model, we propose distributed generalized linear models as well as the estimation procedure for the model coefficients. The estimators from our proposed procedure are shown to be oracle or asymptotically efficient. Simulation studies confirm the asymptotic properties of the estimators and present consequences of model misspecification as well as better model prediction accuracy. The application is illustrated by analysis of the presidential election data in 2016. Fourth, we aim to provide an easy-to-use data analysis procedure for linear regression with non-independent errors. In practice, errors in regression model may be non-independent. In such situation, it is usually suitable to assume that the error terms for the model follow a time series structure. In fact, this type of model structure (reffered as RegARMA) has received great interests from researchers. Pierce (1971) discussed a nonlinear least squares estimation of RegARMA; Greenhouse et al. (1987) studied biological rhythm data by using the RegARMA model. Recently, Wu and Wang (2012) used the shrinkage estimation procedure to analyze data using RegARMA. However, in the literature the trend factor of the time series has not been considered. We will use the same idea of the two step-procedure as in the first project and the second project for our approach. We first estimate the trend of the time series by using a non-parametric method such as B-spline or linear Kernel. We then use the adaptive LASSO method for model selection and model estimation of the linear part and the time series error part. Simulation results show that our approach works quite well. However, it would be very interesting and challenging to improve the estimations and extend the estimation method to more complicated models, which will be the focus of the future research.
Book Synopsis VARMA Models and Macroeconomic Modelling by : Wenying Yao
Download or read book VARMA Models and Macroeconomic Modelling written by Wenying Yao and published by . This book was released on 2013 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis studies the usefulness of vector autoregressive moving average (VARMA) models in macroeconomic modelling and forecasting. This thesis aims to provide more evidence on the empirical performance of VARMA models, such as forecast evaluations and impulse response analysis. By doing so, it will contribute to the growing body of literature which uses VARMA models for macroeconomic modelling, and suggest that VARMA models can be both beneficial and relatively straightforward to estimate.
Book Synopsis Estimation of the Vector Moving Average Model by Vector Autoregression and Links Between Wholesale and Retail Inventories by : John W. Galbraith
Download or read book Estimation of the Vector Moving Average Model by Vector Autoregression and Links Between Wholesale and Retail Inventories written by John W. Galbraith and published by . This book was released on 1998 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Note on Reparameterizing a Vector Autoregressive Moving Average Model to Enforce Stationarity by : Craig F. Ansley
Download or read book A Note on Reparameterizing a Vector Autoregressive Moving Average Model to Enforce Stationarity written by Craig F. Ansley and published by . This book was released on 1985 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Unified Approach to ARMA (Autoregressive-Moving Average) Model Identification and Preliminary Estimation by : G. T. Wilson
Download or read book A Unified Approach to ARMA (Autoregressive-Moving Average) Model Identification and Preliminary Estimation written by G. T. Wilson and published by . This book was released on 1983 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper reviews several different methods for identifying the orders of autoregressive-moving average models for time series data. The case is made that these have a common basis, and that a unified approach may be found in the analysis of a matrix G, defined to be the covariance matrix of forecast values. The estimation of this matrix is considered, emphasis being placed on the use of high order autoregression to approximate the predictor coefficients. Statistical procedures are proposed for analyzing G, and identifying the model orders. A simulation example and three sets of real data are used to illustrate the procedure, which appears to be very useful as a tool for order identification and preliminary model estimation. (Author).
Book Synopsis Var Models in Macroeconomics - New Developments and Applications by : Thomas B. Fomby
Download or read book Var Models in Macroeconomics - New Developments and Applications written by Thomas B. Fomby and published by Emerald Group Publishing Limited. This book was released on 2013-12-18 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Econometrics publishes original scholarly econometric papers with the intention of expanding the use of developed and emerging econometric techniques by disseminating ideas on the theory and practice of econometrics, throughout the empirical economic, business and social science literature.
Book Synopsis Economic Forecasting by : Graham Elliott
Download or read book Economic Forecasting written by Graham Elliott and published by Princeton University Press. This book was released on 2016-04-05 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and integrated approach to economic forecasting problems Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. Economic Forecasting presents a comprehensive, unified approach to assessing the costs and benefits of different methods currently available to forecasters. This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance. Presents a comprehensive and integrated approach to assessing the strengths and weaknesses of different forecasting methods Approaches forecasting from a decision theoretic and estimation perspective Covers Bayesian modeling, including methods for generating density forecasts Discusses model selection methods as well as forecast combinations Covers a large range of nonlinear prediction models, including regime switching models, threshold autoregressions, and models with time-varying volatility Features numerous empirical examples Examines the latest advances in forecast evaluation Essential for practitioners and students alike