Univariate Time Series Analysis With Matlab
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Univariate Time Series Analysis With Matlab
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Author : Perez M.
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-06-26
Univariate Time Series Analysis With Matlab written by Perez M. and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-26 with categories.
Econometrics Toolbox(tm) provides functions for modeling economic data. You can select and calibrate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can use a variety of diagnostic functions for model selection, including hypothesis, unit root, and stationarity tests. This book focuses on Univariate Time Series Analysis.
Linear Time Series With Matlab And Octave
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Author : Víctor Gómez
language : en
Publisher: Springer Nature
Release Date : 2019-10-04
Linear Time Series With Matlab And Octave written by Víctor Gómez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-04 with Computers categories.
This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform. The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc. This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book ‘Multivariate Time Series With Linear State Space Structure’, by the same author, if they require more details.
Univariate Time Series Analysis With Matlab
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Author : Mara Prez
language : en
Publisher: CreateSpace
Release Date : 2014-09-12
Univariate Time Series Analysis With Matlab written by Mara Prez and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-12 with Mathematics categories.
MATLAB Econometrics Toolbox provides functions for modeling economic data You can select and calibrate economic models for simulation and forecasting Time series capabilities include univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis The toolbox provides Monte Carlo methods for simulating systems of linear and nonlinear stochastic differential equations and a variety of diagnostics for model selection, including hypothesis, unit root, and stationarity tests.This book develops, among others, the following topics:Econometric Modeling Model Objects, Properties, and Methods Stochastic Process Characteristics Stationary Processes Linear Time Series Model Lag Operator Notation Unit Root ProcessNonstationary Processes Trend Stationary Difference Stationary Nonseasonal and Seasonal Differencing Time Series Decomposition Moving Average Filter Moving Average Trend Estimation Parametric Trend Estimation Hodrick-Prescott Filter Seasonal Filters Seasonal Adjustment Box-Jenkins Methodology Autocorrelation and Partial Autocorrelation Ljung-Box Q-Test Detect Autocorrelation Engle's ARCH Test Detect ARCH Effects Test Autocorrelation of Squared Residuals Engle's ARCH Test Unit Root Nonstationarity Modeling Unit Root Processes Testing for Unit Roots Test Simulated Data for a Unit RootAssess Stationarity of a Time Series Test Multiple Time Series Information Criteria Model Comparison Tests Likelihood Ratio Test Lagrange Multiplier Test Wald Test Covariance Matrix Estimation Compare GARCH Models Using Likelihood Ratio Test Check Fit of Multiplicative ARIMA Model Goodness of Fit Residual Diagnostics Check Residuals for Normality Check Residuals for Autocorrelation Check Residuals for Conditional Heteroscedasticity Check Predictive Performance Nonspherical Models Plot Confidence Band Using HAC Estimates Change the Bandwidth of a HAC Estimator
Econometric Modeling With Matlab Multivariate Time Series Models
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Author : B. Noriega
language : en
Publisher: Independently Published
Release Date : 2019-03-06
Econometric Modeling With Matlab Multivariate Time Series Models written by B. Noriega and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-06 with Mathematics categories.
Econometrics Toolbox provides functions for modeling economic data. You can select and estimate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate Bayesian linear regression, univariate ARIMAX/GARCH composite models with several GARCH variants, multivariate VARX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filte. You can use a variety of diagnostics for model selection, including hypothesis tests, unit root, stationarity, and structural change.The more important topics in this book are the next: -"Vector Autoregression (VAR) Models" -"Multivariate Time Series Data Structures" -"Multivariate Time Series Model Creation" -"VAR Model Estimation" -"Convert VARMA Model to VAR Model" -"Fit VAR Model of CPI and Unemployment Rate" -"Fit VAR Model to Simulated Data" -"VAR Model Forecasting, Simulation, and Analysis" -"Generate VAR Model Impulse Responses" -"Compare Generalized and Orthogonalized Impulse Response Functions"-"Forecast VAR Model"-"Forecast VAR Model Using Monte Carlo Simulation" -"Forecast VAR Model Conditional Responses"-"Multivariate Time Series Models with Regression Terms" -"Implement Seemingly Unrelated Regression" -"Estimate Capital Asset Pricing Model Using SUR" -"Simulate Responses of Estimated VARX Model"-"Simulate VAR Model Conditional Responses" -"Simulate Responses Using filter -"VAR Model Case Study" -"Cointegration and Error Correction Analysis" -"Determine Cointegration Rank of VEC Model" -"Identifying Single Cointegrating Relations"-"Test for Cointegration Using the Engle-Granger Test" -"Estimate VEC Model Parameters Using egcitest"-"VEC Model Monte Carlo Forecasts" -"Generate VEC Model Impulse Responses" -"Identifying Multiple Cointegrating Relations" -"Test for Cointegration Using the Johansen Test" -"Estimate VEC Model Parameters Using jcitest" -"Compare Approaches to Cointegration Analysis" -"Testing Cointegrating Vectors and Adjustment Speeds" -"Test Cointegrating Vectors" -"Test Adjustment Speeds"
Time Series Analysis With Matlab Arima And Arimax Models
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Author : Perez M.
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-06-23
Time Series Analysis With Matlab Arima And Arimax Models written by Perez M. and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-23 with categories.
Econometrics Toolbox(TM) provides functions for modeling economic data. You can select and calibrate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can use a variety of diagnostic functions for model selection, including hypothesis, unit root, and stationarity tests.. This book especially developed ARIMA and ARIMAX models acfross BOX-JENKINS methodology
Econometric Modeling With Matlab Arimax Arch And Garch Models For Univariate Time Series Analysis
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Author : B. Noriega
language : en
Publisher: Independently Published
Release Date : 2019-02-24
Econometric Modeling With Matlab Arimax Arch And Garch Models For Univariate Time Series Analysis written by B. Noriega and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-24 with Mathematics categories.
This book develops the time series univariate models through the Econometric Modeler tool. This tool allows to work the phases of identification, estimation and diagnosis of a time series. Incorporates AR, MA, ARMA, ARIMA, ARCH, GARCH and ARIMAX models.The Econometric Modeler app is an interactive tool for analyzing univariate time series data. The app is well suited for visualizing and transforming data, performing statistical specification and model identification tests, fitting models to data, and iterating among these actions. When you are satisfied with a model, you can export it to the MATLAB Workspace to forecast future responses or for further analysis. You can also generate code or a report from a session.
Econometric With Matlab
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Author : A. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-11-10
Econometric With Matlab written by A. Smith and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-10 with categories.
Econometrics Toolbox provides functions for modeling economic data. You can select and estimate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate Bayesian linear regression, univariate ARIMAX/GARCH composite models with several GARCH variants, multivariate VARX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can use a variety of diagnostics for model selection, including hypothesis tests, unit root, stationarity, and structural change. A state-space model is a discrete-time, stochastic model that contains two sets of equations: - One describing how a latent process transitions in time (the state equation) - Another describing how an observer measures the latent process at each period (the observation equation) A diffuse state-space model is a state-space model that can contain at least one state with an infinite initial variance, called a diffuse state. In addition to having an infinite initial variance, all diffuse states are uncorrelated with all other states in the model. In a time-invariant state-space model: - The coefficient matrices are equivalent for all periods. - The number of states, state disturbances, observations, and observation innovations are the same for all periods. In a time-varying state-space model: - The coefficient matrices might change from period to period. - The number of states, state disturbances, observations, and observation innovations might change from period to period. For example, this might happen if there is a regime shift or one of the states or observations cannot be measured during the sampling time frame. Also, you can model seasonality using time-varying models. To create a standard or diffuse state-space model, use ssm or dssm, respectively. For time-invariant models, explicitly specify the parametric form of your state-space model by supplying the coefficient matrices. For time-variant, complex models, or models that require constraints, supply a parameter-to-matrix mapping function. The software can infer the type of state (stationary, the constant one, or nonstationary), but it is best practice to supply the state type using, for example, the StateType name-value pair argument. To filter and smooth the states of a specified ssm or dssm model, the software uses the standard Kalman filter or the diffuse Kalman filter. In the state-space model framework, the Kalman filter estimates the values of a latent, linear, stochastic, dynamic process based on possibly mismeasured observations. Given distribution assumptions on the uncertainty, the Kalman filter also estimates time series model parameters via maximum likelihood. This book develops state-space models for work with time series.
Time Series Analysis With Matlab
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Author : Mara Prez
language : en
Publisher: CreateSpace
Release Date : 2014-09-12
Time Series Analysis With Matlab written by Mara Prez and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-12 with Mathematics categories.
MATLAB Econometrics Toolbox provides functions for modeling economic data You can select and calibrate economic models for simulation and forecasting Time series capabilities include univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis The toolbox provides Monte Carlo methods for simulating systems of linear and nonlinear stochastic differential equations and a variety of diagnostics for model selection, including hypothesis, unit root, and stationarity tests.This book develops, among others, the following topics:Conditional Mean Models for Stationary Processes Specify Conditional Mean Models Using ARIMA Autoregressive Model AR(p) Model AR Model with No Constant Term AR Model with Nonconsecutive Lags AR Model with Known Parameter Values AR Model with a t Innovation Distribution Moving Average Model MA(q) Model Invertibility of the MA Model MA Model Specifications MA Model with No Constant Term MA Model with Nonconsecutive Lags MA Model with Known Parameter Values MA Model with a t Innovation Distribution Autoregressive Moving Average ModelARMA(p,q) Model Stationarity and Invertibility of the ARMA Model ARMA Model Specifications ARMA Model with No Constant Term ARMA Model with Known Parameter Values ARIMA Model ARIMA Model Specifications ARIMA Model with Known Parameter Values Multiplicative ARIMA Model Multiplicative ARIMA Model Specifications Seasonal ARIMA Model with No Constant Term Seasonal ARIMA Model with Known Parameter Values Specify Multiplicative ARIMA Model ARIMA Model Including Exogenous Covariates ARIMAX(p,D,q) Model ARIMAX Model Specifications Specify Conditional Mean Model Innovation Distribution Specify Conditional Mean and Variance Model Impulse Response Function Plot Impulse Response Function Box-Jenkins Differencing vs ARIMA Estimation Maximum Likelihood Estimation for Conditional Mean ModelsConditional Mean Model Estimation with Equality Constraints Initial Values for Conditional Mean Model Estimation Optimization Settings for Conditional Mean Model Estimation Estimate Multiplicative ARIMA Model Model Seasonal Lag Effects Using Indicator Variables Forecast IGD Rate Using ARIMAX Model Estimate Conditional Mean and Variance Models Choose ARMA Lags Using BIC Infer Residuals for Diagnostic Checking Monte Carlo Simulation of Conditional Mean Models Presample Data for Conditional Mean Model Simulation Transient Effects in Conditional Mean Model Simulations Simulate Stationary Processes Simulate an AR Process Simulate an MA Process Simulate Trend-Stationary and Difference-Stationary Processes Simulate Multiplicative ARIMA Models Simulate Conditional Mean and Variance Models Monte Carlo Forecasting of Conditional Mean Models Monte Carlo Forecasts MMSE Forecasting of Conditional Mean Models Forecast Error Convergence of AR Forecasts Forecast Multiplicative ARIMA Model Forecast Conditional Mean and Variance Model
Time Series
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Author : Raquel Prado
language : en
Publisher: CRC Press
Release Date : 2010-05-21
Time Series written by Raquel Prado and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-21 with Mathematics categories.
Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling and analysis, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and emerging topics at research frontiers. The book presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. The authors also explore the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian tools, such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. They illustrate the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, and finance. Data sets, R and MATLAB® code, and other material are available on the authors’ websites. Along with core models and methods, this text offers sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application areas.
New Introduction To Multiple Time Series Analysis
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Author : Helmut Lütkepohl
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-07-26
New Introduction To Multiple Time Series Analysis written by Helmut Lütkepohl and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-26 with Business & Economics categories.
This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.