Econometric Modeling With Matlab Arimax Arch And Garch Models For Univariate Time Series Analysis
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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.
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 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
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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
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
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.
Econometric Modeling With Matlab Conditional Variance Time Series Models
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Author : B. Noriega
language : en
Publisher: Independently Published
Release Date : 2019-03-03
Econometric Modeling With Matlab Conditional Variance 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 2019-03-03 with Mathematics categories.
Conditional variance models are appropriate for time series that do not exhibit significant autocorrelation, but are serially dependent. For modeling time series that are both autocorrelated and serially dependent, you can consider using a composite conditional mean and variance model.Two characteristics of financial time series that conditional variance models address are: -Volatility clustering. Volatility is the conditional standard deviation of a time series. Autocorrelation in the conditional variance process results in volatility clustering. The GARCH model and its variants model autoregression in the variance series.-Leverage effects. The volatility of some time series responds more to large decreases than to large increases. This asymmetric clustering behavior is known as the leverage effect. The EGARCH and GJR models have leverage terms to model this asymmetry.The generalized autoregressive conditional heteroscedastic (GARCH) model is an extension of Engle's ARCH model for variance heteroscedasticity. If a series exhibits volatility clustering, this suggests that past variances might be predictive of the current variance. The GARCH(P, Q) model is an autoregressive moving average model for conditional variances, with P GARCH coefficients associated with lagged variances, and Q ARCH coefficients associated with lagged squared innovations.The exponential GARCH (EGARCH) model is a GARCH variant that models the logarithm of the conditional variance process. In addition to modeling the logarithm, the EGARCH model has additional leverage terms to capture asymmetry in volatility clustering. The EGARCH(P, Q) model has P GARCH coefficients associated with lagged log variance terms, Q ARCH coefficients associated with the magnitude of lagged standardized innovations, and Q leverage coefficients associated with signed, lagged standardized innovations.
Time Series Models For Business And Economic Forecasting
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Author : Philip Hans Franses
language : en
Publisher: Cambridge University Press
Release Date : 1998-10-15
Time Series Models For Business And Economic Forecasting written by Philip Hans Franses and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-10-15 with Business & Economics categories.
An introduction to time series models for business and economic forecasting.
The Econometric Modelling Of Financial Time Series
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Author : Terence C. Mills
language : en
Publisher: Cambridge University Press
Release Date : 1999-08-26
The Econometric Modelling Of Financial Time Series written by Terence C. Mills and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-08-26 with Business & Economics categories.
Provides detailed coverage of the models currently being used in the empirical analysis of financial markets. Copyright © Libri GmbH. All rights reserved.
Econometric With Matlab
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Author : A. Smith
language : en
Publisher:
Release Date : 2017-11-10
Econometric With Matlab written by A. Smith and has been published by 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.This book develops VAR, VARX, VARMA, VARMAX and VEC time series models.The most important content is the following:* Vector Autoregression (VAR) Models* Types of Multivariate Time Series Models* Lag Operator Representation* Stable and Invertible Models* Building VAR Models* Multivariate Time Series Data Structures* Multivariate Time Series Data* Data Preprocessing* Partitioning Response Data* Multivariate Time Series Model Creation* Models for Multiple Time Series* Creating VAR Models* Create and Adjust VAR Model Using Shorthand Syntax* Create and Adjust VAR Model Using Longhand Syntax* Model Objects with Known Parameters* Model Objects with No Parameter Values* Model Objects with Selected Parameter Values* VAR Model Estimation* Preparing VAR Models for Fitting* Fitting Models to Data* Examining the Stability of a Fitted Model* 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* VAR Model Forecasting* Data Scaling* Calculating Impulse Responses* Generate Impulse Responses for a VAR model* 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* Design Matrix Structure for Including Exogenous Data* Estimation of Models that Include Exogenous Data* Implement Seemingly Unrelated Regression Analyses* 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* The Engle-Granger Test for Cointegration* Limitations of the Engle-Granger Test* Test for Cointegration Using the Engle-Granger Test* Estimate VEC Model Parameters Using egcitest* Simulate and Forecast a VEC Model* 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