Econometric Modeling with Matlab. Conditional Mean Time Series Models PDF Download

Author: B. Noriega
Publisher: Independently Published
ISBN: 9781798409312
Category : Mathematics
Languages : en
Pages : 240

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Book Description
For a random variable yt, the unconditional mean is simply the expected value, E( yt ) . In contrast, the conditional mean of yt is the expected value of yt given a conditioning set of variables, Ωt. A conditional mean model specifies a functional form for E( yt Ωt). For a static conditional mean model, the conditioning set of variables is measured contemporaneously with the dependent variable yt. An example of a static conditional mean model is the ordinary linear regression model. In time series econometrics, there is often interest in the dynamic behavior of a variable over time. A dynamic conditional mean model specifies the expected value of yt as a function of historical information. The more important topics in this book are the next: -"Conditional Mean Models"-"Specify Conditional Mean Models" -"Autoregressive Model" -"AR Model Specifications" -"Moving Average Model" -"MA Model Specifications" -"Autoregressive Moving Average Model" -"ARMA Model Specifications" -"ARIMA Model" -"ARIMA Model Specifications" -"Multiplicative ARIMA Model"-"Multiplicative ARIMA Model Specifications"-"Specify Multiplicative ARIMA Model"-"ARIMA Model Including Exogenous Covariates"-"ARIMAX Model Specifications" -"Modify Properties of Conditional Mean Model Objects" -"Specify Conditional Mean Model Innovation Distribution" -"Specify Conditional Mean and Variance Models" -"Impulse Response Function" -"Plot the Impulse Response Function" -"Box-Jenkins Differencin vs. ARIMA Estimation" -"Maximum Likelihood Estimation for Conditional Mean Models" -"Conditional Mean Model Estimation with Equality Constraints" -"Presample Data for Conditional Mean Model Estimation" -"Initial Values for Conditional Mean Model Estimation" -"Optimization Settings for Conditional Mean Model Estimation" -"Estimate Multiplicative ARIMA Model" -"Model Seasonal Lag Effect 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 Effect in Conditional Mean Model Simulations" -"Simulate Stationary Processes" -"Simulate Trend-Stationary and Difference-Stationar Processes" -"Simulate Multiplicative ARIMA Models" -"Simulate Conditional Mean and Variance Models" -"Monte Carlo Forecasting of Conditional Mean Models" -"MMSE Forecasting of Conditional Mean Models" -"Convergence of AR Forecasts" -"Forecast Multiplicative ARIMA Model" -"Forecast Conditional Mean and Variance Model"

Author: B. Noriega
Publisher: Independently Published
ISBN: 9781798663752
Category : Mathematics
Languages : en
Pages : 150

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Book Description
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.

Author: B. Noriega
Publisher: Independently Published
ISBN: 9781798968253
Category : Mathematics
Languages : en
Pages : 278

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Book Description
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"

Author: A. Smith
Publisher:
ISBN: 9781979593984
Category :
Languages : en
Pages : 210

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Book Description
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.In time series econometrics, there is often interest in the dynamic behavior of a variable over time. A dynamic conditional mean model specifies the expected value of yt as a function of historical information. The constant mean assumption of stationarity does not preclude the possibility of a dynamic conditional expectation process. The serial autocorrelation between lagged observations exhibited by many time series suggests the expected value of yt depends on historical information. Special cases of stationary stochastic processes are the autoregressive (AR) model, moving average (MA) model, and the autoregressive moving average (ARMA) model. ARIMAX model contains coefficients corresponding to the effect that the aditional predictors have on the response.This book develops AR, MA, ARMA, ARIMA and ARIMAX time series models.

Author: A. Smith
Publisher:
ISBN: 9781979581332
Category :
Languages : en
Pages : 250

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Book Description
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 probabilistic time series model is necessary for a wide variety of analysis goals ,including regression inference, forecasting, and Monte Carlo simulation. When selecting a model, aim to find the most parsimonious model that adequately describes your data. Asimple model is easier to estimate, forecast, and interpret*Specification tests help you identify one or more model families that could plausiblydescribe the data generating process.*Model comparisons help you compare the fit of competing models, with penalties for complexity.*Goodness-of-fit checks help you assess the in-sample adequacy of your model, verify that all model assumptions hold, and evaluate out-of-sample forecast performance.Model selection is an iterative process. When goodness-of-fit checks suggest model assumptions are not satisfied-or the predictive performance of the model is not satisfactory-consider making model adjustments. Additional specification tests, model comparisons, and goodness-of-fit checks help guide this process..The most important content is the following:* Econometrics Toolbox Product Description* Econometric Modeling* Econometrics Toolbox Model Objects, Properties, and Methods* Stochastic Process Characteristics* Data Transformations* Data Preprocessing* Trend-Stationary vs. Difference-Stationary Processes* Nonstationary Processes* Trend Stationary* Difference Stationary* Specify Lag Operator Polynomials* Lag Operator Polynomial of Coefficients* Difference Lag Operator Polynomials* Nonseasonal Differencing* Nonseasonal and Seasonal Differencing* Time Series Decomposition* Moving Average Filter* Moving Average Trend Estimation* Parametric Trend Estimation* Hodrick-Prescott Filter* Using the Hodrick-Prescott Filter to Reproduce Their* Original Result* Seasonal Filters* Seasonal Adjusment* Seasonal Adjustment Using a Stable Seasonal Filter* Seasonal Adjustment Using S(n,m) Seasonal Filters* Box-Jenkins Methodology* Box-Jenkins Model Selection* Autocorrelation and Partial Autocorrelation* Theoretical ACF and PACF* Sample ACF and PACF* Ljung-Box Q-Test* Detect Autocorrelation* Engle's ARCH Test* Detect ARCH Effects* Unit Root Nonstationarity* Unit Root Tests* Assess Stationarity of a Time Series* Information Criteria* Model Comparison Tests* Likelihood Ratio Test* Lagrange Multiplier Test* Wald Test* Covariance Matrix Estimation* Conduct a Lagrange Multiplier Test* Conduct a Wald Test* 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 a Confidence Band Using HAC Estimates* Change the Bandwidth of a HAC Estimator* Check Model Assumptions for Chow Test* Power of the Chow Test

Author: K. Lorentz
Publisher: Lulu.com
ISBN: 9781716568534
Category : Computers
Languages : en
Pages : 232

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Book Description
For a random variable yt, the unconditional mean is simply the expected value, E( yt ). In contrast, the conditional mean of yt is the expected value of yt given a conditioning set of variables, Ot. A conditional mean model specifies a functional form for E(yt -Ot). For a static conditional mean model, the conditioning set of variables is measured contemporaneously with the dependent variable yt. An example of a static conditional mean model is the ordinary linear regression model. In time series econometrics, there is often interest in the dynamic behavior of a variable over time. A dynamic conditional mean model specifies the expected value of yt as a function of historical information. This book develops the most important conditional time series models: ARIMA models and ARIMAX models across Box-Jenkins Methodology. Examples developed with MATLAB are presented

Author: Mara Prez
Publisher: CreateSpace
ISBN: 9781502348050
Category : Mathematics
Languages : en
Pages : 204

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Book Description
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 Variance Models GARCH Model Specify GARCH Models Using garch GARCH Model Specifications GARCH Model with a Mean Offset GARCH Model with Nonconsecutive Lags GARCH Model with Known Parameter Values GARCH Model with a t Innovation Distributio EGARCH Model Specify EGARCH Models Using egarch EGARCH Model Specifications EGARCH Model with a Mean Offset EGARCH Model with Nonconsecutive Lags EGARCH Model with Known Parameter Values EGARCH Model with a t Innovation Distribution GJR Model Specify GJR Models Using gjr GJR Model Specifications GJR Model with a Mean Offset GJR Model with Nonconsecutive Lags GJR Model with Known Parameter Values GJR Model with a t Innovation Distribution Modify Properties of Conditional Variance Model Objects Specify the Conditional Variance Model Innovation Distribution Specify a Conditional Variance Model Maximum Likelihood Estimation for Conditional Variance Models Innovation Distribution Loglikelihood Functions Conditional Variance Model Estimation with Equality Constraints Presample Data for Conditional Variance Model EstimationInitial Values for Conditional Variance Model Estimation Optimization Settings for Conditional Variance Model Estimation Conditional Variance Model Constraints Infer Conditional Variances and Residuals Likelihood Ratio Test for Conditional Variance Models Compare Conditional Variance Models Using Information Criteria Monte Carlo Simulation of Conditional Variance Models Presample Data for Conditional Variance Model Simulation Simulate GARCH Models Assess the EGARCH Forecast Bias Using Simulations Simulate Conditional Variance Model Monte Carlo Forecasting of Conditional Variance Models MMSE Forecasting of Conditional Variance Models EGARCH MMSE Forecasts Forecast GJR Models Forecast Conditional Variance Model Including an Exogenous Regression Component ARMAX Model Specifying ARMAX Models Using garchset Maximum Likelihood Estimation Initial Parameter Values for Optimization GARCHFIT Examples Estimation Presample Data GARCHSIM Examples Simulation Presample Data MMSE Forecasting GARCHPRED Examples

Author: Mara Prez
Publisher: CreateSpace
ISBN: 9781502346384
Category : Mathematics
Languages : en
Pages : 192

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Book Description
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

Author: R. Prost
Publisher: Lulu.com
ISBN: 9781716568213
Category : Computers
Languages : en
Pages : 150

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Book Description
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 and Leverage effects. 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. In this book a variety of examples are presented, all of them treated with MATLAB.

Author: Thomas B. Fomby
Publisher: Emerald Group Publishing
ISBN: 0762312742
Category : Business & Economics
Languages : en
Pages : 407

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Book Description
Talks about the time varying betas of the capital asset pricing model, analysis of predictive densities of nonlinear models of stock returns, modelling multivariate dynamic correlations, flexible seasonal time series models, estimation of long-memory time series models, application of the technique of boosting in volatility forecasting, and more.