Econometric Modeling with Matlab. Time Series Regression Models

By: Noriega, B.

Time series regression models attempt to explain the current response using the response history (autoregressive dynamics) and the transfer of dynamics from relevant predictors (or otherwise). Theoretical frameworks for potential relationships among variables often permit different representations of the system.Use time series regression models to analyze time series data, which are measurements that you take at successive time points. For example, use time series regression modeling to: Examine the linear effects of the current and past unemployment rates and past inflation rates on the current inflation rate, Forecast GDP growth rates by using an ARIMA model and include the CPI growth rate as a predictor and Determine how a unit increase in rainfall, amount of fertilizer, and labor affect crop yield.The more important topics in this book are the next: - "Time Series Regression Models"- "Regression Models with Time Series Errors"- "Create Regression Models with ARIMA Errors"- "Specify the Default Regression Model with ARIMA Errors"- "Modify regARIMA Model Properties"- "Create Regression Models with AR Errors"- "Create Regression Models with MA Errors"- "Create Regression Models with ARMA Errors"- "Create Regression Models with ARIMA Errors"- "Create Regression Models with SARIMA Errors"- "Specify Regression Model with SARIMA Errors"- "Specify ARIMA Error Model Innovation Distribution"- "Impulse Response of Regression Models with ARIMA Errors"- "Plot Impulse Response of Regression Model with ARIMA Errors"- "Maximum Likelihood Estimation of regARIMA Models"- "regARIMA Model Estimation Using Equality Constraints"- "Presample Values for regARIMA Model Estimation"- "Initial Values for regARIMA Model Estimation"- "Optimization Settings for regARIMA Model Estimation"- "Estimate a Regression Model with ARIMA Errors"- "Estimate a Regression Model with Multiplicative ARIMA Errors"- "Select Regression Model with ARIMA Errors"- "Choose Lags for ARMA Error Model"- "Intercept Identifiability in Regression Models with ARIMA Errors"- "Alternative ARIMA Model Representations"- "Simulate Regression Models with ARMA Errors"- "Simulate Regression Models with Nonstationary Errors"- "Simulate Regression Models with Multiplicative Seasonal Errors"- "Monte Carlo Simulation of Regression Models with ARIMA Errors"- "Presample Data for regARIMA Model Simulation"- "Transient Effects in regARIMA Model Simulations"- "Forecast a Regression Model with ARIMA Errors"- "Forecast a Regression Model with Multiplicative Seasonal ARIMA Errors"- "Verify Predictive Ability Robustness of a regARIMA Model"- "MMSE Forecasting Regression Models with ARIMA Errors"- "Monte Carlo Forecasting of regARIMA Models"

• Format: Paperback
• Language: English
• ISBN: 1798144085
• ISBN13: 9781798144084
• Release Date: January 1
• Publisher: Independently Published
• Length: 278 Pages
• Weight: 0.91 lbs.
• Dimensions: 0.6" x 6.0" x 9.0"