Overview

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, t. A conditional mean model specifies a functional form for E( yt t). 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

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9781798409312

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