Chapter 5: The ARIMA Model: Special Applications
5.1 Regression with Time Series Errors and Unequal Variances
5.1.2 Example: Energy Demand at a University
5.1.4 ARCH, GARCH, and IGARCH for Unequal Variances
5.2.1 Cointegration and Eigenvalues
5.2.2 Impulse Response Function
5.2.3 Roots in Higher-Order Models
5.2.4 Cointegration and Unit Roots
5.2.6 Estimation of the Cointegrating Vector
5.2.7 Intercepts and More Lags
5.2.9 Interpretation of the Estimates
5.2.10 Diagnostics and Forecasts
5.1 Regression with Time Series Errors and Unequal Variances
Regression models can contain a wide variety of inputs, but for ordinary least squares ...
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