Forecasting with Dynamic Regression Models
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
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A Primer on ARIMA Models
Federal Government Receipts ARIMA
A Primer on Regression Models
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absolute value ACF and PACF approximate ARIMA model ARIMA process autocorrelation pattern Box and Jenkins Box-Cox transformation Box-Jenkins component computed consider correlation Data Appendix decay pattern denoted detection discussed in Chapter distributed lag disturbance series DR model dynamic regression effect Equation ESACF estimated coefficients estimated disturbance Estimation Results EWMA example expected value exponential exponential decay feedback forecast error form transfer function identify impulse response impulse response function input variable Koyck model leading indicator data linear Lydia Pinkham matrix nonseasonal nonstationary normally distributed null hypothesis observations original metric outliers partial autocorrelation past values period preliminary LTF model procedure pulse intervention random shock rational form transfer regression model residual SACF residual series residuals from model sales and leading sample SCA System seasonal differencing shipments shown in Table shows significant single-equation SPACF standard deviation standard errors standardized residuals stationary statistic stochastic cycle theoretical ACFs variance weights zero