Elements of ForecastingWritten by a leading expert on forecasting, this concise and modern text focuses on the core techniques of widest applicability and assumes only an elementary background in statistics. It is applications-oriented and illustrates all methods with detailed real-world applications, many of them international in flavor, designed to mimic typical forecasting situations. In many chapters, the application is the centerpiece of the presentation. |
Contents
CONTENTS | 1 |
Graphing Four Components of Real | 4 |
Looking Ahead | 9 |
Copyright | |
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Common terms and phrases
1-step-ahead analysis approximation ARMA models asymmetric loss autocorrelation function autocorrelations and partial autoregressive B₁ Chapter compute conditional mean conditional variance correlogram covariance covariance stationary cycles dataset density forecasts Dependent variable Diebold discussion Displacement distributed lag disturbances Durbin-Watson statistic dynamics Econometrics Economic example FIGURE forecast error forecast error variance forecasting models GARCH graphics housing starts hypothesis information set innovations interval forecast Journal lag operator least squares linear regression linear trend Ljung-Box Log Liquor Sales loss function model selection modeling and forecasting moving average multivariate nonlinear optimal forecasts out-of-sample parameter estimation partial autocorrelation partial autocorrelation function point forecast prediction Quadratic Trend recursive regression model residual plot right-hand-side variables sample autocorrelations scatterplot Schwarz criterion seasonal dummies serial correlation simply squared residuals standard error Statistics stochastic sum of squared symmetric trend model unconditional unit root univariate volatility white noise Y₁ ει σ²