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| Choosing the Right Type of Forecasting Model | |
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| Introduction | |
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| Statistics, Econometrics, and Forecasting | |
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| Concept of Forecast Accuracy: Compared to What? | |
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| Structural Shifts in Parameters | |
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| Model Misspecification | |
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| Missing, Smoothed, Preliminary, or Inaccurate Data | |
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| Changing Expectations by Economic Agents | |
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| Policy Shifts.Unexpected Changes in Exogenous Variables | |
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| Incorrect Assumptions about Exogenity | |
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| Error Buildup in Multi-Period Forecasts | |
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| Alternative Types of Forecasts | |
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| Point or Interval | |
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| Absolute or Conditional | |
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| Alternative Scenarios Weighed by Probabilities | |
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| Asymmetric | |
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| Single or Multi Period | |
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| Short Run or Long Range | |
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| Forecasting Single or Multiple Variables | |
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| Some Common Pitfalls in Building Forecasting Equations | |
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| Useful Tools for Practical Business Forecasting | |
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| Introduction | |
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| Types and Sources of Data | |
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| Time Series, Cross Section, and Panel Data | |
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| Basic Sources of U.S. Government Data | |
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| Major Sources of International Government Data | |
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| Principal Sources of Key Private Sector Data | |
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| Collecting Data from the Internet | |
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| Forecasting Under Uncertainty | |
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| Utilizing Graphs and Charts | |
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| Mean and Variance | |
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| Goodness of Fit Statistics | |
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| Covariance and Correlation Coefficients | |
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| Standard Errors and t-ratios.F-ratios and Adjusted R-squared | |
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| Using the EViews Statistical Package | |
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| Utilizing Graphs and Charts | |
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| Checklist Before Analyzing Data | |
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| Adjusting for Seasonal Factors | |
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| Checking for Outlying Values | |
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| Using Logarithms and Elasticities | |
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| The General Linear Regression Model | |
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| Introduction | |
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| The General Linear Model | |
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| The Bivariate Case.Desirable Properties of Estimators | |
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| Expanding to the Multivariate Case | |
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| Uses and Misuses of R-Bar Squared | |
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| Differences Between R-Square and R-Bar Square | |
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| Pitfalls in Trying to Maximize R-Bar Square | |
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| An Example: the Simple Consumption Function | |
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| Measuring And Understanding Partial Correlation | |
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| Covariance and the Correlation Matrix | |
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| Partial Correlation Coefficients | |
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| Pitfalls Of Stepwise Regression | |
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| Testing and Adjusting for Autocorrelation | |
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| Why Autocorrelation Occurs and What it Means | |
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| Durbin-Watson Statistic to Measure Autocorrelation | |
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| Autocorrelation Adjustments: Cochrane-Orcutt and Hildreth-Lu | |
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| Higher Order Autocorrelation | |
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| Overstatement of t-ratios When Autocorrelation is Present | |
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| Pitfalls of Using the Lagged Dependent Variable | |
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| Testing and Adjusting for Heteroscedasticity | |
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| Causes of Heteroscedasticity in Cross-Section and Time-Series Data | |
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| Measuring and Testing for Heteroscedasticity | |
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| Getting Started: An Example in Eviews | |
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| Predicting Retail Sales for Hardware Stores | |
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| German Short-Term Interest Rates | |
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| Lumber Prices | |
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| Additional Topics for Single-Equation Regression Models | |
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| Introduction | |
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| Problems Caused by Multicollinearity | |
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| Eliminating or Reducing Spurious Trends | |
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| Demand for Airline Travel | |
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| Log-Linear Transformation | |
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| Percentage First Differences | |
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| Ratios | |
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| Deviations Around Trends | |
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| Weighted Least Squares | |
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| Summary and Comparison of Methods | |
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| Distributed Lags | |
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| General Discussion of Distributed Lags | |
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| Polynomial Distributed Lags | |
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| General Guidelines for Using PDLs | |
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| Treatment of Outliers and Issues of Data Adequacy | |
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| Outliers | |
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| Missing Observations | |
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| General Comments of Data Adequacy | |
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| Uses and Misuses of Dummy Variables | |
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| Single-Event Dummy Variables | |
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| Changes in Dummy Variables for Institutional Structure | |
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| Changes in Slope Coefficients | |
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| Nonlinear Regressions | |
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| Log-Linear Regressions | |
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| Quadratic and Other Powers, Including Inverse | |
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| Ceilings, Floors, and Kronecker Deltas: Linearizing with Dummy Variables | |
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| General Steps For Formulating A Multiple Regression Equation | |
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| The Consumption Function | |
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| Case | |