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Econ 427 lecture 2 slides

Econ 427 lecture 2 slides. Lecture 2. Jan. 13, 2010. Anyone need syllabus? See pdf EViews documentation on CD-Rom Problem set 1 will be available by Tues at the latest. The forecasting problem.

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Econ 427 lecture 2 slides

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  1. Econ 427 lecture 2 slides

  2. Lecture 2. Jan. 13, 2010 • Anyone need syllabus? • See pdf EViews documentation on CD-Rom • Problem set 1 will be available by Tues at the latest.

  3. The forecasting problem • You’re given a forecasting assignment. What things do you need to consider before deciding how to develop your forecast? • Diebold’s 6 considerations for successful forecasting

  4. The decision environment • How will the forecast be used? What will constitute a “good” forecast? • What are the implications of making forecast errors? • How large are the costs of errors? • Are they symmetric? • An optimal forecast will be one that minimizes expected losses.

  5. Loss functions • Error • Loss • What characteristics would you expect a loss function to have? • Types of loss functions Lossfunction.xls • Absolute loss • Quadratic loss • Why is this one appealing/convenient? • Asymmetric loss functions • How do you decide which to use?

  6. Measures of Forecast Fit • Making it concrete: some common measures of forecast fit • Notation: error of a forecast made at time t of period t+h is:

  7. Measures of Forecast Fit • Mean absolute error MAE is • Mean squared error MSE is • (see pp 260-262 in book) • Look at my MAE/MSE forecast comparison example MaeMseExample_Mine.xls

  8. Measures of Forecast Fit • Do they give the same ranking? Need they always? • Would you want to use in-sample data for this?

  9. The forecast object • What kind of object are we trying to forecast? • Event outcome • Event timing • *Time series • What are examples of each? • Other considerations: availability and quality of data

  10. The forecast statement • What sort of forecast of that object do we want? • Point forecast • Interval forecast • Density forecast

  11. The forecast horizon • How far into the future do we need to predict? • The “h-step-ahead forecast” • also, h-step-ahead extrapolative forecasts • Likely dependence of optimal forecasting model on fcst horizon

  12. The information set. • What do we know that can inform the forecast?

  13. Optimal model complexity • The parsimony principle • more accurate param ests, easier interp, easier to commun intuition, avoids data mining • The shrinkage principle • imposing restriction—sometimes even if wrong!—can improve forecast performance • The KISS principle • Keep it sophisticatedly simple

  14. Next time… • Read Chapter 2 carefully before class.

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