Byron Gangnes Econ 427 lecture 2 slides. Byron Gangnes Lecture 2. Jan. 13, 2010 Anyone need syllabus? See pdf EViews documentation on CD- Rom Problem.

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Presentation transcript:

Byron Gangnes Econ 427 lecture 2 slides

Byron Gangnes 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.

Byron Gangnes 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

Byron Gangnes 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.

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

Byron Gangnes 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:

Byron Gangnes Measures of Forecast Fit –Mean absolute error MAE is –Mean squared error MSE is (see pp in book) –Look at my MAE/MSE forecast comparison example MaeMseExample_Mine.xls

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

Byron Gangnes 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

Byron Gangnes The forecast statement What sort of forecast of that object do we want? –Point forecast –Interval forecast –Density forecast

Byron Gangnes 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

Byron Gangnes The information set. What do we know that can inform the forecast?

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 Byron Gangnes Optimal model complexity

Read Chapter 2 carefully before class. Byron Gangnes Next time…