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
e
 y
L
( e )
 ˆ
• 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?
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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: e t
 h , t
 y t
 h
 y t
 h , t
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Measures of Forecast Fit
– Mean absolute error MAE is
MAE

1
T
T  t

1 e t
 h , t
– Mean squared error MSE is
MSE

T
1 T  t

1 e t t
2
 h , , t t
• (see pp 260-262 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?
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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
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The forecast statement
• What sort of forecast of that object do we want?
– Point forecast
– Interval forecast
– Density forecast
   
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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
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The information set.
• What do we know that can inform the forecast?
 univariate
T
 multivariate
T

 y
T

 y
T
, y
T

1
,..., y
1

, x
T
, y
T

1
, x
T

1
,..., y
1
, x
1

Byron Gangnes

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