Regression Models
description
Transcript of Regression Models
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Part 4: Prediction4-1/22
Regression ModelsProfessor William GreeneStern School of Business
IOMS DepartmentDepartment of Economics
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Part 4: Prediction4-2/22
Regression and Forecasting Models
Part 4 – Prediction
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Part 4: Prediction4-3/22
Prediction Use of the model for prediction
Use “x” to predict y based on y = β0 + β1x + ε Sources of uncertainty
Predicting ‘x’ first Using sample estimates of β0 and β1 (and,
possibly, σ instead of the ‘true’ values) Can’t predict noise, ε Predicting outside the range of experience –
uncertainty about the reach of the regression model.
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Part 4: Prediction4-4/22
Base Case Prediction For a given value of x*: Use the equation.
True y = β0 + β1x* + ε Obvious estimate: y = b0 + b1x
(Note, no prediction for ε) Minimal sources of prediction error
Can never predict ε at all The farther from the center of experience,
the greater is the uncertainty.
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Part 4: Prediction4-5/22
Prediction Interval for y|x*
0 1
22
0 1 e N 2i 1 i
Prediction includes a range of uncertaintyˆPoint estimate: y b b x*
The range of uncertainty around the prediction:
1 (x * x)b b x* 1.96 s 1+N (x x)
The usual 95% Due to ε Due to estimating β0 and β1 with b0 and b1
(Remember the empirical rule, 95% of the distribution within two standard deviations.)
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Part 4: Prediction4-6/22
Prediction Interval for E[y|x*]
0 1
22
0 1 e N 2i 1 i
Prediction includes a range of uncertaintyˆPoint estimate: y b b x*.
The range of uncertainty around the prediction:
1 (x * x)b b x* 1.96 SN (x x)
Not predicting ε.
The usual 95% Due to estimating β0 and β1 with b0 and b1
(Remember the empirical rule, 95% of the distribution within two standard deviations.)
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Part 4: Prediction4-7/22
Predicting y|x vs. Predicting E[y|x]Predicting y itself, allowing for in the prediction interval.
Predicting E[y], no provision for in the prediction interval.
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Part 4: Prediction4-8/22
Simpler Formula for Prediction
0 1
22 20 1 e
Prediction includes a range of uncertaintyˆPoint estimate: y b b x*
The range of uncertainty around the prediction:
1b b x* 1.96 s 1+ (x * x) SE(b)N
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Part 4: Prediction4-9/22
Uncertainty in Prediction
2 2 2e
1 1.96 s 1+ (x* x) (SE(b))N
The interval is narrowest at x* = , the center of our experience. The interval widens as we move away from the center of our experience to reflect the greater uncertainty.(1) Uncertainty about the prediction of x(2) Uncertainty that the linear relationship will continue to exist as we move farther from the center.
x
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Part 4: Prediction4-10/22
Prediction from Internet Buzz Regression
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Part 4: Prediction4-11/22
Prediction Interval for Buzz = .8
0 1
2 2 2e
2 2 2
Predict Box Office for Buzz = .8b +b x = -14.36 + 72.72(.8) = 43.82
1 s 1 (.8 Buzz) SE(b)N113.3863 1 (.8 .48242) 10.9462
13.93Interval = 43.82 1.96(13.93) = 16.52 to 71.12
Data obtained separatelyBuzz = 0.48242Max(Buzz)= 0.79
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Part 4: Prediction4-12/22
Predicting Using a Loglinear Equation Predict the log first
Prediction of the log Prediction interval – (Lower to Upper)
Prediction = exp(lower) to exp(upper)
This produces very wide intervals.
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Part 4: Prediction4-13/22
Interval Estimates for the Sample of Monet Paintings
ln (SurfaceArea)
ln (U
S$)
7.67.47.27.06.86.66.46.26.0
18
17
16
15
14
13
12
11
10
S 1.00645R-Sq 20.0%R-Sq(adj) 19.8%
Regression95% PI
Fitted Line Plotln (US$) = 2.825 + 1.725 ln (SurfaceArea)Regression Analysis: ln (US$) versus
ln (SurfaceArea) The regression equation isln (US$) = 2.83 + 1.72 ln (SurfaceArea)Predictor Coef SE Coef T PConstant 2.825 1.285 2.20 0.029ln (SurfaceArea) 1.7246 0.1908 9.04 0.000S = 1.00645 R-Sq = 20.0% R-Sq(adj) = 19.8%
Mean of ln (SurfaceArea) = 6.72918
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Part 4: Prediction4-14/22
Prediction for An Out of Sample Monet
Claude Monet: Bridge Over a Pool of Water Lilies. 1899. Original, 36.5”x29.”
2 2 2
2 2
lnSurface ln(36.5 29) 6.96461Prediction 2.83 1.72(6.96461) 14.809
1Uncertainty 1.96 1.00645 1 (6.96461 6.72918) (.1908)328
1.96 1.012942(1.003049) (.23453) (.1908)
1.96(1.008984) 1.977608Prediction Interval = 14.809 1.977608 = 12.83139 to 16.786608
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Part 4: Prediction4-15/22
Predicting y when the Model Describes log y
Predicted Price: Mean = Exp(a + bx ) = Exp(14.809 ) = $2
The inter
,700,641.
val predicts log price. What abo
78Upper Limit
ut the
= Exp(
price?
14.809+1.9776) = $19,513,166.53Lower Limit = Exp(14.809-1.9776) = $ 373,771.53
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Part 4: Prediction4-16/22
39.5 x 39.125. Prediction by our model = $17.903MPainting is in our data set. Sold for 16.81M on 5/6/04 Sold for 7.729M 2/5/01Last sale in our data set was in May 2004Record sale was 6/25/08. market peak, just before the crash.
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Part 4: Prediction4-17/22
http://www.nytimes.com/2006/05/16/arts/design/16oran.html
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Part 4: Prediction4-18/22
32.1” (2 feet 8 inches)
26.2” (2 feet 2.2”)
167” (13 feet 11 inches)
78.74” (6 Feet 7 inch)
"Morning", Claude Monet 1920-1926, oil on canvas 200 x 425 cm, Musée de l Orangerie, Paris France. Left panel
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Part 4: Prediction4-19/22
Predicted Price for a Huge PaintingRegression Equation: ln $ = 2.825 + 1.725 ln Surface Area Width = 167 Inches Height = 78.74 Inches Area = 13,149.58 Square inches, ln = 9.484 Predicted ln Price = 2.825 + 1.725 (9.484) = 19.185Predicted Price = exp(19.185) = $214,785,473.40
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Part 4: Prediction4-20/22
Prediction Interval for Price
22 2
e
Prediction Interval for ln Price is
1Predicted ln Price 1.96 S 1 ln Area* ln Area ( )
ln Area* = ln (167 78.74) = 9.484
ln Area = 6.72918 (computed from the data)S = 1.00645 (from
e SE bN
22 2
regression results)SE(b) = 0.1908
119.185 1.96 (1.00645) 1 9.484 6.72918 (.1908)328
19.185 2.228 = [16.957 to 21.413]Predicted Price = exp(16.957) to exp(21.413) =
$23,138,304 to $1,993,185,600
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Part 4: Prediction4-21/22
Use the Monet Model to Predict a Price for a Dali?
118” (9 feet 10 inches)
157”
(13
Feet
1 in
ch)
Hallucinogenic Toreador
26.2
” (2
feet
2.2
”) 32.1” (2 feet 8 inches)
Average Sized Monet
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Part 4: Prediction4-22/22
Forecasting Out of Sample
Income
G
2750025000225002000017500150001250010000
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3
S 0.370241R-Sq 88.0%R-Sq(adj) 87.8%
Regression95% PI
Fitted Line PlotG = 1.928 + 0.000179 Income
Per Capita Gasoline Consumption vs. Per Capita Income, 1953-2004.
How to predict G for 2017? You would need first to predict Income for 2017.How should we do that?
Regression Analysis: G versus Income The regression equation isG = 1.93 + 0.000179 IncomePredictor Coef SE Coef T PConstant 1.9280 0.1651 11.68 0.000Income 0.00017897 0.00000934 19.17 0.000S = 0.370241 R-Sq = 88.0% R-Sq(adj) = 87.8%