1 econ 240a power 7. 2 this week, so far §normal distribution §lab three: sampling distributions...
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Econ 240A
Power 7
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This Week, So Far
Normal DistributionLab Three: Sampling DistributionsInterval Estimation and Hypothesis Testing
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Outline
Distribution of the sample varianceThe California Budget: Exploratory Data
AnalysisTrend ModelsLinear Regression ModelsOrdinary Least Squares
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PopulationRandom variable xDistribution f(f ?
Sample
Sample Statistic:
),(~ 2Nx
Sample Statistic
)1/()( 2
1
2
nxxsn
ii
Pop.
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The Sample Variance, s2
22
1
22
/*)1(
)1/(])([
sn
nxixsn
i
Is distributed 2 with n-1 degrees of
freedom (text, 12.2 “inference about a population variance)(text, pp. 266-270, Chi-Squared distribution)
n
i
n
ii zxxsn
1 1
22222 /)(/)1(
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TextChi-SquaredDistribution
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TextChi-SquaredTable 5Appendixp. B-10
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Example: Lab Three50 replications of a sample of size 50
generated by a Uniform random number generator, range zero to one, seed =20. expected value of the mean: 0.5 expected value of the variance: 1/12
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Histogram of 50 sample means
05
1015
20
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0.25
0.35
0.45
0.55
0.65
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0.85
0.95
Mor
e
Sample Mean
Fre
qu
en
cy
Histogram of 50 Sample Means, Uniform, U(0.5, 1/12)
Average of the 50 sample means: 0.4963
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Histogram of 50 Sample Variances
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Sample variance
Fre
qu
en
cy
Histogram of 50 sample variances, Uniform, U(0.5, 0.0833)
Average sample variance: 0.0832
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Confidence Interval for the first sample variance of 0.07667A 95 % confidence interval
95.0]0562.01161.0[
95.0]01.19/161.8[
95.0]42.71/07667.0*4936.32[
95.0]42.71/*)1(36.32[
95.0]42.7136.32[
2
2
2
22
2
p
p
p
snp
p
Where taking the reciprocal reverses the signs of the inequality
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The UC Budget
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The UC Budget
The part of the UC Budget funded by the state from the general fund
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Total General Fund ExpendituresAppendix, p.11Schedule 6
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UC General Fund Expenditures, Appendix p. 33
2003-04, General fund actual, $2,901,257,000
2004-05, estimated $2,175,205,000
2005-06, estimated $2,806,267,000
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UC General Fund Expenditures, Appendix p. 46
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UC Budget in Nominal Millions: 1968-69 through 2007-08
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Fiscal Year
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ion
s $
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How to Forecast the UC Budget?
Linear Trendline?
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Trend Models
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UC Budget in Nominal Millions: 1968-69 through 2007-08
y = 80.323x + 36.343
R2 = 0.9431
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Fiscal Year
Mill
ion
s $
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UC Budget In Millions of Nominal Dollars, 1968-69 to 2006-07
y = 80.143x + 38.773
R2 = 0.9385
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Mill
ion
s $
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UC Budget in Nominal Millions: 1968-69 through 2007-08
y = 80.323x + 36.343
R2 = 0.9431
0
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Fiscal Year
Mill
ion
s $
Slope: increase of 80.323 Million $ per yearGovernor’s Proposed Increase 186.712 Million $
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Linear Regression Trend Models
A good fit over the years of the data sample may not give a good forecast
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How to Forecast the UC Budget?
Linear trendline?Exponential trendline ?
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Trend Models
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An Application
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Time Series Trend Analysis
Two Steps Select a trend model Fit the trend model
• Graphically
• algebraically
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Trend Models
Linear Trend: y(t) = a + b*t +e(t) dy(t)/dt = b
Exponential trend: z(t) = exp(c + d*t + u(t)) ln z(t) = c + d*t + u(t) (1/z)*dz/dt = d
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Linear Trend Model Fitted to UC Budget UCBUDB(t) = 0.0363 + 0.0803*t, R2 = 0.943
UC Budget in Nominal Billions $: 1968-69 through 2007-08
y = 0.0803x + 0.0363
R2 = 0.9431
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Time Series ModelsLinear
UCBUD(t) = a + b*t + e(t) where the estimate of a is the intercept: $0.0363
Billion in 68-69 where the estimate of b is the slope: $0.0803
billion/yr where the estimate of e(t) is the the difference
between the UC Budget at time t and the fitted line for that year
Exponential
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UC Budget in Billions $:1968-69 through 2007-08
y = 0.3838e0.0611x
R2 = 0.9047
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Exponential Trend Model Fitted to UC Budget
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-1.5
-1.0
-0.5
0.0
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1.0
1.5
0 10 20 30 40
TIME
LN
UC
BU
DB
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lnUCBudB(t) = a-hat + b-hat*timelnUCBudB(t) = -0.896566 + 0.0611 *timeExp(-0.896566) = 0.408 B (1968-69) intercept
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-0.4
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70 75 80 85 90 95 00 05
Residual Actual Fitted
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Time Series Models
Exponential UCBUD(t) = UCBUD(68-69)*eb*teu(t)
UCBUD(t) = UCBUD(68-69)*eb*t + u(t)
where the estimate of UCBUD(68-69) is the estimated budget for 1968-69
where the estimate of b is the exponential rate of growth
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Linear Regression Time Series Models
Linear: UCBUD(t) = a + b*t + e(t)How do we get a linear form for the
exponential model?
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Time Series Models
Linear transformation of the exponential take natural logarithms of both sides ln[UCBUD(t)] = ln[UCBUD(68-69)*eb*t + u(t)] where the logarithm of a product is the sum of
logarithms: ln[UCBUD(t)] = ln[UCBUD(68-69)]+ln[eb*t + u(t)] and the logarithm is the inverse function of the
exponential: ln[UCBUD(t)] = ln[UCBUD(68-69)] + b*t + u(t) so ln[UCBUD(68-69)] is the intercept “a”
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Naïve Forecasts
Averageforecast next year to be the same as this
year
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A Naive Budget Forecast
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Fiscal Year
$ M
illi
on
s
UC Budget
Average
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UC Budget Forecasts for 2006-07
Method Increase Forecast
Linear Trend $80.5 M $2,886,707,000
Exponential Trend 6.4% $2,985,047,000*
Same as 2005-06 0 $2,806,207,000
Average 0 $1,603,671,000
* 1.068x$2,806,207,000; exponential trendline forecast ~$4.5 B
Actual:$2,806,207,000 in Governor’s Budget Summary for 05-06
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Time Series Forecasts
The best forecast may not be a regression forecast
Time Series Concept: time series(t) = trend + cycle + seasonal + noise(random or error)
fitting just the trend ignores the cycleUCBUD(t) = a + b*t + e(t)
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Application of Bivariate Plot
O-Ring FailurePlot zeros (no failure) and the ones (failure)
versus launch temperature for the 24 launches prior to Challenger
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O-Ring Failure (yes=1, No=0) Versus Launch Temperature
y = -0.0367x + 2.8583
R2 = 0.3254
-0.2
0
0.2
0.4
0.6
0.8
1
30 40 50 60 70 80 90
Launch Temperatue
Pro
ba
bili
ty
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O-Ring Failure (yes=1, No=0) Versus Launch Temperature
y = -0.0367x + 2.8583
R2 = 0.3254
-0.2
0
0.2
0.4
0.6
0.8
1
30 40 50 60 70 80 90
Launch Temperatue
Pro
ba
bili
ty
Linear Approximation to Backward Sigmoid
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Ordinary Least Squares
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Criterion for Fitting a LineMinimize the sum of the absolute value of
the errors?Minimize the sum of the square of the
errors easier to use
error is the difference between the observed value and the fitted value example UCBUD(observed) - UCBUD(fitted)
)(ˆ)(ˆ tytye
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The fitted value:
The fitted value is defined in terms of two parameters, a and b (with hats), that are determined from the data observations, such as to minimize the sum of squared errors
tbaty *ˆˆ)(ˆ
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Minimize the Sum of Squared Errors
062005
691968
2
2062005
691968
062005
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2
]*ˆˆ)([
])(ˆ)([ˆ
tbaty
tytye
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How to Find a-hat and b-hat?
Methodology grid search differential calculus likelihood function
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Grid Search, a-hat=0, b-hat=80
FiscalYear
Timeindex
UCBUD y-hat Error-hat
68-69 0 291.3 0 291.3
69-70 1 329.3 80 249.3
70-71 2 335.9 160 175.9
… … … … …
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Grid Search
a-hat
-
+
+-0
b-hat
Find the point where the sum of squared errors is minimum
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Differential CalculusTake the derivative of the sum of squared errors with
respect to a-hat and with respect to b-hat and set to zero.
Divide by -2*n
or
0]1[]*ˆˆ)([2ˆ/]*ˆˆ)([0605
6968
0605
6968
2
tbatyatbaty
0605
6968
0605
6968
/ˆˆ/)( ntbanty
tbay *ˆˆ
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Least Squares Fitted Parameters
So, the regression line goes through the sample means.
Take the other derivative:
divide by -2
0605
6968
0605
8968
2 0]][*ˆˆ)([2ˆ/]*ˆˆ)([ ttbatybtbaty
0605
6968
0605
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20605
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ˆˆ*)( tbtatty
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Ordinary Least Squares(OLS)
Two linear equations in two unknowns, solve for b-hat and a-hat.
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O-Ring Failure Versus launch temperature
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