Thurman Model 1 Cross Sectional ECN 405

Elizabeth Thurman ECN 405 2L
1
Model 1 Paper
Cross Sectional
Introduction:
The cost of housing is an important factor when choosing where to live and also
in considering the economic well-being of a state. High housing costs can be an
indication of higher paying jobs and higher prices in general in a particular area. When
housing prices are increased, it indicates an increased demand for housing in that area.
Often, when prices are high, one must achieve a certain level of education in order to
obtain a high paying job in a high-priced area.
The question I would like to analyze is how personal income affects housing
prices in a state, and also how level of education ties in with level of income to affect
said housing prices. Therefore, in this paper, I will be using personal income and level
of education as my testing variables in a regression analysis to see if the amount of
money a consumer makes, and their level of education will have an effect on the
dependent variable, housing prices.
Also, I will be exploring the impact of income and education on home mortgages
throughout this paper by using five related studies in the form of journal articles. I will
be exploring how each article relates and shapes my model and question. I will then
pose my economic model that I will use to answer my question along with its estimation
and corresponding graphs.

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Further, I will test to see if I have evidence of heteroscedasticity which could
imply that I have a violation of constant variance in my error terms. I will then test for
endogeneity which will allow me to assess whether there is a correlation between an
independent variable and an error term. If the test results show that I do have an
endogenous model, my regression coefficient would cause the model to be biased, and
therefore violating the OLS rules for being the best estimator.
After testing for homoscedasticity and endogeneity, I will do a Ramsey reset test
for zero mean to see if my model is specified correctly, test for normality, and preform a
Wald test. Then, I will perform a final weighted estimation on my variables and then test
a binary model for states west versus east of the Mississippi River.
Review of the literature:
The article by Stephen Malpezzi, "Housing prices, externalities, and regulation in
US metropolitan areas" analyzes the determinants of housing prices which vary widely
across the United States. The study focuses mainly on city and metropolitan areas. It
uses a simple supply and demand framework to assess how regulatory actions effect
housing prices and uses factors such as income and population changes.
This article has helped me to shape my model because of the use of population
and income in relation to housing prices. I have included both as variables in my model
to study their effect. In Malpezzi’s article, I like that he related the housing prices to
metropolitan areas and chose to do my binary testing based on states east and west of
the Mississippi. This is because although both coasts tend to have a relatively large
number of metropolitan areas compared to the “inner states”, the east coast states

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seem to have more populated areas throughout. Highly populated areas tend to have
high demand for housing, thus increasing the prices of homes (or other housing
amenities). This is why I especially wanted to include population; however, I used
population for an entire state which will not give me the more specific relation to housing
prices that I would have liked.
The article "Education and income" by Hendrik S. Houthakker, analyzes income
levels in relation to a person’s level of education. It is performed as a cross sectional
analysis of different age groups across a single year. The article concludes that “capital
values increase uniformly as education increases”. However, it finds that those with
college levels 1-3 do not fare as well as those with only a high school diploma.
This concept led me to use the testing variable ‘level of education’ because those
who afford higher priced homes may be correlated with higher paying jobs which would
most likely require more than a high school diploma. Because the study found a
positive correlation between level of education and level of income, I decided it would
be a reasonable variable to use along with the amount of personal income.
In the article "Valuation of education and crime neighborhood characteristics
through hedonic housing prices" by Robin A. Dubin and Allen C. Goodman, housing
prices are studied as a bundle of neighborhood characteristics. Among the
characteristics in the bundle are those of crime and neighborhood schools. The study
found that housing values were influenced in the Baltimore metropolitan area
significantly when the two variables were studied together.

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This article inspired me to use state crime rates in 2010 to see how it affects the
cost of housing. Because one would intuitively guess that housing prices are cheaper
where there is more crime, I would like to test for a correlation. Many homeowners want
to live in an area where there is low crime not only for the safety of their family and safe
schools, but also for the safety of their belongings. Areas with high breaking and
entering rates may also drive the nearby cost of housing down. However, sometimes it
costs more to live in a city where the crime rates tend to be higher. This is another
reason I would also like to add crime rates as a variable. However, I will be using crime
as the amount of burglaries and will use it as a state data and not specifying it to
suburban, city, or rural housing, although this would be an interesting study.
The article "Housing and income" by Alan R. Winger finds that income and
housing do have a relationship and should involve other factors such as the permanent
income of an individual. In the past, studies have concluded that there is a relationship;
however, the specific factors involved where not clear. The article notes that you must
take into account that the decision to purchase a home was made in the past relative to
when each study is performed. Therefore it is harder to conclude what factors are
considered when a consumer decides to buy their home. The article mentions a paper
by Margaret Reid who studied the effects and also found that there is a strong
correlation between income and the housing market.
Because a strong correlation was found, and the two variables of housing related
to income are widely studied, I was inspired to also test for a correlation between the
two. If I were to analyze a second model, I would include a test for income over a
certain time period to see if it fluctuates with housing prices because income can also

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fluctuate. Thus, what a consumer may be able to afford in one year, may not be true in
the next.
Similarly, in the article, "Housing and permanent income: Tests based on a
three-year re-interview survey” by Lee, Tong Hun, the author points out that as
consumption is tested against income, income is usually tested at a point in time.
Instead, he notes, particularly with the consumption and purchase of mortgage loans
(housing), it is better to test the relationship with a fixed income over time. This is
because purchasing power may fluctuate and although a consumer may be making a lot
in a particular year, they may not make as much in following years and therefore not
affect the mortgage market as greatly.
This article gave some great insight into the notion that it must be kept in mind
we are testing income and housing at one point in time. Although results were found,
indication that income over time is a more accurate measure when related to housing
consumption, I decided to keep my variable of income because I am interested to see if
there is any correlation with housing prices combined with other factors such as the
population’s level of education, level of crime, GDP per capita, etc.
The Model:
Y(House Prices) = B0 + B1(Personal Income) + B2(Level of Education) + B3(State
Taxes) + B4(Unemployment Rate) + B5(GDP) + B6(Population) + B7(Burglary) + U
This is a cross sectional model measuring 2010 data on average housing costs
in relation to income and level of education in each of the 50 states. (Data used can be
found in Appendix A)

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Assumptions about disturbance term:
1. Linearity – The expected value of Y is linearly associated given the X’s. Also, the
average value of the error term is equal to zero given X.
2. Unbiased – Error terms are independent of one another.
3. Homoscedasticity - Constant error variance. This means that the variance of the
errors is the same regardless of X. V(ε|xi)=σε2. The degree of random noise is
the same regardless of the value of the X’s.
4. Mean Independence – The error term is independent of the X variables.
5. Normality – The error terms are normally distributed.
Variables:
House Price Index: This is the dependent variable (Y).
Personal Income per capita: This is a testing independent variable. This should have a
positive effect on housing prices because the greater purchasing power consumers
have, the more housing they will buy, thus increasing demand and therefore pricing of
houses.
Level of Education: This is another testing independent variable. It should have a
positive effect on housing prices. Those who obtain a higher level of education tend to
earn higher income, thus have higher purchasing power driving up demand and prices
of housing.
Total State Taxes: Independent variable. This should have a positive impact on
housing prices. As taxes increase, so do property taxes.

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Unemployment Rate: This is another independent variable. If the unemployment rate is
low, the housing rate may be higher because more people are able to earn a living, thus
driving demand for housing.
GDP per capita: Independent variable. This should have a positive effect on housing
prices. Economically healthy states who have higher GDP may have higher costs of
living due to its many consumers earning a living.
Population: This is an independent variable. The population in a state may cause
higher costs for housing because with more people comes a higher demand for
housing. When supply is limited, and housing demand increases, so must the price.
State Total Burglary: Independent variable. This may have a negative impact on home
prices because housing tends to be cheaper if there is a higher rate of crime in the area.
The demand for housing in these types of areas is lower than safer areas.
I will use this model to answer my question by testing the independent variables,
Personal Income and Level of Education against Housing Prices. I will test to see how
correlated the variables are and whether they are significant.
I will then do a test on a binary variable for which I chose to use states west and
states east of the Mississippi river. I chose this criteria for my binary because I wanted
to compare if there was a difference between the west coast and east coast. The
United States tends to be split with higher populations toward each coast, having lots of
land with lower populations throughout its middle section. This is why I chose to
compare the large populations of California and Texas (these are the states with the

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higher population data on the west coast) to the states Florida, New York, Ohio,
Pennsylvania and Illinois (higher population data on the east coast).
Initial estimation and hypothesis testing:
DependentVariable:CPIHOUSING
Method: LeastSquares
Date: 12/11/14 Time:16:48
Sample:1 50
Included observations:50
Variable Coefficient Std. Error t-Statistic Prob.
C -115.4761 88.06088 -1.311322 0.1967
INCOME 0.003878 0.003856 1.005642 0.3202
EDUCATION 10.95013 2.965625 3.692351 0.0006
TAXES 2.28E-06 8.96E-07 2.543248 0.0147
BURGLARY -0.000868 0.000305 -2.840839 0.0068
CAPITAGDP -0.001513 0.001888 -0.801578 0.4272
UNEMPLOYMENT 7.641442 4.836332 1.580008 0.1214
R-squared 0.654494 Mean dependentvar 329.1984
Adjusted R-squared 0.606284 S.D. dependentvar 94.81226
S.E. of regression 59.49163 Akaike info criterion 11.13873
Sum squared resid 152187.9 Schwarz criterion 11.40641
Log likelihood -271.4681 Hannan-Quinn criter. 11.24066
F-statistic 13.57588 Durbin-Watson stat 1.928779
Prob(F-statistic) 0.000000

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When testing the dependent variable (housing prices) against all other variables,
initially I find that burglary has a negative correlation with housing prices. This was as I
expected. All variables seem to have a correlation with my dependent variable;
however, GDP per capita seems to have the least correlation when compared with other
variables. Also, this variable has a negative beta, which I expected the correlation to be
positive because the income of a state increases as taxes increase which would mean
the housing costs more.
The following are correlation graphs. This gives a visual display the relationship
between each variable against the dependent variable.
100
200
300
400
500
600
700
30,000 35,000 40,000 45,000 50,000 55,000 60,000
Personal Income per capita ($)
HousePriceIndex(inhundreds)
Personal income appears to have a generally positive correlation, as expected.

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100
200
300
400
500
600
700
0 20,000,000 60,000,000 100,000,000
Total State Taxes (in thousands)
It appears as though total state taxes remain around one area with varying house
prices. This makes sense since state income may be in the same general area, the
prices of its houses vary much more greatly.
100
200
300
400
500
600
700
16 20 24 28 32 36 40
level of Education
Level of education appears to have a positive correlation with housing prices, as
expected.

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100
200
300
400
500
600
700
0 40,000 80,000 120,000 200,000
State Total Burglary
State burglary appears to vary. As burglary levels remain around a certain number,
house prices seem to vary. However, it appears as burglary rates increase, housing
prices don’t stay at high levels.
100
200
300
400
500
600
700
30,000 40,000 50,000 60,000 70,000
GDP per capita
GDP per capita appears to have a roughly positive correlation, however not very
strongly. I expected a stronger correlation.

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100
200
300
400
500
600
700
2 4 6 8 10 12 14
Unemployment Rate
Unemployment rate does not appear to have a very strong correlation; however
unemployment tends to stay around a certain level while house prices vary.
100
200
300
400
500
600
700
0 10,000,000 20,000,000 30,000,000 40,000,000
POPULATION
The population variable does not appear to have a high correlation with the housing
price variable.

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Conclusion/Test Results:
In conclusion, I have found that housing prices are more correlated with my
testing variable income, than with the testing variable level of education. In my final
estimation I found that my variables are significant, and that burglary is negatively
correlated as I expected.
Due to the high F-stat in my Breusch-Pagan-Godfrey test for heteroscedasticity
(Appendix B), I find that I reject the null concluding that my model has implication of
heteroscedasticity. Therefore, my error terms may be correlated to some degree and
also have varying distributions and variances.
In my test for endogeniety found in (Appendix B) I found that because my t-stat of
-0.586935 was less than my t-crit of 2.02, I fail to reject the null, concluding that I do not
have endogeneity. Therefore, my variables are not correlated with the error term.
In the Ramsey reset test for zero mean I find that my model is specified correctly.
The normality test shows that my model is not a normal distribution. The Wald test
shows that my testing variables are not jointly significant at a value of 0. In the final
weighted estimation I found that most all of my variables tend to be significantly
correlated with the exception of GDP per capita, as it was in my initial estimation.
Therefore, I would conclude that while I would want to include more data
variables, housing prices are related to the level of one’s income. There is also a higher
correlation between housing prices and burglary rates. It has a negative correlation,
meaning as burglary rates are higher, housing prices tend to be lower. If I created a

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new model, or added onto this model, I would do more testing for crime rates and use it
as a testing variable rather than a control variable.
References:
Article 1
Dubin, Robin A., and Allen C. Goodman. "Valuation of education and crime
neighborhood characteristics through hedonic housing prices." Population and
environment 5.3 (1982): 166-181.
Article 2
Houthakker, Hendrik S. "Education and income." The Review of Economics and
Statistics (1959): 24-28.
Article 3
Lee, Tong Hun. "Housing and permanent income: Tests based on a three-year
reinterview survey." The Review of Economics and Statistics (1968): 480-490.
Article 4
Malpezzi, Stephen. "Housing prices, externalities, and regulation in US metropolitan
areas." Journal of Housing Research 7 (1996): 209-242.
Article 5
Winger, Alan R. "Housing and income." Economic Inquiry 6.3 (1968): 226-252.

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Appendix A:
State
House
Price
Index (in
hundreds
)
Personal
Income
per
capita ($)
level of
Educatio
n
Total State
Taxes (in
thousands)
Unemplo
yment
Rate
GDP per
capita
Populatio
n
State
Total
Burglary
Binary:
West=1,
East=0
Alabama 291.11 33,894 21.9 8,419,911 9.3 36156 4,785,570 42,034 0
Alaska 280.23 45,565 27.9 4,522,927 7.9 68656 713,868 3,105 1
Arizona 263.87 33,993 25.9 10,719,958 10.4 38222 6,408,790 50,771 1
Arkansas 245.27 32,017 19.5 7,559,898 7.9 37658 2,922,280 32,511 1
California 411.31 42,282 30.1 107,195,465 12.3 51546 37,333,601 228,857 1
Colorado 342.25 41,689 36.4 8,575,262 9 49923 5,048,196 26,153 1
Connecticut 409.47 55,216 35.5 12,344,106 9.3 64766 3,579,210 15,172 0
Delaware 437.58 40,969 27.8 2,763,032 8 62994 899,711 7,515 0
Florida 296.59 38,478 25.8 30,484,883 11.3 38258 18,846,054 169,119 0
Georgia 284.15 34,341 27.3 14,782,779 10.2 41894 9,713,248 96,723 0
Hawaii 448.63 41,668 29.5 4,837,862 6.8 48694 1,363,731 8,663 1
Idaho 278.66 32,100 24.4 2,951,703 8.7 34825 1,570,718 6,502 1
Illinois 318.26 42,033 30.8 27,795,759 10.4 50296 12,839,695 75,399 0
Indiana 245.34 34,344 22.7 13,795,221 10.1 43207 6,489,965 47,115 0
Iowa 246.02 39,033 24.9 6,809,344 6.3 46052 3,050,314 16,656 1
Kansas 234.66 38,811 29.8 6,492,996 7.1 43556 2,858,910 19,404 1
Kentucky 287.18 32,929 20.5 9,531,404 10.2 37746 4,347,698 30,311 0
Louisiana 241.53 37,199 21.4 8,758,633 7.4 48594 4,545,392 45,435 1
Maine 462.13 37,213 26.8 3,489,953 8.2 38374 1,327,366 7,359 0
Maryland 423.05 50,035 36.1 15,237,748 7.8 54080 5,787,193 36,542 0
Massachusetts 617.39 51,487 39 20,090,563 8.3 60354 6,563,263 37,767 0
Michigan 241.09 35,082 25.2 22,208,870 12.7 39056 9,876,149 73,868 0
Minnesota 312.11 42,572 31.8 17,208,877 7.4 50641 5,310,337 24,415 1
Mississippi 243.14 30,834 19.5 6,268,823 10.5 31331 2,970,047 30,444 0
Missouri 278.25 36,606 25.6 9,707,053 9.3 42610 5,996,063 44,043 1
Montana 353.21 34,612 28.8 2,142,809 6.7 36918 990,527 3,654 1
Nebraska 252.66 39,926 28.6 3,864,897 4.7 49119 1,829,838 8,326 1
Nevada 216.76 36,657 21.7 5,835,963 13.8 44102 2,703,230 22,226 1
N Hampshire 404.59 44,963 32.8 2,271,936 6.1 47224 1,316,614 5,441 0
New Jersey 485.57 50,941 35.4 25,927,891 9.6 56025 8,802,707 38,732 0
New Mexico 299.21 33,175 25 4,295,237 7.9 39316 2,064,982 21,014 1
New York 576.76 49,582 32.5 63,807,610 8.6 60974 19,398,228 64,973 0
N Carolina 314.54 35,435 26.5 21,514,930 10.8 43778 9,559,533 102,690 0
N Dakota 254.68 43,275 27.6 2,645,695 3.8 51254 674,344 1,966 1
Ohio 245.55 36,199 24.6 23,583,596 10 42342 11,545,435 106,521 0
Oklahoma 203.06 35,912 22.9 7,082,161 6.9 39377 3,759,263 37,476 1
Oregon 373.79 35,898 28.8 7,475,135 10.7 49538 3,837,208 19,637 1
Pennsylvania 373.96 41,635 27.1 30,169,122 8.4 45976 12,710,472 55,171 1
Rhode Island 474.92 43,013 30.2 2,568,759 11.7 46277 1,052,669 6,121 0
S Carolina 317.6 32,669 24.5 7,242,724 11.2 35078 4,636,361 46,156 0
S Dakota 290.44 40,613 26.3 1,321,228 5.1 46507 816,211 3,181 1
Tennessee 288.17 35,426 23.1 10,513,788 9.8 39649 6,356,683 64,235 0
Texas 223.8 38,065 25.9 39,516,186 8.2 47617 25,245,178 228,597 1
Utah 320.5 32,447 29.3 5,237,427 8.1 42075 2,774,424 15,017 1
Vermont 436.9 40,134 33.6 2,511,387 6.4 42097 625,793 3,366 0
Virginia 403.22 44,836 34.2 16,411,055 7.1 52084 8,024,417 30,629 0
Washington 414.56 42,547 31.1 16,106,154 9.9 52850 6,742,256 55,164 1
W Virginia 217.41 31,798 17.5 4,803,704 8.4 34818 1,854,146 10,756 0
Wisconsin 303.86 38,728 26.3 14,368,569 8.5 44431 5,689,060 26,566 0
Wyoming 274.93 45,025 24.1 2,158,260 7 66256 564,222 2,149 1

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Appendix B:
Test for Heteroscedasticity:
HeteroscedasticityTest:Breusch-Pagan-Godfrey
F-statistic 6.258169 Prob. F(1,42) 0.0163
Obs*R-squared 5.705965 Prob. Chi-Square(1) 0.0169
Scaled explained SS 3.957595 Prob. Chi-Square(1) 0.0467
Test Equation:
DependentVariable:RESID^2
Date: 12/11/14 Time:14:53
Sample:1 50
C -6821.033 4419.425 -1.543421 0.1302
CAPITAGDP 0.236573 0.094568 2.501633 0.0163
Sum squared resid 1.17E+09 Schwarz criterion 20.10639

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Date: 12/11/14 Time:14:55
Sample:1 50
Weighting series:ABS(VRES)
Weight type: Variance (average scaling)
C -108.2291 52.54895 -2.059586 0.0463
INCOME 0.012422 0.002053 6.050098 0.0000
TAXES 2.13E-06 5.89E-07 3.615020 0.0009
BURGLARY -0.000847 0.000182 -4.642958 0.0000
CAPITAGDP -0.002872 0.001753 -1.638338 0.1096
UNEMPLOYMENT 9.806262 3.717914 2.637571 0.0120
Weighted Statistics
Prob(F-statistic) 0.000000 Weighted mean dep. 313.1386
Unweighted Statistics
S.E. of regression 69.00406 Sum squared resid 180939.3

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Durbin-Watson stat 2.070976
Test for Endogeniety:
Date: 12/11/14 Time:14:08
Sample:1 50
C -122.7353 100.7347 -1.218402 0.2310
INCOME -0.008659 0.022321 -0.387937 0.7003
TAXES 2.01E-06 1.15E-06 1.752352 0.0882
EDUCATION 11.58633 4.461864 2.596747 0.0135

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CAPITAGDP 0.008608 0.017224 0.499797 0.6203
BURGLARY -0.000785 0.000376 -2.088543 0.0439
UNEMPLOYMENT 9.239402 5.959531 1.550357 0.1298
IVRES -0.010193 0.017367 -0.586935 0.5609
Test for Zero Mean:
RamseyRESET Test
Equation:UNTITLED
Specification:CPIHOUSING C INCOME TAXES EDUCATION CAPITAGDP
BURGLARY UNEMPLOYMENT
Instrumentspecification:C INCOME TAXES EDUCATION CAPITAGDP
BURGLARY POPULATION
Omitted Variables:Powers of fitted values from 2 to 4
Value df Probability
F-statistic 2.214646 (3, 34) 0.1043
Likelihood ratio 7.853523 3 0.0491
F-test summary:
Sum of Sq. df
Mean
Squares

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Test SSR 24134.14 3 8044.714
Restricted SSR 147639.3 37 3990.252
Unrestricted SSR 123505.2 34 3632.506
LR testsummary:
Value df
Restricted LogL -241.0367 37
Unrestricted LogL -237.1100 34
Unrestricted TestEquation:
Date: 12/11/14 Time:14:20
Sample:1 50
C 11126.00 7566.153 1.470497 0.1506
INCOME -0.245853 0.165883 -1.482087 0.1475
EDUCATION -585.4722 400.5812 -1.461557 0.1530
TAXES -0.000137 9.33E-05 -1.469711 0.1508
CAPITAGDP 0.081795 0.055551 1.472444 0.1501
BURGLARY 0.051451 0.035064 1.467355 0.1515
UNEMPLOYMENT -441.7197 300.9174 -1.467910 0.1513
FITTED^2 0.275000 0.179630 1.530927 0.1350
FITTED^3 -0.000558 0.000353 -1.579705 0.1234
FITTED^4 4.15E-07 2.54E-07 1.635528 0.1112

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Normality:
0
1
2
3
4
5
6
-40 -30 -20 -10 0 10 20 30 40 50
Series: Standardized Residuals
Sample 1 50
Observations 44
Mean 0.151793
Median -3.511059
Maximum 48.16692
Minimum -43.24634
Std. Dev. 26.00272
Skewness 0.266158
Kurtosis 1.794042
Jarque-Bera 3.185773
Probability 0.203338
Wald Test:
Equation:Untitled
Test Statistic Value df Probability
F-statistic 28.84905 (2, 38) 0.0000
Chi-square 57.69810 2 0.0000
Null Hypothesis:C(2)=C(3)=0
Null Hypothesis Summary:

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Normalized Restriction (= 0) Value Std. Err.
C(2) 0.012422 0.002053
C(3) 2.13E-06 5.89E-07
Restrictions are linear in coefficients.
Final Weighted Estimation:
Date: 12/11/14 Time:14:55
Sample:1 50
Weighting series:ABS(VRES)
Weight type: Variance (average scaling)
C -108.2291 52.54895 **-2.059586 0.0463
INCOME 0.012422 0.002053 ***6.050098 0.0000
TAXES 2.13E-06 5.89E-07 ***3.615020 0.0009
BURGLARY -0.000847 0.000182 ***-4.642958 0.0000
CAPITAGDP -0.002872 0.001753 *-1.638338 0.1096
UNEMPLOYMENT 9.806262 3.717914 ***2.637571 0.0120
Weighted Statistics

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Prob(F-statistic) 0.000000 Weighted mean dep. 313.1386
Unweighted Statistics
S.E. of regression 69.00406 Sum squared resid 180939.3
Durbin-Watson stat 2.070976
*Reject at alpha = 0.10
**Reject at alpha = 0.05
***Reject at alpha = 0.01
Binary Estimation:
Date: 12/11/14 Time:16:30
Sample:1 50
C 59.01909 118.0173 0.500089 0.6198
INCOME 0.008922 0.004040 2.208542 0.0332
INCOME*BINARY -0.001853 0.005813 -0.318785 0.7516
TAXES 1.84E-06 1.47E-06 1.249160 0.2191
TAXES*BINARY 5.24E-07 1.89E-06 0.277112 0.7832
BURGLARY -0.000798 0.000511 -1.562475 0.1263
BURGLARY*BINARY 4.04E-05 0.000684 0.059132 0.9531

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CAPITAGDP 0.000345 0.003074 0.112353 0.9111
CAPITAGDP*BINARY -0.002311 0.004385 -0.527089 0.6011
UNEMPLOYMENT -6.536023 8.989954 -0.727036 0.4715
UNEMPLOYMENT*BINAR
Y 13.60267 9.166913 1.483888 0.1459
Appendix C:
Test for heteroscedasticity:
H0: The model contains homoscedasticity
H1: The model contains heteroscedasticity
Critical value F(1,42): 4.07
F Statistic: 6.258169
Therefore reject null. Conclude the model is heteroscedastic
Test for endogeniety:
H0: The model is not endogenous
H1: The model is endogenous
DF: 36
Tcritical: 0.10, 0.05, 0.01 = 1.302, 1.683, 2.422 respectively.

25
Tstat: |-0.586935|
Therefore, conclude that the model is not endogenous.
Test for zero mean:
H0: Γ = 0 (model is specified correctly)
H1: Γ ≠ 0 (model is not specified correctly)
Fstatistic = 2.214646 < Tcritical = 2.89
Therefore, fail to reject H0.
Normality:
H0: skewness & kurtosis = 0
H1: otherwise
Tcritical: 1.696 < Jarque-Bera: 3.1858
Therefore, reject H0, conclude my model is not normal.
Wald test:
H0: B1=B2=0
H1: Otherwise
Fcritical: 3.24 < Fstatistic: 28.84905
Therefore reject Ho. My testing variables are not jointly significant at a value of 0.

Thurman Model 1 Cross Sectional ECN 405

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