Econometrics Project Completed

America’s Big Babies: Aneconometric analysis of thepercentage of male adults

between the ages of 18 and 34living at home with their

parents in USA

Project prepared forDr John Stinespring

Md Samiul H. Dhrubo12/11/2013

I. Introduction

The share of young adults between the age group of 24 and

34 living with parents have edged up last year despite

improvements in the economy. A new study from Pew research has

estimated that a total of 21 million young adults are living

with parents, a clear sign that effects of recession are still

lingering. “Although the media at times present a picture of

an increasing proportion of young adults living in their

parent’s home, Messineo and Wojkiewicz (2004) finds that the

increase in propensity from 1960 to 1990 for young adults age

19 to 30 to live with parents was largely due to an increasing

proportion of young adults over this time period who were

never married, or formerly married – groups that are much more

likely to reside with their parents” Kreider, M said in a

speech at the ASA annual meetings in New York, August 12,

2007. The predicted percentage of young male adults living at

home is of particular importance in determining the loss of

potential productivity faced by The United States every year.

While there is a substantial literature which examines the

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home-leaving (and returning) behavior of young adults, little

work has been done to show the socio-economic reasons behind

the rising trend over the last decade.

This paper provides new empirical evidence on the

relationship between percentage of males within the age group

of 18 and 34 living at home and limited labor market outcomes,

average marriage age for young people, and rent of house to

price of house ratio in the United States. I use this evidence

to argue that percentage of males between the age group of 18

and 34 are affected by these key socio-economic variables. To

understand the relationship it is necessary to understand the

uncertainties and opportunities that exist for young adults in

the labor market. I based my research paper primarily on one

paper, written by Liu, Yang, Di Zhu “Young American Adults

living in Parental Homes,” (2011). The data I used was for the

United States as a country dating back to 1983 through 2012

and primarily sourced from The Bureau of Labor Statistics

(BLS), The Current Population Survey (CPA) and American

Housing Survey (AHS).

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II. Literature review

The basis of my research and calculations are from a

paper entitled “Young American Adults living in parental

homes” written by Zhu Xiao Di, Yi Yang and Xiaodong Liu. Their

paper, written in 2002 reviewed the literature of young adults

(ages 25-34) living in parental homes in regard to gender

difference, racial difference, family structure variation,

parental resource gap, personal income gap, and the long-term

trend. They test to see the effect of personal income,

parental resource, and race on the living arrangements of

young adults. They based their research on data collected from

The Current Population Survey (CPS). One of the limitations

they faced while using CPS data for their analysis is that the

data did not have information on rent. To amend, they

generated a median monthly contract rent variable based on the

American Housing Survey (AHS) of 1999 which was adjusted for

four regions and metropolitan status, namely inner cities,

suburbs, and non-metro areas. For each dataset, they estimated

the effect of various factors on the probability of young

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adults living at parental homes, controlling for selected

demographic young adults living at parental homes, controlling

for selected demographic characteristics. Their dependent

variable is whether the young adult lives in parental home

(1=yes, 0=no). Independent variables include young adults’

personal income, average rent in an area (For CPS 2001 data),

parental resources (for PSID data), age, gender, race,

educational attainment, marital status, and regional and urban

variation (for PSID data). Their analysis confirms as pointed

out in their research by Liu, Yang, Di Zhu (2002) “their

belief that personal income is one of the most important

factors explaining the living arrangements of young adults

(ages 25-34)” (p. 40). Controlling for parental resources and

selected demographic factors, those with lower personal income

are more likely to live in parent’s home. Even though their U-

shaped pattern representing the long term trends of co-

residence was in line with the overall economic conditions in

income distribution such as family income inequality, low-wage

share of total employment, inequality in wages and salaries,

and the number of persons below the poverty level, their

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conclusions do not give us a numerical prediction of the

percentage of big babies living at home.

The Liu, Yang, Di Zhu paper wasn’t the only paper used in

my research, but the theory served as the back bone of my

model. The strong relationship between living arrangements and

personal income encouraged me to observe what other socio-

economic factors can affect this relationship and change the

percentage of young adults living at home. Another piece used

as a reference was published by the Fertility and Family

Statistics Branch, U.S. Census Bureau and presented by Rose M.

Krieder in August of 2012 and was titled “Young Adults Living

in Their Parent’s Home”. Her literature “Young Adults Living

in Their Parent’s Home” (2007) examined how “… [T]he

characteristics of young adults living in their parents’ home

might differ from young adults living elsewhere” (p.1).

Krieder’s findings indicate that the profile of young adults

living in their parents’ home suggests that young adults often

live in their parents’ home for their own benefit. Another

paper I found interesting was, “Intergenerational Transfers

and Household Structure Why Do Most Italian Youths Live With 5 | P a g e

Their Parents?” by Marco Manacorda and Enrico Moretti (2002).

I did not focus entirely on this due to the geographical

relevance and it used independent variables which are

primarily social factors that are typical characteristics of

Italian Youths only. Their basic analysis was that Italy is an

outlier in terms of the living arrangements of its young man.

III. Methodology and Data

To test the hypothesis that socio-economic factors have a

greater impact on percentage of young adults (18-34), I

created a total of three linear-logged model based on the

model used in the Liu, Yang, Di Zhu paper, but I added

different independent variables and expressed my dependent

variable as a percentage of young adults (18-34) living at

home. My models attempt to estimate the impact of socio-

economic factors including rent to price ratio of housing,

labor market participation ratio of people over the age of 65,

average marriage age of males and real weekly wage of adults

(18-34). Percentage of young adults living at home denoted by

PHt, rent to price ratio of housing as RPt, average marriage

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age of males as AVGMt, labor market participation ratio of

people over the age of 65 as LPt, and real weekly wage as RWt

all of which I expect to have a significant impact on

percentage of young adults living at home.

The idea to start with a linear-logged model came from

Liu, Yang, Di Zhu paper and I also thought the variables

should have a linear-logged relationship with my dependent

variables and the errors to be normally distributed. I have

run a Jarque-Bera Normality test to show that my errors are

normally distributed. I decided to log some of the independent

variables in the model because of the fact that the regressand

and some of the regressors are in different units. Logging

some of the regressors will help me minimize the spread of the

data and attempt to get the data on a comparable scale.

The linear-logged model is written as follows:

Model I: PHt = β0 + β1 Log (RPt) + β2 Log (AVGMt) + β3 LPt + β4 Log (RWt) +Ut

From this point I developed my second model which is

essentially the same as my original model but controlling for the

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independent variable, labor force participation ratio of people over the age

of 65(LPt).

Firstly, the introduction of this control variable will

enable me to predict the long-run trend of the percentage of

young adults (18-34) living at home without taking into

consideration a phenomenon which has been only recently observed

in the labor market and might not hold in the long-run with the

economy emerging out of the Great Recession.

Secondly, another reason behind dropping the independent

variable, labor force participation ratio of people over the age of 65(LPt) in

the second model is entirely based on suspecting

multicollinearity between LPt and one or more independent

variables such as average marriage age of males as people are

less likely to get married if they do not have a stable job. I

included tests results in the appendix section to show

evidence of multicollinearity.

The second model is as follows:

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Model II: PHt = β0 + β2 Log (RPt) + β3 Log (AVGMt) + β4 Log (RWt) + Ut

Building up on this model a dummy variable was added to

make a third model and to account for any impact that a

recession may have on real wages and coincidentally affect the

dependent variable, percentage of young adults (18-34) living

at home. We added this variable on the account that recessions

would have a qualitative impact on percentage of young adults

(18-34) living at home, one that couldn’t be measured by

adding numerical data. The third model is still controlling

for the independent variable, labor force participation ratio of people

over the age of 65(LPt):

Model III: PHt = β0 + β1 Log (RPt) + β2 Log (AVGMt) + β3 Log (RWt) + β4

(Recession*Log (RWt)) +Ut

All models underwent a series to test to verify their

legitimacy and to ensure no models contained underlying

problems, resulting in biased predictions. The first test was

for normality which was done by looking at the probability of

the Jarque-Bera Normality Test. It is important that the error

terms u are normally distributed. In the classical normal

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linear regression model (CNLRM) it is assumed that the error

terms follow the normal distribution (with zero mean and

constant variance). Using the central limit theorem (CLT) to

justify the normality of the error term, I was able to show

the OLS estimators themselves are normally distributed. This

in turn allowed us to use the t and F statistics in hypothesis

testing in small, or finite, samples like my samples.

Therefore the role of the normality assumption is very

critical. Due to the small size of the samples I ran a Jarque-

Bera Normality Test and it showed that the errors were

normally distributed. All the Jarque-Bera Test error terms

output gave me probabilities of more than 20% as shown in the

table below:

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Figure 1

The errors are normally distributed because the

likelihood of getting a Jarque-Bera score of 1.5654

(approximately) and the errors being normally distributed is

45.72% (approximately).

Secondly, I tested the slopes of the regression line to

see if there is a significant relationship between the

independent and dependent variable. Just because the slope

coefficients are not equal to zero, it doesn’t mean that there

is a statistically significant relationship. To evaluate I

conducted t-tests for each slope coefficients of the

independent variables. All my slope coefficients had t-

statistics greater than the t-critical value at 5%

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significance level. So I rejected the null hypothesis and I

could statistically conclude that there is a relationship

between the independent and dependent variables. My F-

statistic computed was also greater than F-critical which

determined that there is a significant relationship between

the dependent variable and any of the independent variables in

our model. The adjusted R2 of all three of my models were

high, which was an excellent indicator that our regression

line was much better than simply using the average value of

the dependent variable for prediction purposes.

Next was to test for multicollinearity this was conducted

in many steps. My first model showed low t-stats for some of

the independent variables with high probability and R2 and F-

statistics were high which were good signs indicating that the

model suffers from multicollinearity. After noticing that two

of my independent variables Log RPt and LPt were showing low t-

stats with high probability I conducted a simple pair-wise

correlation test and it confirmed that the independent

variables Log AVGMt is highly collinear with Log RWt and LPt as

shown in the table underneath.12 | P a g e

LOG(RWt) LOG(RPt) LOG(AVGMt) LPt

LOG(RWt) 1.000000-0.531632 0.835753 0.746151LOG(RPt) -0.531632 1.000000 -0.318637 -0.411232LOG(AVGMt) 0.835753-0.318637 1.000000 0.906715

LPt 0.746151-0.411232 0.906715 1.000000Table 1

I further confirmed my doubts by using confidence

ellipses to decipher which variables had a possibility of

being collinear, which was indicated by an elliptical shape as

opposed to a circular one, where a circular shape would have

indicated no multicollinearity. It confirmed my simple-pair

wise correlation test.

I regressed the independent variables on the other

independent variables. Upon regressing Log AVGM on Log RW I

got an auxiliary regression R2 = 0.698483 which is less than

the adjusted R2 of the original model indicating that there is

no problematic collinearity between these two independent

variables. However, upon regression Log AVGMt on LPt I got an

auxiliary regression R2 of 0.822132. Using Klein’s Rule of

Thumb I can conclude that there is high collinearity between

the two independent variables.

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I had two options for correcting the multicollinearity.

According to O.J. Blanchard, Comment, Journal of Business and

Economic Statistics, multicollinearity is essentially a data

deficiency problem (micronumerosity). Faced with

micronumerosity I decided to drop the independent variable LPt

in my second model although carefully checking for

specification bias. Even though economic theory suggest that

the labor force participation of people over the age of 65 is

important, out limitation in having a priori information on

how much it will affect the dependent variable I dropped the

variable. It corrected for multicollinearity in the first

model and the ensuing models.

Since all my data was collected for the same population

over a period of time the variables were of similar orders of

magnitude, as a result of which I did not face any trouble

with Heteroscedasticity.

The next test was for autocorrelation. I started

detecting for autocorrelation by plotting the residuals

against time, the time sequence plot as show overleaf:

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Figure 2

Examining the time sequence plot as above, I observed

that our estimated error terms exhibit a pattern (negative

runs to the positive runs) suggesting that perhaps our error

terms are not random.

Then we conducted a Durbin-Watson test to check if the d-

statistic shows results of autocorrelation. The calculated d-

statistic for all our models were close 2.00 indicating there

is no autocorrelation. To avoid some of the limitations of the

Durbin-Watson d test for autocorrelation, I also used a

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Breusch-Godfrey (BG) Test to further verify our observation.

Using Breusch-Godfrey test we fail to reject the null

hypothesis of no auto correlation. I also checked each model

more model misspecification using a Ramsey RESET test.

As mentioned earlier our data includes percentage of

young adults (18-34) PH, Rent to Price of houses ratio (RP),

average marriage age of males (AVGM), labor market

participation ratio of people over the age of 65 (LP), real

weakly wage (RW), and a dummy variable indicating recessions.

All the data was collected primarily from The Bureau of Labor

Statistics (BLS), The Current Population Survey (CPA) and

American Housing Survey (AHS) expressed annually between the

years or 1983 to 2012. I would also like to acknowledge

Associate Professor John Stinespring, on his contribution with

reliable dataset for years 1983 to 2011. The descriptive

statistics of all our variables is listed below.

PH RP AVGM LP RW

Mean14.76

14.699

26.84

3

13.30

0572.952

Median 14.54 4.938 26.85 12.23 588.375

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8 0 8

Maximum18.69

55.327

29.10

0

17.80

0689.000

Minimum12.85

93.098

25.40

0

10.77

5419.250

St. Dev. 1.400 0.628 0.923 2.279 88.305

Skewness 1.267-

1.3760.480 0.853 -0.222

Kurtosis 4.730 3.800 2.983 2.274 1.512

Jarque-

Bera

11.76

5

10.26

21.152 4.296 3.014

Probabili

ty0.003 0.006 0.562 0.117 0.222

Sum442.8

34

140.9

79

805.3

00

399.0

10

17188.5

50

Sum Sq.

Dev.

56.85

3

11.08

2

24.71

4

150.6

44

226135.

000

Observati

ons30 30 30 30 30

Table 2

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IV. Results

The results of my models showed different beta values for

each variable depending on the model.

The first model had betas were all statistically significant and

had a good Durbin-Watson score of almost 2.00.

PHt = -179.3907 + 1.658688 Log RPt + 77.72824 Log AVGMt – 0.206411 LPt -9.676319 Log RWt

(-8.342) (2.079) (9.279) (-2.070) (-7.960)

Adjusted R2 = 0.88 DW = 2.03

After the variables in the linear model were tested for

their significance the model indicates that the average marriage

age of males and the real wage of adults are the most significant

variables with probability of being equal to zero is 0.0000. Even

though the model wasn’t represented in any of the papers I used

as reference, it confirms Rose M. Krieder’s claim that average

marriage age of males have a significant impact on their decision

to live at home with their parents. My findings also confirms

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Liu, Yang, Di Zhu’s finding that personal income is one of the

most important factors explaining the living arrangements of

young adults.

The intercept fail to infer any economically significant

prediction because the percentage of young adults living at home

cannot be negative. However analyzing the other slope

coefficients, we can start interpreting them. When the rent to

house price ratio goes up by 1 percent, on average, the

percentage of young adults living at home goes up by 1.659% which

meets my apriori expectation that as rent of houses increase the

percentage of young adults living at home should increase as

well. The average marriage age of males is a highly economical

and statistically significant variable as its slope co-efficient

shows. It indicates that as the average marriage age of males

increase by 1%, on average, the percentage of young adults living

at home will increase by 77.3% hence the most important

determinant of a young adult’s decision to continue to live at

home with their parents. As I mentioned it verifies Rose M.

Krieder’s claim that average marriage age of males have a

significant impact on their decision to live at home with their

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parents. As labor force participation rate of people over the age

of 65 goes up by 1%, on average, the percentage of young adults

living at home decreases by .206%. It is interesting to see that

this independent variable shows such results as it contradicts my

apriori expectation of how this variable would affect the

percentage of young adults living at home. Real wages also show

an interesting relationship indicating that as real wages go up

by 1%, on average, percentage of young adults living at home will

decrease by 9.676%.

Model II: PHt = -140.8271 + 2.295887 Log (RPt) + 63.65141 Log (AVGMt) -9.042724 β3 Log

(RWt) + Ut

(-12.356) (2.940) (12.263) (-7.242)

Adjusted R2 = 0.870 DW = 1.591

After testing for the significance of the slope coefficients we

get very similar results as model I predictions. It also confirms

Rose M. Krieder’s claim that average marriage age of males have a

significant impact on their decision to live at home with their

parents. My findings also confirms Liu, Yang, Di Zhu’s finding

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that personal income is one of the most important factors

explaining the living arrangements of young adults.

The intercept once again fail to infer any economically

significant prediction because the percentage of young adults

living at home cannot be negative. However analyzing the other

slope coefficients, we can start interpreting them. When the rent

to house price ratio goes up by 1 percent, on average, the








at home will increase by 63.651% hence once again verifying the

most important determinant of a young adult’s decision to

continue to live at home with their parents. As real wages goes

up by 1% the percentage of young adults living at home decreases

by 9.043%. 21 | P a g e

Model III: PHt = -141.1819 + 2.371887 Log (RPt) + 63.35958 Log (AVGMt) – 8.848407 Log

(RWt) -0.243939 +Ut

(-12.323) (3.000) (12.136) (-6.975)(-0.876) Adjusted R2 = 0.869 DW = 1.754

After introducing the dummy variable in our third model the

slope coefficient fails to pass the significance test suggesting

that the percentage of young males living at home are not

affected by the decrease in real wage due to recession.

After testing for the significance of the remaining slope

coefficients we get very similar results as model II predictions.

It also confirms Rose M. Krieder’s claim that average marriage

age of males have a significant impact on their decision to live

at home with their parents. My findings also confirms Liu, Yang,

Di Zhu’s finding that personal income is one of the most

important factors explaining the living arrangements of young

adults.

The intercept once again fail to infer any economically

significant prediction because the percentage of young adults

living at home cannot be negative. However analyzing the other

22 | P a g e

slope coefficients, we can start interpreting them. When the rent

to house price ratio goes up by 1 percent, on average, the








at home will increase by 63.384% hence once again verifying the

most important determinant of a young adult’s decision to

continue to live at home with their parents. As real wages goes

up by 1% the percentage of young adults living at home decreases

by 8.864%.

V. Conclusion

After comparing two ensuing models with my original model

(Model I) we find very similar results as predicted by Rose M.

Krieder and Liu, Yang, Di Zhu papers. My slope coefficients have

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only slightly changed across the model however none of the models

are hugely different from each other when it came to explaining

the variability of the predicted dependent variable. However it

is important to note that our independent variable LPt from our

first model showed a negative relationship with our dependent

variable suggesting that as labor force participation of people

over 65 is negatively related with percentage of people living at

home. As mentioned before this is a very new phenomenon observed

in the US economy especially since “The Great Recession”. It will

be interesting to see if this relationship changes as more and

more young adults will start looking for jobs in the next few

years with 3rd quarter economic report suggesting that two more

million jobs will be created in the next coming years. (As I

caught up with next research I found out that the jobs added on

the month of august 2014, the job number is only 142,000 compared

to jobs added in the economy in the preceding month to be well

over 250,000. This is an alarming figure and needs to be further

worked on).

Looking at the statistical significance of the model the

adjusted R2 suggest that they are all great prediction models for24 | P a g e

the percentage of young adults (18-34) living at home with their

parents. Logically it would be advisable to use the simpler model

with the fewest independent variables based on the idea of

Parsimony, however I would suggest using Model I as it captures

more independent variable giving a higher Adjusted R2. Also the

slope coefficient of LPt is statistically significant. I would also ignore

the effect of recession in predicting the percentage of young

adults living at home as the slope coefficient is not

statistically different from zero at the 5% significance level.

If I had more time to allocate to this study we would be

able to identify more socio-economic indicators affecting the

young adults decision to stay at home and sacrificing

independence of living alone. At this point more research needs

to be conducted before a conclusion can be made as to whether

what other socio-economic factors have an influence on the

percentage of young adults (18-34) living at home.

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Appendix

Section I:

Slope significance test for Model I:

Dependent Variable: PHSample: 1983 2012Included observations: 30

VariableCoefficien

t Std. Errort-Statistic Prob.

C -179.3907 21.50475 -8.341911 0.0000LOG(RP) 1.658688 0.797686 2.079375 0.0480LOG(AVGM) 77.72824 8.376944 9.278829 0.0000

LP -0.206411 0.099724 -2.069826 0.0490LOG(RW) -9.676319 1.215673 -7.959638 0.0000

R-squared 0.900528 Mean dependent var 14.76113Adjusted R-squared 0.884613 S.D. dependent var 1.400155

S.E. of regression 0.475614 Akaike info criterion 1.502592

Sum squared resid 5.655217 Schwarz criterion 1.736124

Log likelihood -17.53887 Hannan-Quinn criter. 1.577301

F-statistic 56.58197 Durbin-Watson stat 2.028597Prob(F-statistic) 0.000000

H0 : β1 = β2 = β3 = β4 = 0

H0 : β1 = β2 = β3 = β4 ≠ 0

α = 5%

Degree of freedom (d.f.) = n-k = 30-4 = 26

tcrit5% = 2.056

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All our |tstat| >|tcrit|, so we reject the null hypothesis and we

can be at least 95% confidence that our estimated betas are

statistically significant from zero.

Slope significance test for Model II:


VariableCoefficien


C -140.8271 11.39716 -12.35633 0.0000LOG(RP) 2.295887 0.780982 2.939743 0.0068LOG(AVGM) 63.65141 5.190515 12.26302 0.0000LOG(RW) -9.042724 1.248598 -7.242301 0.0000






H0 : β1 = β2 = β3 = 0

H0 : β1 = β2 = β3 ≠ 0

α = 5%

d.f. = n-k = 30-3 = 27

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tcrit5% = 2.052

All our |tstat| >|tcrit|, so we reject the null hypothesis and we

can be at least 95% confidence that our estimated betas are

statistically significant from zero.

Slope significance test for Model III:


VariableCoefficien


C -141.1575 11.45478 -12.32303 0.0000LOG(RP) 2.366142 0.788595 3.000455 0.0060LOG(AVGM) 63.38360 5.222882 12.13575 0.0000LOG(RW) -8.863524 1.270809 -6.974712 0.0000RECESSION -0.243939 0.278525 -0.875826 0.3895






H0 : β1 = β2 = β3 = β4 = 0

H0 : β1 = β2 = β3 = β4 ≠ 0

α = 5%

d.f. = n-k = 30-4 = 26

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tcrit5% = 2.056

Since all our |tstat| are not greater than |tcrit| so we fail to

reject the null hypothesis and we can be at least 95% confidence

that our estimated betas are statistically significant from zero.

Section II:

Pair wise test for model, confidence ellipse and auxiliary

regression results:

LOG(RP) LOG(AVGM) LP LOG(RW)LOG(RP) 1.000000 -0.318637 -0.411232 -0.531632LOG(AVGM) -0.318637 1.000000 0.906715 0.835753

LP -0.411232 0.906715 1.000000 0.746151LOG(RW) -0.531632 0.835753 0.746151 1.000000

29 | P a g e

Auxiliary regression of Log (AVGMt) on LPt

Dependent Variable: LOG(AVGM)Sample: 1983 2012Included observations: 30

VariableCoefficien


C 3.108717 0.016111 192.9586 0.0000LP 0.013589 0.001194 11.37630 0.0000

30 | P a g e

R-squared 0.822132Adjusted R-squared 0.815780

Auxiliary regression of Log (AVGMt) on LPt

Dependent Variable: LOG(AVGM)Sample: 1983 2012Included observations: 30

VariableCoefficien


C 2.150726 0.141433 15.20670 0.0000LOG(RW) 0.179642 0.022305 8.053795 0.0000

R-squared 0.698483Adjusted R-squared 0.687714

Section III:

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Model I:

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.441716 Prob. F(3,22) 0.7255

Obs*R-squared 1.704359 Prob. Chi-Square(3) 0.6360

Test Equation:Dependent Variable: RESIDSample: 1983 2012Included observations: 30Presample missing value lagged residuals set to zero.

VariableCoefficien


C -4.502255 22.99093 -0.195827 0.8465LOG(RP) -0.265268 0.860940 -0.308115 0.7609LOG(AVGM) 2.671937 9.164657 0.291548 0.7734

LP -0.021390 0.105820 -0.202139 0.8417LOG(RW) -0.568701 1.374306 -0.413810 0.6830RESID(-1) -0.086486 0.221765 -0.389987 0.7003RESID(-2) -0.197194 0.224157 -0.879716 0.3885RESID(-3) -0.190655 0.230274 -0.827946 0.4166

R-squared 0.056812 Mean dependent var -1.66E-14Adjusted R-squared -0.243293 S.D. dependent var 0.441597





32 | P a g e

Model II:





VariableCoefficien


C 1.719738 11.93287 0.144118 0.8867LOG(RP) -0.007951 0.814401 -0.009763 0.9923LOG(AVGM) -0.680223 5.388396 -0.126238 0.9006LOG(RW) 0.083450 1.294370 0.064472 0.9492RESID(-1) 0.178384 0.208116 0.857135 0.4002RESID(-2) -0.068794 0.217944 -0.315650 0.7551RESID(-3) -0.162499 0.217271 -0.747910 0.4621






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Model III:





VariableCoefficien


C 0.382405 12.21984 0.031294 0.9753LOG(RP) 0.008127 0.840510 0.009669 0.9924LOG(AVGM) -0.130788 5.535181 -0.023629 0.9814LOG(RW) 0.005049 1.356372 0.003722 0.9971RECESSION 0.029965 0.320852 0.093393 0.9264RESID(-1) 0.098099 0.216814 0.452459 0.6554RESID(-2) 0.004921 0.236883 0.020775 0.9836RESID(-3) -0.117395 0.224004 -0.524075 0.6055





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Section IV:

Model I:

Ramsey RESET TestEquation: UNTITLEDSpecification: PH C LOG(RP) LOG(AVGM) LP LOG(RW)Omitted Variables: Powers of fitted values from 2 to 4

Value dfProbabilit

yF-statistic 1.217814 (3, 22) 0.3267Likelihood ratio 4.609060 3 0.2028

F-test summary:Sum ofSq. df

MeanSquares

Test SSR 0.805389 3 0.268463Restricted SSR 5.655217 25 0.226209Unrestricted SSR 4.849828 22 0.220447Unrestricted SSR 4.849828 22 0.220447

LR test summary:Value df

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Restricted LogL -17.53887 25Unrestricted LogL -15.23434 22

Unrestricted Test Equation:Dependent Variable: PHSample: 1983 2012Included observations: 30

VariableCoefficie

nt Std. Errort-

Statistic Prob.

C -36549.10 83177.65 -0.439410 0.6647LOG(RP) 330.2806 752.6735 0.438810 0.6651

LOG(AVGM) 15497.79 35286.09 0.439204 0.6648LP -41.21711 93.76256 -0.439590 0.6645

LOG(RW) -1929.148 4392.295 -0.439212 0.6648FITTED^2 -18.72183 44.26227 -0.422975 0.6764FITTED^3 0.778442 1.909809 0.407602 0.6875FITTED^4 -0.012026 0.030760 -0.390962 0.6996

R-squared 0.914695 Mean dependent var 14.76113

Adjusted R-squared 0.887552 S.D. dependent var 1.400155




F-statistic 33.69957 Durbin-Watson stat 1.997110

Prob(F-statistic) 0.000000

Model II:

Ramsey RESET TestEquation: UNTITLEDSpecification: PH C LOG(RP) LOG(AVGM) LOG(RW)Omitted Variables: Powers of fitted values from 2 to 4

Value dfProbabilit


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MeanSquares





VariableCoefficie

nt Std. Errort-

Statistic Prob.

C 77573.20 56626.82 1.369902 0.1839LOG(RP) -1231.602 898.7099 -1.370411 0.1838

LOG(AVGM) -34156.05 24912.76 -1.371026 0.1836LOG(RW) 4852.880 3539.113 1.371214 0.1835FITTED^2 53.73434 37.97062 1.415156 0.1704FITTED^3 -2.374822 1.630973 -1.456077 0.1589FITTED^4 0.039146 0.026168 1.495956 0.1483








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Model III:

Ramsey RESET TestEquation: UNTITLEDSpecification: PH C LOG(RP) LOG(AVGM) LOG(RW) RECESSIONOmitted Variables: Powers of fitted values from 2 to 4

Value dfProbabilit



MeanSquares





VariableCoefficie

nt Std. Errort-

Statistic Prob.

C 56587.14 54350.69 1.041148 0.3091LOG(RP) -923.9276 887.0017 -1.041630 0.3089

LOG(AVGM) -24759.77 23758.50 -1.042144 0.3087LOG(RW) 3462.787 3322.301 1.042286 0.3086

RECESSION 95.36145 91.38539 1.043509 0.3080FITTED^2 39.46957 36.45807 1.082602 0.2907FITTED^3 -1.757769 1.569674 -1.119831 0.2749FITTED^4 0.029177 0.025237 1.156148 0.2600






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Bibliography

A) Liu, Yang, Di Zhu, “Young American Adults living in

Parental Homes,” (2011), Harvard University

B) Kreider, Rose, “Young Adults Living in Their Parent’s

Home”, U.S. Census Bureau, Presented at the ASA annual

meetings in NY, August 12, 2007

C) Gujarati, Damodar N., and Dawn C. Porter. Basic

Econometrics. Boston: McGraw-Hill Irwin, 2009. Print

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Econometrics Project Completed

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