Sunday, November 16, 2014

Econometrics paper (Difficult Subject)

.


 

4.1 Trends in Entrepreneurship


 


 

"How has entrepreneurship fared over the same period of time in which unemployment

rates have increased rapidly and the housing market has dropped significantly? Figure 3

displays annual estimates of the monthly entrepreneurship rate from 1996 to 2009. As

noted above the entrepreneurship rate measures the percentage of the adult, nonbusiness

owner population that starts a business each month. It captures all new business owners,

including those who own incorporated or unincorporated business, and those who are

employers or non employers. In 2009, an average of 0.34% of the adult population, or

340 out of 100,000 adults created a new business each month. The business formation

rate increased from 2008 when it was 0.32%. It was the third straight year that the

index increased, resulting in an increase from 0.29% in 2006 to 0.34% in 2009.


 

The recent increase is the largest over the 14-year sample period. In fact, over the period from 1996
to 2009, the business creation rate fluctuated within the range of 0.27–0.31%. It was not
until 2008 and 2009 that it rose above the high end of this range, which coincides with the
recent recession. In the late 1990s, the entrepreneurship rate decreased slightly, then rose
from 2001 to 2003. It remained relative constant over the next 3 years before increasing
in the recent recession."[7]


 



 


 

"Another interesting finding is that home owners are more likely to start businesses.

The coefficient is positive and statistically significant, although relatively small. Home

owners have a 0.012 percentage point higher rate of entrepreneurship than non home

owners, which is roughly a 4% higher rate relative to the mean. In the presence of

liquidity constraints, the ability of owners to borrow against the value of their homes,

such as home equity loans, may make it easier to finance new business ventures."[7]


 

"Figure 4 displays the entrepreneurship rate against the national median home price

in $2009. The negative relationship between the two trends in the recent recession is very

clear. Home prices have dropped sharply over the past few years as entrepreneurship

rates have increased. These patterns run counter to the decline in home equity decreasing

entrepreneurship and are likely due to stronger positive effects of rising unemployment

rates. Entrepreneurship rates also dropped in the late 1990s when home prices were

rising. Interestingly, however, both entrepreneurship and home prices rose steadily in

the early 2000s. In this period, rising home equity may have provided capital for would be-

entrepreneurs to start businesses."[7]


 

"At the national level, trends in entrepreneurship appear to be primarily counter

cyclical—rising in economic downturns and declining in strong economic growth periods.

The national patterns for entrepreneurship, however, are weaker than unemployment

patterns over the business cycle. Trends in home prices and their effects on access

to capital may have offset some of the business cycle effects. But, these are only broad

strokes based on national trends. Instead, it is important to focus on variation in local

labor market and housing conditions. Unemployment rates and housing prices differ

substantially across metropolitan areas, and these differences can be used to more carefully

examine the relationship between entrepreneurship, and unemployment and home

prices."[7]


 



 

"Summary measures of goodness of fit are useful for comparing the performance of different models fit to the same data. Measures include information criteria or R2-type measures. Calculation of these summary measures usually depends on the sample size (or range of data used) and model structure used (such as linear or logistic regression), and so comparisons should only be made between models of the same structure built using the same data. It should be noted that for non-linear models there are multiple different ways to calculate R2-type measures, with no consensus as to which should be used. For non-linear models, goodness-of-fit measures based on the residuals (such as the deviance) or information criteria are preferred. There are sometimes also measures specific to the model type, such as the Hosmer– Lemeshow test for logistic regression; for further details see Campbell. If Markov Chain Monte Carlo methods have been used, additional properties such as the convergence of the chains, autocorrelation plots, and deviance information criteria should be reported [16]."


 


 


 

"The central puzzle in economic development is to explain what accounts for differences in output per capita (inequality) across nations. This is what Lucas (1988) posited as the problem of economic development. Based on the neo-classical production function, differences in output per capita can be attrib-uted to differences in physical capital, human capital and total factor productivity (TFP). Chari et al. (1997) argue that observed differences in output per capita can be explained by differences in factors of production (e.g., physical and human capital). However, Hall and Jones (hereinafter HJ 1999), and Parente and Prescott (1999, 2000) show that the difference in TFP is the key determinant of differences in international incomes. Thus, to be able to answer Lucas' question, we must first answer the question: What explains international differences in TFP?[17]


 

"Recently, considerable attention has been given to the role of institutions in explaining not only differences in productivity across countries, but also why some countries invest more in physical and human capital (North 1990, Knack and Keefer 1995; Nugent and Robinson 1998; HJ 1999; Parente and Prescott 2000; Acemoglu et al. 2001, Easterly and Levine 2002, among others). By institutions, North (1990) means the formal (laws, constitutions) and informal (customs, traditions) constraints, and government policies (enforcement, punishment) that shape the interactions of economic actors1. For instance, countries with more secure property rights have, in general, higher productivity and therefore higher levels of income per capita. According to North (1990, p.107), institutions ''are the underlying determinant of the long-run performance of economies.''[17]


 


 


 



 


 


 

The output per worker gap among nations, would tend to widen. Quantile regression provides the appropriate tools to determine whether there are different marginal responses of output per worker to changes in institutions. In Fig. 2, we depict the IV estimates (horizontal dashed lines) along with the corresponding quantile regression estimates.


 

The shaded areas represent 90% confidence intervals; at all estimated quantiles, institutions are statistically and foremost economically significant. As expected, at higher quantiles ðsÞ the return for each additional ''unit'' of institutions decreases relative to lower conditional quantiles of output per worker. Returns vary from approximately 6.2 to 3.8 as s increases. This first difference relative to HJ, as a result of applying the more robust and descriptive methodology of quantile regression, reinforces the importance attributed to institutions in promoting not only development, but also in closing differences in output per worker across nations."




 


 

"The state housing collateral ratio is computed using the Lustig and van Nieuwerburgh (2005) method. The unemployment rates are from the BLS. The relative unemployment rate is the ratio of the current unemployment rate to the moving average of the unemployment rates from the previous 16 quarters. Labor income is from the BEA. U.S. cay and U.S. They are downloaded from Sydney Ludvigson's and Stijn van Nieuwerburgh's web sites, respectively.


 

The three spreads use quarterly data obtained from the Board of Governors of the Federal Reserve System web site. We use 13(f) institutional holdings data from Thomson Reuters to compute the institutional trading variables, while the retail trading variables are computed using retail holdings data from a large U.S. discount brokerage house. The retail data are available only for the 1991 to 1996 period.


 

The change in local institutional or retail holdings is the percentage change in the value of local holdings (i.e., the total value of shares of firms headquartered in a state held by investors in the same state). The change in nonlocal institutional or retail holdings is the percentage change in the value of nonlocal holdings (i.e., the total value of shares of firms headquartered in a state but held by out-of-state investors). To compute the state economic activity index, we add the standardized values of state income growth and state, subtract the standardized value of relative unemployment, and divide this sum by three.


 



 


 

"Low-paid, repetitive positions are most likely to go, with people earning less than £30,000 a year five times more likely to see their jobs taken over by machines than those paid £100,000, new research has warned. Huge advances in technology risks creating an under-class of low-skilled people whose jobs have been automated, according to a joint report from Deloitte, the Big Four accountancy firm, and the University of Oxford.[18]"


 


 


 


 

Year

Entrepeneurship Rate

Unemployment Rate

Median home price

Entrepeneurship

1996

0.33

0.28

160000

210000

1997

0.27

0.26

165000

185000

1998

0.28

0.24

167000

180000

1999

0.26

0.23

180000

182000

2000

0.27

0.24

182000

183000

2001

0.26

0.23

185000

185000

2002

0.28

0.25

187000

190000

2003

0.29

0.27

205000

210000

2004

0.29

0.26

215000

215000

2005

0.28

0.27

220000

225000

2006

0.27

0.24

240000

185000

2007

0.31

0.24

230000

192000

2008

0.33

0.27

210000

210000

2009

0.34

0.38

185000

225000

2010

0.35

0.4

175000

235000


 


 


 


 

SUMMARY OUTPUT ENTREPENEURSHIP AND UNEMPLOYMENT

       
         

Regression Statistics

       

Multiple R

0.804484

       

R Square

0.647195

       

Adjusted R Square

0.620056

       

Standard Error

0.018609

       

Observations

15

       
         

ANOVA

        

  

df

SS

MS

F

Significance F

   

Regression

1

0.008258

0.008258

23.84756

0.000299

   

Residual

13

0.004502

0.000346

     

Total

14

0.01276

  

  

  

   
         

  

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

0.165243

0.026801

6.165666

3.4E-05

0.107344

0.223142

0.107344

0.223142

X Variable 1

0.475703

0.097412

4.883397

0.000299

0.265257

0.68615

0.265257

0.68615

         
       


 

 
         


 

SUMMARY OUTPUT MEDIUM HOME PRICE AND ENTRPENEURSHIP

       
         

Regression Statistics

       

Multiple R

0.086584

       

R Square

0.007497

       

Adjusted R Square

-0.06885

       

Standard Error

0.031212

       

Observations

15

       
         

ANOVA

        

  

df

SS

MS

F

Significance F

   

Regression

1

9.57E-05

9.57E-05

0.098195

0.758977

   

Residual

13

0.012664

0.000974

     

Total

14

0.01276

  

  

  

   
         

  

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

0.314501

0.065916

4.771234

0.000365

0.172098

0.456903

0.172098

0.456903

X Variable 1

-1.1E-07

3.38E-07

-0.31336

0.758977

-8.4E-07

6.24E-07

-8.4E-07

6.24E-07

         
         
         


 

CONCLUSION:


 

Through many tests of Data analysis, regression, studies of quotations, that has shown through multiple T-Tests, that

Entrepreneurship and unemployment and home prices are correlated through regression, I have not been able to prove that Institutions are increasing Entrepeneurship rates because of lack of Data available, except through graphs available but not data.


 

ENTREPENEURSHIP_i = .16*UNEMPLOYMENT_i + .32*HOMEPRICE_i +Log(C*INSTITUTIONS(0))+ e_i

(6.16)     (4.77)


 

N=15 R^2=.64


 

Bibliography:


 

References

  1. Baharum, A., & Ataharul, M. The analysis of transitions in economic performance using co variate dependent statistical models.Journal of Developing Areas, 43(2), 289-298.
  2. Chien-Chung Nieh, Russel, P. S., Hung, K., & Ya-Kai Chang. The asymmetric impact of financial intermediaries development on economic growth. International Journal of Finance, 21(2), 6035-6069.
  3. Chris Forman, Avi Goldfarb, & Shane Greenstein. The internet and local wages: A puzzle. The American Economic Review, 102(1), 556-575.
  4. De V. Cavalcanti, T. V., & Novo, Á. A. Institutions and economic development: How strong is the relation? Empirical Economics, 30(2), 263-276.
  5. Edelman, B. Using internet data for economic research. Journal of Economic Perspectives, 26(2), 189-206.
  6. Einav, L., & Levin, J. The data revolution and economic analysis. NBER Innovation Policy & the Economy (University of Chicago Press), 14(1), 1-24.
  7. Fairlie, R. W. Entrepreneurship, economic conditions, and the great recession. Journal of Economics & Management Strategy, 22(2), 207-231.
  8. Florens, J., Johannes, J., & Van Bellegem, S. Instrumental regression in partially linear models. Econometrics Journal, 15(2), 304-324.
  9. Kearns, B., Ara, R., Wailoo, A., Manca, A., Alava, M., Abrams, K., et al. Good practice guidelines for the use of statistical regression models in economic evaluations. Pharmacoeconomics, 31(8), 643-652.
  10. KORNIOTIS, G. M., & KUMAR, A. State-level business cycles and local return predictability. Journal of Finance, 68(3), 1037-1096.
  11. Naiana, Ţ., Teodora, V., Cristian, C., & Ioan, Ţ. Study regarding the use of spreadsheet applications in the economic field. Annals of the University of Oradea, Economic Science Series, 19(1), 834-837.
  12. Nelson, N. E., & Germani, P. J. The use of economic data in collective bargaining. Labor Law Journal, 38(11), 715-719.
  13. Rodrigues, J. F. D. A bayesian approach to the balancing of statistical economic data. Entropy, 16(3), 1243-1271.
  14. Rodríguez, F. What can we really learn from growth regressions? Challenge (05775132), 51(4), 55-69.
  15. Webber, D. J., & Mearman, A. Students' perceptions of economics: Identifying demand for further study. Applied Economics, 44(9), 1121-1132.
  16. Cooper NJ, Sutton AJ, Mugford M, Abrams KR. Use of Bayesian Markov Chain Monte Carlo methods to model cost-of-illness data. Med Decision Mak. 2003;23(1):38–53.
  17. North D (1990) Institutions, institutional change, and economic performance. Cambridge University Press, Cambridge.

18. Ten Million Jobs at Risk from Advancing Technology." The Telegraph. Telegraph Media Group, 28 Apr. 0010. Web. 10 Nov. 2014.