Presentation is loading. Please wait.

Presentation is loading. Please wait.

LOGO Analysis of Unemployment Qi Li Trung Le David Petit Brian Weinberg Dwaraka Polakam Doug Skipper-Dotta Team #4.

Published by Modified over 9 years ago

Similar presentations

Presentation on theme: "LOGO Analysis of Unemployment Qi Li Trung Le David Petit Brian Weinberg Dwaraka Polakam Doug Skipper-Dotta Team #4."— Presentation transcript:

1 LOGO Analysis of Unemployment Qi Li Trung Le David Petit Brian Weinberg Dwaraka Polakam Doug Skipper-Dotta Team #4

2 Table of Contents Concepts of Unemployment 1 Descriptive Data Analysis 2 Statistical Analysis 3 Conclusions 4 Questions? 5

3 Group #4 Concepts of Unemployment EmployedUnemployedNot Looking Population Labor Force Labor Force: People willing to work at market equilibrium wage, both employed and unemployed Unemployment Rate: Number of Unemployed/Labor Force Keynesian View: Unemployment consists of excess labor supply in market economy Classical View: The unemployed consist of those searching for jobs

4 Team #4 Descriptive Statistics  Data from prior studies

5 Variables  Unemployment Rate  No Degree/Degree  Men/Women  White/Minority  Other Rates  Crime Rate  Suicide Rate  Welfare Budget  Annual Income Per Capita

6 Team #4 Descriptive Statistics  Histograms  Crime Rate  Annual Income  Suicide Rate  Welfare Budget  Unemp Rate

7 Team #4 Descriptive Statistics  Histograms No Degree Women White Minor Unemp Rate Men Degree

8 Exploratory Data Analysis Team #4 Test for Equality of Means Between Series Date: 11/25/10 Time: 21:04 Sample: 1 10 Included observations: 10 MethoddfValueProbability t-test18-0.6195310.5433 Satterthwaite-Welch t-test*15.10255-0.6195310.5448 Anova F-test(1, 18)0.3838190.5433 Welch F-test*(1, 15.1025)0.3838190.5448 *Test allows for unequal cell variances Analysis of Variance Source of VariationdfSum of Sq.Mean Sq. Between10.8405 Within1839.4172.189833 Total1940.25752.118816 Category Statistics VariableCountMeanStd. Dev.Std Mean Err WOMEN_UNEMP105.321.1093540.350809 MEN_UNEMP105.731.7745420.56116 All205.5251.4556150.325485  Unemployment rates between Men and Women have no significant difference  High f-test probability  A labor market that does not discriminate on the basis of sex

9 Team #4 Exploratory Data Analysis  Unemployment Rate is Regressed against male unemployment rate and female unemployment rate  The regression is Significant as seen by the F-stat  The variables are both equally significant in the unemployment rate as seen by their the t-stat  Therefore male and female unemployment rates are very close. Dependent Variable: UNEMP_RATE Method: Least Squares Date: 11/25/10 Time: 21:20 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C0.0145420.1095220.1327760.8981 MEN_UNEMP0.5366520.03768414.24070 WOMEN_UNEMP0.4602340.0602817.6348590.0001 R-squared0.999796 Mean dependent var5.538 Adjusted R-squared0.999738 S.D. dependent var1.4607 S.E. of regression0.023633 Akaike info criterion-4.409 Sum squared resid0.00391 Schwarz criterion-4.318 Log likelihood25.04513 Hannan-Quinn criter.-4.5086 F-statistic17187.56 Durbin-Watson stat3.3868 Prob(F-statistic)0

10 Team #4 Exploratory Data Analysis  Without a constant, the regression variables have even greater significance Dependent Variable: UNEMP_RATE Method: Least Squares Date: 11/25/10 Time: 21:15 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. MEN_UNEMP0.5319680.0124142.864860 WOMEN_UNEMP0.4680.01366734.243310 R-squared0.999796 Mean dependent var5.538 Adjusted R-squared0.99977 S.D. dependent var1.460706 S.E. of regression0.022134 Akaike info criterion-4.60651 Sum squared resid0.003919 Schwarz criterion-4.54599 Log likelihood25.03255 Hannan-Quinn criter.-4.6729 Durbin-Watson stat3.327676

11 Team #4 Exploratory Data Analysis  Unemployment rates between those with a degree and those without differ significantly Test for Equality of Means Between Series Date: 11/25/10 Time: 21:06 Sample: 1 10 Included observations: 10 MethoddfValueProb t-test18-5.6304310.000 Satterthwaite-Welch t-test*12.48443-5.6304310.001 Anova F-test(1, 18)31.701750.000 Welch F-test*(1, 12.4844)31.701750.001 *Test allows for unequal cell variances Analysis of Variance Source of VariationdfSum of Sq.Mean Sq. Between132126055 Within18182409171013384 Total19503669722650893 Category Statistics VariableCountMeanStd. Dev. Std. Err. Of Mean DEGREE_UNEMP101547.6582.9332184.3397 NO_DEGREE_UNEMP104082.41298.829410.7259 All2028151628.156364.0668

12 Team #4 Exploratory Data Analysis  There is no significant relationship (as seen by the t-stats) between having a degree and being unemployed or having no degree and being unemployed  Intuitively this seems very wrong and can be accounted for by the constant.  In the next slide the constant will be removed Dependent Variable: UNEMP_RATE Method: Least Squares Date: 11/25/10 Time: 21:18 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C1.3946680.41333.374490.0118 DEGREE_UNEMP0.0015120.001351.124470.2979 NO_DEGREE_UNEMP0.0004420.00060.732030.4879 R-squared0.991372 Mean dependent var5.538 Adjusted R-squared0.988907 S.D. dependent var1.461 S.E. of regression0.15385 Akaike info criterion-0.662 Sum squared resid0.165688 Schwarz criterion-0.57 Log likelihood6.311784 Hannan-Quinn criter.-0.762 F-statistic402.1441 Durbin-Watson stat0.437 Prob(F-statistic)0

13 Team #4 Exploratory Data Analysis  With the Constant removed both variables become significant  Small coefficients imply a very small effect on the unemployment rate Dependent Variable: UNEMP_RATE Method: Least Squares Date: 11/25/10 Time: 21:19 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. DEGREE_UNEMP-0.0026320.00083-3.1691110.0132 NO_DEGREE_UNEMP 0.002350.000327.3410460.0001 R-squared0.977336 Mean dependent var5.538 Adjusted R-squared0.974503 S.D. dependent var1.4607 S.E. of regression0.233243 Akaike info criterion0.1034 Sum squared resid0.43522 Schwarz criterion0.1639 Log likelihood1.483058 Hannan-Quinn criter.0.037 Durbin-Watson stat0.492591

14 Team #4 Exploratory Data Analysis  Annual Income is not significant when regressed with a constant  Low t-stat and R 2 Dependent Variable: AN_INC_PER_CAP Method: Least Squares Date: 11/25/10 Time: 21:24 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C28599.635233.2135.4650230.0006 UNEMP_RATE1099.2916.70161.1990810.2648 R-squared0.152344 Mean dependent var34687 Adjusted R-squared0.046388 S.D. dependent var4113.64 S.E. of regression4017.095 Akaike info criterion19.6114 Sum squared resid1.29E+08 Schwarz criterion19.6719 Log likelihood-96.05681 Hannan-Quinn criter.19.545 F-statistic1.437796 Durbin-Watson stat0.41066 Prob(F-statistic)0.264805

15 Team #4 Exploratory Data Analysis  This regresses the Unemployment rate vs the Crime rate  We found that the unemployment rate is not a significant factor in the crime rate as seen by the low f-stat and the low t-stat Dependent Variable: CRIMERATE Method: Least Squares Date: 11/25/10 Time: 21:26 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C125.241516.14427.757680.0001 UNEMP_RATE1.1310032.8279780.3999330.6997 R-squared0.019601 Mean dependent var131.505 Adjusted R-squared-0.102948 S.D. dependent var11.8 S.E. of regression12.39253 Akaike info criterion8.04892 Sum squared resid1228.599 Schwarz criterion8.10944 Log likelihood-38.24461 Hannan-Quinn criter.7.98254 F-statistic0.159947 Durbin-Watson stat0.45088 Prob(F-statistic)0.699672

16 Team #4 Exploratory Data Analysis  This regression has the Unemployment Rate vs Suicide Rate  We found that there is a slight relationship between the two  The f-stat is low, but the R 2 indicates that there is some relationship between the variables Dependent Variable: SUICIDE_RATE Method: Least Squares Date: 11/25/10 Time: 21:31 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C10.314780.36848627.99230 UNEMP_RATE0.12680.0645481.9644420.0851 R-squared0.325409 Mean dependent var11.017 Adjusted R-squared0.241085 S.D. dependent var0.32469 S.E. of regression0.282856 Akaike info criterion0.4891 Sum squared resid0.640059 Schwarz criterion0.54961 Log likelihood-0.445485 Hannan-Quinn criter.0.42271 F-statistic3.859031 Durbin-Watson stat0.58848 Prob(F-statistic)0.085072

17 Team #4 Exploratory Data Analysis  Welfare regressed against unemployment shows a significant relationship between the two  Intuitively, as the number of unemployed people grows, the greater demand for welfare Dependent Variable: WELFARE_BUDGET Method: Least Squares Date: 11/25/10 Time: 21:34 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. C1131949327896.73.452150.0087 UNEMP_RATE159511.557437.652.7771240.024 R-squared0.490849 Mean dependent var2015323 Adjusted R-squared0.427205 S.D. dependent var332568 S.E. of regression251698.6 Akaike info criterion27.8867 Sum squared resid5.07E+11 Schwarz criterion27.9472 Log likelihood-137.4335 Hannan-Quinn criter.27.8203 F-statistic7.712418 Durbin-Watson stat0.35586 Prob(F-statistic)0.024031

18 Team #4 Exploratory Data Analysis  Here the Unemployment Rate is regressed against multiple variables  All variables are significantly contribute to the Unemployment Rate  Annual Inc per cap coefficient is negative, suggesting a higher income implies a lower unemployment rate  Surprisingly, as crime rate increases unemployment decreases Dependent Variable: UNEMP_RATE Method: Least Squares Date: 11/28/10 Time: 12:20 Sample: 1 10 Included observations: 10 VariableCoefficientStd. Errort-StatisticProb. AN_INC_PER_CAP-0.0004110.000113-3.647430.0148 CRIMERATE-0.0835540.02457-3.400710.0192 SUICIDE_RATE4.7287421.3832113.4186690.0189 WELFARE_BUDGET5.61E-061.13E-064.9795030.0042 C-32.6309411.73778-2.779990.0389 R-squared0.948687 Mean dependent var5.538 Adjusted R-squared0.907637 S.D. dependent var1.46071 S.E. of regression0.443928 Akaike info criterion1.52054 Sum squared resid0.98536 Schwarz criterion1.67184 Log likelihood-2.602719 F-statistic23.1103 Durbin-Watson stat2.027352 Prob(F-statistic)0.00201

19 Team #4 Statistical Analysis Income Welfare Suicide Constant Crime Unemployment What does it effect? – +

20 Team #4 Statistical Analysis UnemploymentUnemployment Significant Regressions Education Sex Ethnicity

21 Team #4 Conclusion  Recap:  Regressing unemployment rate with these a few durations has no meanings.  Unemployment rates between Men and Women have no significant difference  We can compare different sample means:  Unemployment rates between Men and Women have no significant difference:  Unemployment rates between Degree and No Degree have significant difference:  Regress unemployment rate with men and women unemp (with c and without c):  Regress unemployment rate with degree and no degree unemp (with c and without c):  Regress annual income with unemployment rate (not significant, no relationship):  Regress crime rate with unemployment rate (not significant, no relationship):  Regress suicide rate with unemployment rate (not significant, some relationship):  Regress welfare budget with unemployment rate (significant, strong relationship):  Regressing unemployment rate with these four variables has no meanings.  Regress Unemployment with Annual Income, Crime rate, Suicide rate, Welfare budget(Significant)

22 Team #4 Conclusions  I have no money and cannot get any work  Father, can ’ t I have a piece of bread  I say father, could you get some specie claws?  I ’ m so hungry  My dear, cannot you continue to get some food for the children I don ’ t care for myself  I say Sam, I wonder where we are to get our Costs  **Warrant Distraint for rent**

23 Team #4 Future Investigations  Next time, I top down approach how does state and county unemployment break down.

24 Team #4 Future Investigations  Or a bottom up approach that considers the dynamic between US unemployment and international unemployment.

25 LOGO Team #4

26 Technical Appendix CountryRates:InterestGrowthInflationJoblessExchange Current Account United States 0.25%2.00%1.20%9.60%82.92 -123 Year JanFebMarAprMayJunJulAugSepOctNovDec 2010 9.7 9.99.79.5 9.6 2009 7.78.28.68.99.49.59.49.79.810.110 2008 54.85.155.45.55.86.16.26.66.97.4

27 Team #4 Works Cited  http://www.bls.gov/cps/  http://en.wikipedia.org/wiki/Une mployment  http:://en.wikipedia.org/wiki/Fil e:Panic1873.jpg  http://upload.wikimedia.org/wik ipedia/commons/c/ce/Chomage- oecd-t3-2009.png

Download ppt "LOGO Analysis of Unemployment Qi Li Trung Le David Petit Brian Weinberg Dwaraka Polakam Doug Skipper-Dotta Team #4."

Similar presentations

EXCHANGE RATE RISK CASE STUDY ROMANIA STUDENT: ŞUTA CORNELIA-MĂDĂLINA SUPERVISOR: PROF. MOISĂ ALTĂR.

EXCHANGE RATE RISK CASE STUDY ROMANIA STUDENT: ŞUTA CORNELIA-MĂDĂLINA SUPERVISOR: PROF. MOISĂ ALTĂR.
COINTEGRATION 1 The next topic is cointegration. Suppose that you have two nonstationary series X and Y and you hypothesize that Y is a linear function.

COINTEGRATION 1 The next topic is cointegration. Suppose that you have two nonstationary series X and Y and you hypothesize that Y is a linear function.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(LGDPI) Method: Least Squares Sample (adjusted): 1960 2003 Included observations: 44 after adjustments.

Augmented Dickey-Fuller Test Equation Dependent Variable: D(LGDPI) Method: Least Squares Sample (adjusted): Included observations: 44 after adjustments.
Christopher Dougherty EC220 - Introduction to econometrics (chapter 13) Slideshow: tests of nonstationarity: example and further complications Original.

Christopher Dougherty EC220 - Introduction to econometrics (chapter 13) Slideshow: tests of nonstationarity: example and further complications Original.
============================================================ Dependent Variable: LGHOUS Method: Least Squares Sample: 1959 2003 Included observations:

============================================================ Dependent Variable: LGHOUS Method: Least Squares Sample: Included observations:
AUTOCORRELATION 1 The third Gauss-Markov condition is that the values of the disturbance term in the observations in the sample be generated independently.

AUTOCORRELATION 1 The third Gauss-Markov condition is that the values of the disturbance term in the observations in the sample be generated independently.
FITTING MODELS WITH NONSTATIONARY TIME SERIES 1 Detrending Early macroeconomic models tended to produce poor forecasts, despite having excellent sample-period.

FITTING MODELS WITH NONSTATIONARY TIME SERIES 1 Detrending Early macroeconomic models tended to produce poor forecasts, despite having excellent sample-period.
Welcome to Econ 420 Applied Regression Analysis Study Guide Week Three Ending Tuesday, September 11 (Note: You must go over these slides and complete every.

Welcome to Econ 420 Applied Regression Analysis Study Guide Week Three Ending Tuesday, September 11 (Note: You must go over these slides and complete every.
Chapter 4 Using Regression to Estimate Trends Trend Models zLinear trend, zQuadratic trend zCubic trend zExponential trend.

Chapter 4 Using Regression to Estimate Trends Trend Models zLinear trend, zQuadratic trend zCubic trend zExponential trend.
1 TIME SERIES MODELS: STATIC MODELS AND MODELS WITH LAGS In this sequence we will make an initial exploration of the determinants of aggregate consumer.

1 TIME SERIES MODELS: STATIC MODELS AND MODELS WITH LAGS In this sequence we will make an initial exploration of the determinants of aggregate consumer.
NBA Statistical Analysis Econ 240A. Intro. to Econometrics. Fall 2010. Group 3 Lu Mao Ying Fan Matthew Koson Ryan Knefel Eric Johnson Tyler Nelson Grop.

NBA Statistical Analysis Econ 240A. Intro. to Econometrics. Fall Group 3 Lu Mao Ying Fan Matthew Koson Ryan Knefel Eric Johnson Tyler Nelson Grop.
Angela Sordello Christopher Friedberg Can Shen Hui Lai Hui Wang Fang Guo.

Angela Sordello Christopher Friedberg Can Shen Hui Lai Hui Wang Fang Guo.
Factors Determining the Price Of Used Mid- Compact Size Vehicles Team 4.

Factors Determining the Price Of Used Mid- Compact Size Vehicles Team 4.
1 Lecture Twelve. 2 Outline Failure Time Analysis Linear Probability Model Poisson Distribution.

1 Lecture Twelve. 2 Outline Failure Time Analysis Linear Probability Model Poisson Distribution.
TAKE HOME PROJECT 2 Group C: Robert Matarazzo, Michael Stromberg, Yuxing Zhang, Yin Chu, Leslie Wei, and Kurtis Hollar.

TAKE HOME PROJECT 2 Group C: Robert Matarazzo, Michael Stromberg, Yuxing Zhang, Yin Chu, Leslie Wei, and Kurtis Hollar.
Marietta College Week 14 1 Tuesday, April 12 2 Exam 3: Monday, April 25, 12- 2:30PM Bring your laptops to class on Thursday too.

Marietta College Week 14 1 Tuesday, April 12 2 Exam 3: Monday, April 25, 12- 2:30PM Bring your laptops to class on Thursday too.
1 Econ 240 C Lecture 3. 2 Part I Modeling Economic Time Series.

1 Econ 240 C Lecture 3. 2 Part I Modeling Economic Time Series.
1 Econ 240 C Lecture 3. 2 3 4 5 6 1 White noise inputoutput 1/(1 – z) White noise input output Random walkSynthesis 1/(1 – bz) White noise input output.

1 Econ 240 C Lecture White noise inputoutput 1/(1 – z) White noise input output Random walkSynthesis 1/(1 – bz) White noise input output.
Is There a Difference?. How Should You Vote? Is “Big Government” better?Is “Big Government” better? –Republicans want less government involvement. –Democrats.

Is There a Difference?. How Should You Vote? Is “Big Government” better?Is “Big Government” better? –Republicans want less government involvement. –Democrats.
Global Warming: Is It True? Peter Fuller Odeliah Greene Amanda Smith May Zin.

Global Warming: Is It True? Peter Fuller Odeliah Greene Amanda Smith May Zin.

Similar presentations

Ads by Google