Consider the log of real GDP and log of nominal wage of a country (8 marks)
OLS Regression model: Dependent variable is log of wage Variable Coefficient Std. Error t-Statistic Prob. C -1.27404 0.062811 -0.28361 0.0000 LNGDP 0.745639 0.004957 150.4348 0.0000 R-squared 0.996565 Mean dependent var 8.112247 Adjusted R-squared 0.996521 S.D. dependent var 1.095522 S.E. of regression 0.064616 Akaike info criterion -.616032
Sum squared resid 0.325666 Schwarz criterion -.556482 Log likelihood 106.6413 F-statistic 22630.63 Durbin-Watson stat 0.301944 Prob(F-statistic) 0.00000 How do you interpret the coefficient of logGDP? Is there autocorrelation problem? Why? Explain in detail Are log of GDP and w integrated of order zero (I(0))?
ADF-test statistics Variables I(0) I(1) lngdp 0.528899 -4.83401 lnw 0.001598 -4.40925 Critical values 1% -3.5164 -3.5176 5% -2.8991 -2.8996 10% -2.5865 -2.5868 Is the regression result spurious? Are the variables cointegrated? Variables I(0) I(1) Lngdp 0.528899 -4.83401 Lnw 0.001598 -4.40925 Residual -2.92463 Critical values 1% -3.5164 -3.5176 5% -2.8991 -2.8996 10% -2.5865 -2.5868 From data for 54 standard metropolitan statistical areas (SMSA),
Demaris estimated the following logit model to explain high murder rate versus low murder rate(4 marks): ln(O_i ) ̂ = 1.1387 + 0.0014P_i + 0.0561C_i − 0.4050 R_i se = (0.0009) (0.0227) (0.1568) where, O = the odds of a high murder rate, P = 1980 population size in thousands, C = population growth rate from 1970 to 1980, R = reading quotient, and the se are the asymptotic standard errors.
How would you interpret the various coefficients? Which of the coefficients are individually statistically significant? What is the effect of a unit increase in the reading quotient on the odds of having a higher murder rate? What is the effect of a percentage point increase in the population Growth rate on the odds of having a higher murder rate?
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