hm help 1.6

Type your solutions in blue type below the problems you are answering. Note that the assignment has 2 pages. When you have completed the assignment, attach it as a word file (not as a PDF, pic, Pages, or in any other form) and return it to me at the address posted above. I won’t accept late assignments sent to other addresses after the fact.

A few reminders: answer carefully – make sure your answers are clear and concise. Answer only the problem that has been posed - do not add extraneous information. Remember to explain your answer, using the correct terminology.

1. From observations 1-51, gretl data set Ramanathan 7-24.gdt ; (40 pts).

Model 1: OLS, using observations 1-224

Dependent variable: sale price

	Coefficient	Std. Error	t-ratio	p-value
const	−745.43	77.565	−9.61	<0.0001	***
Sqft (#)	0.235	0.0117	20.04	<0.0001	***
Bedrms (#)	−19.97	15.93	−1.25	0.2114
Baths (#)	11.05	15.84	0.69	0.4860
Garage (#)	136.59	22.09	6.18	<0.0001	***
Age (years)	9.764	2.97	3.28	0.0012	***

Mean dependent var	642.92		S.D. dependent var	371.37
Sum squared resid	3842035		S.E. of regression	132.75
R-squared	0.875		Adjusted R-squared	0.872
F(5, 218)	305.42		P-value(F)	2.43e-96

1a. Evaluate the regression results for:

i. Influence of each independent variable

ii. statistical significance

iii. goodness of fit

1b. Do you notice a peculiar result regarding the independent variables? If yes, briefly discuss.

2. Suppose you have performed a regression (sample size n = 122) and the results are:

Ci = 27.5 + .511Yi + .046Ai +ui

(1.05) (.095)

r2 = .91; adjusted r2 = .79; s.e regression = 19.4

where C = consumption, Y = income, A = non-cash assets. Standard errors are in parentheses (15 pts).

2a. Do the equation, regression results, and summary statistics shown above suggest a potential problem in the regression to you? If so, what might that problem be?

2b. What in the equation and summary statistics leads you to that conclusion?

2c. What difficulty might the problem cause for your regression?

3. From gretl data set Ramanathan 7-13.gdt, identify and interpret the coefficients and appropriate hypothesis tests for only two independent variables in the results below: ‘Male,’ and ‘White.’ (30 pts).

OLS, using observations 1-252

Dependent variable: UCOMP (unemployment compensation rec’d)

	Coefficient	Std. Error	t-ratio	p-value
Const	−2935.14	693.20	−4.23	<0.0001	***
UHOURS (# hrs unemployed)	2.23	0.157	14.17	<0.0001	***
EDUC (# yrs educ)	136.96	48.54	2.82	0.0052	***
MALE (1=y,0=no)	457.69	215.09	2.12	0.0343	**
WHITE(1=y,0=no)	610.28	203.25	3.00	0.0030	***
FAMSIZE (number)	246.71	44.77	5.51	<0.0001	***

Mean dependent var	2136.54		S.D. dependent var	1920.39
Sum squared resid	4.58e+08		S.E. of regression	1363.94
R-squared	0.505		Adjusted R-squared	0.495
F(5, 246)	50.315		P-value(F)	8.91e-36

4. We have the following regression: ln_quant = a + b1Y + ln_pr; where ln_quant = log quantity in pounds, Y = income in dollars, and ln_pr = log price in dollars. Interpret the dependent and the 2 independent variables. (15 pts.)