Use the data set chick6.xls in Moodle to answer the following: 

In the chick6 data set y=per capita chicken consumption, pounds per year; yd= disposable income per capita, hundreds of dollars; pc=price of chicken, cents per pound; pb=price of beef, cents per pound.

 

Before beginning you will want to inform STATA that this dataset are in the time-series format. To do this, generate a variable that identifies time.  In STATA, type generate time=_n.   Then type tsset time.

 

1.  
2. Estimate the linear function for the demand for chicken using price of chicken (pc), price of beef (pb), and disposable income (yd) as independent variables. What is the price elasticity of chicken? Is it inelastic or elastic? Explain
3.  
4. In STATA, calculate the correlation coefficients and VIF for the independent variables. Are there any signs of multicollinearity?  Why or why not?  Be sure to indicate in which variables multicollinearity is likely an issue.Explain.
5.  
6. Formulate the hypothesis for the Ramsey RESET test. Then, perform the Ramsey RESET test in STATA. Are there any signs of biased results? Why or why not?  Explain.
7.  
8. Estimate the lin-log model of the equation in (a). Which model, the linear or the lin-log fits better? Explain.
9.  
10. Estimate the log-log model of the equation in (a). What is the price elasticity of demand? Does this model fit better than the linear or lin-log? Explain.  
11.  

 

Chick6 data

                                                                                                                                                                                                                                                

OBS	YEAR	Y	PC	PB	YD	TEMP	PRP
1	1974	39.7	42.3	143.8	50.1	-16	107.8
2	1975	38.69	49.4	152.2	54.98	-4	134.6
3	1976	42.02	45.5	145.7	59.72	-24	134
4	1977	42.71	45.3	145.9	65.17	16	125.4
5	1978	44.75	49.3	178.8	72.24	5	143.6
6	1979	48.35	50	222.4	79.67	13	152.5
7	1980	48.47	53.5	233.6	88.22	21	147.5
8	1981	50.37	53.8	234.7	97.65	49	161.2
9	1982	51.52	51.5	238.4	104.26	4	185.6
10	1983	52.55	56	234.1	111.31	35	179.7
11	1984	54.61	61.5	235.5	123.19	11	171.4
12	1985	56.42	56.2	228.6	130.37	4	170.8
13	1986	57.7	63.1	226.8	136.49	18	188.8
14	1987	61.94	53.1	238.4	142.41	35	199.4
15	1988	63.8	62.1	250.3	152.97	46	194
16	1989	66.88	64.2	265.7	162.57	32	193.5
17	1990	70.34	60.5	281	171.31	64	224.9
18	1991	73.26	57.7	288.3	176.09	52	224.2
19	1992	76.39	59	284.6	184.94	18	209.5
20	1993	78.27	27.1	293.4	188.72	27	209.1
21	1994	79.65	26.2	282.9	195.55	48	209.5
22	1995	79.27	26.9	284.3	202.87	71	206.1
23	1996	80.61	28	280.2	210.91	36	233.7
24	1997	83.1	33.2	279.5	219.4	60	245
25	1998	83.76	33.4	277.1	231.61	89	242.7
26	1999	88.98	39.5	287.8	239.68	60	241.4
27	2000	90.08	43	306.4	254.69	62	258.2
28	2001	89.71	43.4	337.7	262.24	74	269.4
29	2002	94.37	43.9	331.5	271.45	85	265.8

Answer rating: 100% (QA)

Okay here are the steps to solve this problem 1 Inform STATA that this is a timeseries dataset by ge... View the full answer