Chat with us, powered by LiveChat Download ?the ?data ?file ?CHICK6.csv ?from ?Assignment ?1. [2] Load the CHICK6.csv data into R and rename it so that it includes your own name or initials (for example, we ?might ?name ?our - EssayAbode

Download ?the ?data ?file ?CHICK6.csv ?from ?Assignment ?1. [2] Load the CHICK6.csv data into R and rename it so that it includes your own name or initials (for example, we ?might ?name ?our

Assignment  1
[1]  Download  the  data  file  CHICK6.csv  from  Assignment  1.
[2] Load the CHICK6.csv data into R and rename it so that it includes your own name or initials (for example,
we  might  name  our  dataset  JunfuCHICK6 ).
[3] Generate the mean and the standard deviation for the variables Y, PC, PB, and YD  from the CHICK6.csv
data  set.
[4]  You  are  going  to  run  a  regression  (in  [5])  where  Y  is  the  dependent  variable,  and  PC,  PB,  and  YD  are
the  independent  variables.  Discuss  whether  you  think  the  independent  variable  PB  will  have  a  negative  or
positive  eect  on  the  dependent  variable.  In  your  decision,  try  to  use  as  much  economic  theory  as  you  can  —
theory  is  what  motivates  what  variables  are  included  in  a  model  and  what  sign  we  anticipate  for  the  model’s
estimates.
[5]  Estimate  the  model  in  R  and  present  the  results.
[6]  Interpret  the  results  for  the  coefficient  PB  from  your  model.  Make  sure  to  include  whether  or  not  the
result  aligned  with  your  expectations. 

Econ 4650, Spring 2019 Assignment 1

Introduction

Welcome to Assignment 1!

It is suggested you do the following before attempting this assignment*

• Read the appropriate textbook chapters (Chapter 1 & Chapter 2 in the Custom Text; Chapter 17 & Chapter 1 in the full text)

• Read the Econ_4650_Sprin_2019_Intro_to_R.pdf in Module 1.a • Install the software R • Read OrdinaryLeastSquares pdf in Module 1.b • Take a deep breath!

*Note, You may also watch the Module 1 video tutorials, but this is not required. Also, please note that there have been adjustments to the number of assignments and the structure of the course since the time the videos were made.

Definitions for Annual Consumption of Chicken Data

Y = per capita chicken consumption (in pound) in a given year

PC = the price of chicken (in cents per pound) in a given year

PB = the price of beef (in cents per pound) in a given year

YD = U.S. per capita disposable income (in hundred of dollars) in a given year

Assignment 1

[1] Download the data file CHICK6.csv from Assignment 1.

[2] Load the CHICK6.csv data into R and rename it so that it includes your own name or initials (for example, we might name our dataset JunfuCHICK6 ).

[3] Generate the mean and the standard deviation for the variables Y, PC, PB, and YD from the CHICK6.csv data set.

[4] You are going to run a regression (in [5]) where Y is the dependent variable, and PC, PB, and YD are the independent variables. Discuss whether you think the independent variable PB will have a negative or positive e�ect on the dependent variable. In your decision, try to use as much economic theory as you can – theory is what motivates what variables are included in a model and what sign we anticipate for the model’s estimates.

[5] Estimate the model in R and present the results.

[6] Interpret the results for the coe�cient PB from your model. Make sure to include whether or not the result aligned with your expectations.

1

Rectangle
FreeText
Principles of Econometrics

,

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

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