Just tell me what R code to use here for questions. If you don't need R, then at least tell me how to do them.

Estimate the relationship between corn demand and price using time series data from 1926 through 2016.

Q

P

This command is telling RStudio to convert the columns D$P and D$Q in the dataframe D to time-series objects, with a start date of 1926 and a frequency of 1 time a year (or annual data).

library(lmtest) # Testing Linear Regression Models

library(dynlm) # Dynamic Linear Regression

library(sandwich) # Robust Covariance Matrix Estimators

Q2.2: Interpret your estimate of the price-elasticity of corn demand:

Q2.3 What is the upper and lower bound of the 95% confidence intervals for the price elasticity of corn demand (provide a number rounded to three decimal points)?

> P P Time Series: Start = 1926 End = 2016 Frequency = 1 P [1,] 0.03959538 [2,] 0.05760234 [3,] 0.05537059 [4,] 0.04718935 [5,] 0.03838461 [6,] 0.02375000 [7,] 0.02043651 [8,] 0.03675188 [9,] 0.06076642 [10,] 0.04434307 [11,] 0.08169014 [12,] 0.04104965 [13,] 0.03417266 [14,] 0.04127143 [15,] 0.04715493 [16,] 0.05239375 [17,] 0.05829070 [18,] 0.06149426 [19,] 0.06539888 [20,] 0.06475410 [21,] 0.07920091 [22,] 0.09999573 [23,] 0.05729832 [24,] 0.05832203 [25,] 0.06974806 [26,] 0.07089734 [27,] 0.05996993 [28,] 0.05870260 [29,] 0.05594007 [30,] 0.05035075 [31,] 0.04728417 [32,] 0.04164931 [33,] 0.04229412 [34,] 0.03999660 [35,] 0.03841611 [36,] 0.03756811 [37,] 0.04001311 [38,] 0.04018446 [39,] 0.04266134 [40,] 0.04001558 [41,] 0.04285152 [42,] 0.03414869 [43,] 0.03266482 [44,] 0.03256021 [45,] 0.03884000 [46,] 0.02964251 [47,] 0.03677829 [48,] 0.06253138 [49,] 0.05546110 [50,] 0.04850984 [51,] 0.04269580 [52,] 0.03764826 [53,] 0.03527650 [54,] 0.03245943 [55,] 0.03929492 [56,] 0.02800424 [57,] 0.02877630 [58,] 0.03284795 [59,] 0.02528008 [60,] 0.02103860 [61,] 0.01307850 [62,] 0.01647296 [63,] 0.02163123 [64,] 0.01901321 [65,] 0.01814296 [66,] 0.01893396 [67,] 0.01500209 [68,] 0.01871331 [69,] 0.01556473 [70,] 0.02516570 [71,] 0.01813000 [72,] 0.01593835 [73,] 0.01266424 [74,] 0.01212150 [75,] 0.01089047 [76,] 0.01074664 [77,] 0.01253529 [78,] 0.01541782 [79,] 0.01017951 [80,] 0.01021221 [81,] 0.01829688 [82,] 0.02414086 [83,] 0.01686967 [84,] 0.01562778 [85,] 0.02947621 [86,] 0.02818320 [87,] 0.03146684 [88,] 0.01963101 [89,] 0.01553594 [90,] 0.01487900 [91,] 0.01394041

> Q Time Series: Start = 1926 End = 2016 Frequency = 1

Q The variables in the dataset are: 1. year : Year 2. Q: Quantity of corn demanded (Q) in billions of bushels 3. P: Price of corn (P) in $/bushel (inflation adjusted) Consider the following population regression model: Model 1: log(Qt) = Bo + Bi log(Pt) + Et = Q2.1 0.25 Points Estimate Model 1 using ordinary least squares. What is your estimate of the price-elasticity of corn demand (provide a number rounded to three decimal points)? Enter your answer here The variables in the dataset are: 1. year : Year 2. Q: Quantity of corn demanded (Q) in billions of bushels 3. P: Price of corn (P) in $/bushel (inflation adjusted) Consider the following population regression model: Model 1: log(Qt) = Bo + Bi log(Pt) + Et = Q2.1 0.25 Points Estimate Model 1 using ordinary least squares. What is your estimate of the price-elasticity of corn demand (provide a number rounded to three decimal points)? Enter your answer here