Campaign Expenditure (part 4)   Use the VOTE.DTA data for th…

Campaign Expenditure (part 4)   Use the VOTE.DTA data for this question. Consider the following model voteA=β0+β1ln⁡(expendA)+β2ln⁡(expendB)+β3prtystrA+u{“version”:”1.1″,”math”:”voteA = \beta_0 + \beta_1 \ln(expendA) + \beta_2 \ln(expendB) + \beta_3 prtystrA + u”} where voteA is the percentage of the vote received by candidate A, expendA and expendB are the campaign expenditures by candidates A and B respectively, and prtystrA is the percentage of the most recent presidential vote that went to A’s party. Now re-parameterize the model to test the null hypothesis from the previous question, i.e., a 1% increase in candidate A’s expenditure would be exactly offset by a 1% increase in candidate B’s expenditure. Let θ=β1+β2{“version”:”1.1″,”math”:”θ=β1+β2″} The null hypothesis will be: 

Campaign Expenditure (part 3) Use the VOTE.DTA data for this…

Campaign Expenditure (part 3) Use the VOTE.DTA data for this question. Consider the following model voteA=β0+β1ln⁡(expendA)+β2ln⁡(expendB)+β3prtystrA+u{“version”:”1.1″,”math”:”voteA = \beta_0 + \beta_1 \ln(expendA) + \beta_2 \ln(expendB) + \beta_3 prtystrA + u”} where voteA is the percentage of the vote received by candidate A, expendA and expendB are the campaign expenditures by candidates A and B respectively, and prtystrA is the percentage of the most recent presidential vote that went to A’s party. Now re-parameterize the model to test the null hypothesis from the previous question, i.e., a 1% increase in candidate A’s expenditure would be exactly offset by a 1% increase in candidate B’s expenditure. Let θ=β1+β2{“version”:”1.1″,”math”:”θ=β1+β2″} The new regression model will be: 

Campaign Expenditure (part 1) Use the VOTE.DTA data for this…

Campaign Expenditure (part 1) Use the VOTE.DTA data for this question. Consider the following model voteA=β0+β1ln⁡(expendA)+β2ln⁡(expendB)+β3prtystrA+u{“version”:”1.1″,”math”:”voteA = \beta_0 + \beta_1 \ln(expendA) + \beta_2 \ln(expendB) + \beta_3 prtystrA + u”} where voteA is the percentage of the vote received by candidate A, expendA and expendB are the campaign expenditures by candidates A and B respectively, and prtystrA is the percentage of the most recent presidential vote that went to A’s party. Estimate the model and then give the interpretation of β^1{“version”:”1.1″,”math”:”β^1″}and β^2{“version”:”1.1″,”math”:”β^2″}.

Campaign Expenditure (part 5)   Use the VOTE.DTA data for th…

Campaign Expenditure (part 5)   Use the VOTE.DTA data for this question. Consider the following model voteA=β0+β1ln⁡(expendA)+β2ln⁡(expendB)+β3prtystrA+u{“version”:”1.1″,”math”:”voteA = \beta_0 + \beta_1 \ln(expendA) + \beta_2 \ln(expendB) + \beta_3 prtystrA + u”} where voteA is the percentage of the vote received by candidate A, expendA and expendB are the campaign expenditures by candidates A and B respectively, and prtystrA is the percentage of the most recent presidential vote that went to A’s party. Now re-parameterize the model to test the null hypothesis from the previous question, i.e., a 1% increase in candidate A’s expenditure would be exactly offset by a 1% increase in candidate B’s expenditure. Let θ=β1+β2{“version”:”1.1″,”math”:”θ=β1+β2″} The result of the test will be: