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Consider a fictional economy that produces only two goods: quinoa and tofu. Information for prices (P) and quantities (Q) over a two-year sample is provided in table 1.    Based on the information provided, nominal GDP for year 2075 is $[word1] while nominal GDP for year 2076 is $[word2]. It can then be inferred that the actual growth rate in nominal GDP is [word3]%. Meanwhile, an approximation using log differences yields [word4]%, which is a relatively [word5] approximation since the actual growth rate in nominal GDP from 2075 to 2076 is relatively [word6].Since nominal GDP is tainted by the effect of prices, a better measure of living standards can be obtained using [word7]. Considering 2076 as the base year, real GDP for year 2075 is $[word8] while real GDP for year 2076 is $[word9].  To avoid the effects associated with [word10], chain-weighted GDP can be computed as a measure of real GDP. In this case, the chain weight for real GDP is [word11], while chain-weighted real GDP for 2075 (expressed in 2076 dollars) is $[word12], and chain-weighted real GDP for 2076 (expressed in 2076 dollars) is $[word13].

What is the effect of removing weak qualifiers such as ‘I think’ or ‘in my opinion’?

Table 2: The table below provides information about a fictional economy in which the typical consumer’s basket consists of 12 pairs of socks and 4 pairs of gloves.   Refer to table 2. If the consumer purchased the CPI basket, the cost of the basket was lowest in year [word1], with a total value of $[word2]. Under the assumption that the base year is year [word3], the CPI was 90.42 in year 3. If the base year is year 3, CPI in year 2 is [word4] and the CPI inflation rate between year 2 and year 3 is [word5] percent. If the consumer purchased 15 pairs of socks and 2 pairs of gloves in year 3, then the actual cost of basket in year 3 is $[word6] and the change in the cost of the basket the consumer actually purchased between year 2 and year 3 is, therefore, [word7] percent, which evidences that CPI overstated inflation in year 3 by [word8] percentage points. An analysis of the reasons why the consumer decided to purchase a different basket in year 3 suggests that this is due to a change in [word9], which implies that the overstatement of CPI inflation is likely associated with the problem of [word10]. Meanwhile, an increase in the prices of socks and gloves will [word11] since those items are [word12], and will [word13] since those items are [word14].

Figure 1 provides the recent evolution of selected macroeconomic aggregates. Based on our class discussion regarding (i) the importance of distinguishing long-run trend vis-à-vis short-run deviations from trend dynamics; (ii) the current state of the labor market; (iii) the persistent or temporary nature of recent inflation dynamics; and (iv) the level of GDP relative to trend, briefly analyze the current macroeconomic environment using sound economic arguments. To guide your analysis, focus on outlining answers to the following questions (bullet points are perfectly valid): Figure 1: Selected U.S. macroeconomic aggregates (various samples) a. Provide an economic assessment of the cyclical nature of economic activity. In particular, is the economy entering a recession in light of recent/current macroeconomic and political events and the FOMC’s decision to keep the federal funds rate steady yesterday? Answer yes or no and provide a reason why. Relate your answer to the data of figure 1.b. In your opinion, and give the current and expected situation, should the FOMC reduce rates in the May 7th, 2025 meeting? Argue in favor or against and provide a reason using sound economic reasons. Relate your answer to the data of figure 1.

Find the set  of all solutions of the following inhomogeneous linear system , where  and  are defined as follows:

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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 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 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 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: