Consider the following numerical example of the IS-LM model: C= 100 + 0.3Yd ; I = 150 + 0.2Y – 1000 i ; G = 200 ; T = 100. Derive the IS relation. I've got the answer written down as =400+0.5Y-500i can someone please provide solutions as to how it is this.

(e) Estimate the following model: In exports = Bo + B1 ln gdp_d + B2 ln dist_d + B3comlang + B4 (In dist_d x comlang) + u Calculate the marginal effect of distance on UK's exports. Interpret this result.

There are four key relationships upon which the Standard Model is based. Please list each of the four relationships and give one example for each

Suppose that in the model of Myers and Majluf (1984) the parameters take on the following values: λ = 0.60, H = 3, L = 2, I = 1.0, R = 1.25, r = 10 per cent. Use the above data and answer the following questions: (a) Does the model have a separating equilibrium, a pooling equilibrium, or both? (b) Based on your answer to question (a), what is the gain in wealth of the current shareholders of a firm that announces the issuing of new shares to finance the project of investment?

Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married females?

Suppose we have the following causal model: Ya₁ + ₁ X + u, X + a₂ + B₂Y + v, where E(u) = E(v) = 0, cov(u, v) The best linear prediction of Y given X is: Y = a₁ + ß₁X + error. Similarly, the best linear prediction of Y given X is: X = a* + ß₂Y+ error. Which of the following statements is/are true? 3* = 0 if and only if ₁ = 0 OB₁ B₂ if B₁ = B₂ B = 0 if 3₂ = 0 O B₁ = 0 if B = 0 = : 0. =

You are the owner of a restaurant located in a beach resort in Hawaii and want to use regression analysis to estimate the demand for your fresh seafood dinners. You have collected data on the daily quantity of seafood dinners sold over the last summer season. In order to correctly specify your regression equation, which of the following variables should be considered? Select one: A. the prices charged for souvenirs in local stores B. the prices charged for scuba diving excursions at the resort C. the wages paid to your chef and servers D. the daily number of vacationers at the resort

The following model studies human welfare using the General Social Survey in the US: happy = 0.014 + 0.209regattend + 0.103occattend + 0.027income + 0.009educ +0.003 female where happy is a dummy variable equal to one if a person is happy, and 0 otherwise: regattend is a dummy variable equal to one if a person regularly attends church, and 0 otherwise: occattend is a dummy variable equal to one if a person occasionally attends church, and 0 otherwise; income is family income in tens of thousands: educ is years of schooling: female = 1 if female. = 0 otherwise Interpret the impact of religion on happiness.

Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the estimated coefficients for each of the variables from part b? Be specific. Note Don't forget to include dummy variables.

Can we compare the linear-log model and the log-log model? Which of the log regression models best fits the data?