First present the equations used in the empirical test of the Heckscher-Ohlin- Vanek (HOV) model explaining all variables, vectors and matrices used. Then present both hypotheses of the HOV model and explain how they relate to the original Heckscher-Ohlin-Samuelson hypothesis.
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First present the equations used in the empirical test of the Heckscher-Ohlin- Vanek (HOV) model explaining all variables, vectors and matrices used. Then present both hypotheses of the HOV model and explain how they relate to the original Heckscher-Ohlin-Samuelson hypothesis.
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- The following scatter plot shows hours of study on the X axis and the final exam score on the Y axis for 21 students in an Econometrics 322 class. The round data points are for economics majors; the square points are for non-majors (and yes, non- majors take econometrics). Score hoursExplain how the residuals of an ARMA model can be used to diagnose potential model mis-specification issues? If any, how can these misspecification issues, be addressed?Suppose you have time series data and estimate the following model y = β0 + β1x1 + ε. When you estimate the model, you find that there are correlated observations (serial correlation). You choose to ignore this issue. What are the consequences? A/ The predictions are biased, but the hypothesis tests are valid. B/ The predictions are not biased, but the hypothesis tests are invalid. C/ Both the predictions and the hypothesis tests are valid. D/ Both the predictions and the hypothesis tests are not valid. Choose the correct answer and briefly explain.
- Consider an equation to explain salaries of CEOS in terms of annual firm sales, return on equity (roe, in percentage form), and return on the firm's stock (ros, in percentage form): log(salary) = Bo + Bilog(sales) + Bzroe + B;ros + u. (i) In terms of the model parameters, state the null hypothesis that, after controlling for sales and roe, ros has no effect on CEO salary. State the alternative that better stock market performance increases a CEO's salary. (ii) Using the data in CEOSAL1, the following equation was obtained by OLS: log (salary) = 4.32 + .280 log(sales) + .0174 roe + .00024 ros (.32) (.035) (.0041) (.00054) n = 209, R2 = .283. By what percentage is salary predicted to increase if ros increases by 50 points? Does ros have a practically large effect on salary? (iii) Test the null hypothesis that ros has no effect on salary against the alternative that ros has a positive effect. Cary out the test at the 10% significance level. (iv) Would you include ros in a final model…Write both a null and research hypothesis for the following variables: study time and academic success exam scores and hours spent sleeping parental support and disciplinary conduct problemswould u kindly give an example of an economic question then identify variables as well as null and alternative hypothesis (hypotheses like μ > 25)
- The leverage or h statistic indicates how much influence an observation has on the estimation of the model parameters. Observations with x-values far from the others have larger influence. Move the mouse over each data point to see its leverage value. If all observations fit the regression model well, then omitting one observation should not change the analysis very much. Moving the mouse over the data point not only reveals its leverage value but also shows the regression line with that data point omitted. Examine how omitting certain points affects the regression model. y = 127.47 + (-0.43) x, r-sq = 0.799, t(5) = -4.46, p = 0.0066 %3D 130 120 110 - 100 10 25 40 55 70 85A) State whether the following statement(s) are true/false with justification i) If the model is linear in parameters, but there is non-linear relation between dependent and independent variables, OLS cannot be used. ii) OLS can be applied to estimate a multiple linear regression model, and each slope coefficient shows the full effect of an individual variable on the dependent variable iii) Both slope and intercept will change if dependent and independent variables are divided by factor k (=1000). B) In a left-tailed test where you reject Ho only in the lower tail, what is the p-value if Z = (-) 1.00?Finally consider the regression model with an extra covariate: Yi = Bo + B1x1i + B2x2; + €; Let 32=3.13, it's standard error s.e.(B1)=1.12 and assume n=811. Based only on this information, decide if it is useful to include X2i in the regression model. Carefully showing every step of your hypothesis test and explaining your answer. Use signifi- cance level a = .05.
- Consider the following Stata output for the model of house prices: lhprice=β0+β1bdrms+β2llotsize+β3lsqrft+u estimated on a random sample of 86 houses. You may assume the Gauss Markov Assumptions hold. The variables are defined as: lhprice = the natural log of the house price bedrms = the number of bedrooms llotsize = the natural log of the land or lot size lsqrft = the natural log of the floor space of the house in square feet. Source SS df MS Model Residual 4.65621742 2.09289629 3 82 1.55207247 0.025523126 Total 6.74911372 85 0.079401338 lprice coef. std. err. t P > |t| [95% Conf. Interval] bdrms 0.0581214 -0.0055282 0.121771 llotsize 0.1494716 0.0616548 0.2372884 lsqrft 0.636171 0.421989 0.850353 _cons -0.7083136 -2.221521 0.8048935 Number of obs = 86 F (3, 82) = 60.81 Prob > F = 0.0000 R-squared = 0.6899 Adj R-squared = 0.6786 Root MSE = 0.15976 Does…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 that there is a running variable (such as GPA) that have a sharp cutoff (such as 3.8). If a student has a GPA above the cutoff, they are eligible to receive a very generous scholarship to attend college. You are interested in studying the effects of attending college on risky health behaviors. Given this description, which of the following is the most appropriate technique for your study? difference-in-difference (DID) Regression discontinuity design (RDD) instrumental variables (IV)