see the attached file... the regression should be done in (r studio) and also provide the codes
Microsoft Word - Homework assignment 3.docx 1 Homework Assignment 3 Problem 1 Mitton (2002) analyses firm-level data from the five East Asian crisis economies of Indonesia, Korea, Malaysia, the Philippines, and Thailand to study the impact of corporate governance on firm performance during the Asian economic crisis. To measure firm performance during the crisis, he uses stock returns over the crisis period, from July 1997 through August 1998. To assess the impact of corporate governance variables on firm stock price performance during the crisis, he estimates the following model: uDummiesIndustryDummiesCountry LeverageSize VariablesGovernanceCorporateeturnRPeriodCrisis )()( )()( )( 54 32 10 One aect of corporate governance studied here is ownership concentration. As a measure of it, he considers two measures of ownership concentration. The first is the ownership percentage (in terms of cash flow rights) of the largest shareholder in the firm, which is named as LARGEST. The second is the total holding of all shareholders that own 5% or more of the stock, which is named as SUMMED.< estimation="" results=""> (i) (ii) (iii) Intercept LARGEST SUMMED SIZE LEVERAGE Country dummies Industry dummies −1.257*** (−5.52) 0.261*** (3.42) 0.083*** (3.42) −0.0027*** (−4.64) Included Included −0.776*** (−3.20) 0.087** (2.09) 0.050*** (2.09) −0.0033*** (−6.04) Included Included −1.012*** (−4.20) 0.107 (0.95) −0.048 (−0.25) 0.078*** (3.15) −0.0031*** (−5.45) Included Included No. of observations R2 294 0.307 384 0.262 294 0.311 The number in each parenthesis is White's heteroscedasticity-consistent t-statistic. *** and ** indicate the coefficient is significant at the level of 1% and 5%, respectively. 2 (a) The corporate governance variables LARGEST and SUMMED are significant factors when they are included separately, as shown in columns (i) and (ii). However, column (iii) shows that the coefficients for the two variables become insignificant when they are included together. Explain why this can happen. (b) The note to the table says that "White's heteroscedasticity-consistent t-statistics" are given in brackets. Explain what it means. Problem 2 In order to examine how effectively the government subsidies for employee training raised the amount of training provided, we have collected observations from 50 firms. The estimation results of the following regression model are shown below. iiiiii uWAGEEMPLOYSALESSUBSIDYTrainingCh logloglog 43210 where iTrainingCh = change rate of annual training hours per employee in firm i; iSUBSIDY = a dummy variable which is set to 1 if firm i was granted a subsidy, 0 otherwise; iSALESlog = annual (logarithm) sales; iEMPLOYlog = annual average (logarithm) number of employees; and iWAGElog = annual average (logarithm) wage. OLS estimation results: 3 White's test for heteroscedasticity: White's correction for heteroscedasticity: (a) Write the null and alternative hypotheses which evaluate the effectiveness of the subsidy policy. (b) Is it correct to use the OLS estimation results for testing the hypotheses? If not, what is the problem with the OLS estimation results? (c) What is your conclusion about the effectiveness of the subsidy policy? In other words, test the hypotheses specified in (a) at the significance level of 0.01. Problem 3 The White test evaluates a joint null hypothesis that a regression model is homoscedastic and the disturbances are independent of the explanatory variables (or no specification error in a sense). (a) What is the alternative hypothesis? (b) If the null hypothesis is rejected, what do we need to do? Problem 4 To examine the effects on the housing prices, we estimated the following regression. iiiiiii uZROOMSSIZEROOMSSIZEprice 43210 )( 4 where price = the housing price, SIZE = size measured in square feet, ROOMS = the number of rooms, and Z denotes other control variables. Below are the summary statistics of the variables and the estimation results of the above regression. Summary Statistics Variables Mean S.D. Minimum Maximum price (million $) SIZE (sq feet) ROOMS 1.6 1052 3.4 0.45 45 1.1 0.8 1000 2 3.1 2000 5 OLS Estimation Results Variables Coeff. estimate t-statistic p-value Intercept SIZE (sq feet) ROOMS SIZE × ROOMS Z 0.52 −0.1 −180 0.2 0.69 6.23 −5.89 −5.12 4.98 1.95 0.000 0.001 0.003 0.004 0.069 (a) Write the estimated equation. (b) State whether the following statement is True, False, or Uncertain. And explain why. "It is believed that the more rooms a house has, the higher its price is. However, the coefficient estimate for ROOMS is negatively significant. Therefore, the above estimation results are wrong and do not make sense." (c) What is the net effect of SIZE on the housing price? Calculate the range of the net effect. (d) Re-express the estimated equation in (a) using the centered variables at their sample means, e.g., using (SIZE−1052) and (ROOMS−3.4).