Econometrics. a Regression Analysis

And the outputs of both dataset are consisted with the results applying the robust function. Betas stay the same and only the standard errors in both cases decline slightly. Similarly, the graphs in both cases remain the same. Based on the non-robust test, we then can confirm that the variables are independent and uncorrelated, which satisfies the easing assumption to state the model is consistent.

We are using the coefficients of excess returns of market, SMB and HML to run the second-pass (cross- section) regression. E[Ri-Rf] is from 20 observations of average excess return on assets, and the bi, si and hi are from 20 observations of beta on market risk premium, beta on SMB and beta on HML in first-pass regression estimation, respectively.

The equation for the age was y=19,839-1,070.25x while the equation for the mileage was y=17,627.5-0.174646x. The y values were relatively the same by the x’s were not. This was because of what each x in the equation represented was different. Since the ages went down by 1, the x could be large because the explanatory variable was fairly small only going from 1-10. The x for the mileage was fairly small because the explanatory variable was fairly large. The explanatory variable for this one stretched from 836 mi to 58,530 mi meaning that the x had to be small to accommodate for its size. The standard deviation of the residuals gave headway for age to be a better predictor. This mathematically shows that age is a better predictor of a car’s price than mileage by a slim

The market portfolio consisted of 8 asset classes 1 , in which we used an adjusted allocation size based

MULTIPLE CHOICE 1. value of a. b. c. d. ANS: A 2. a. b. c. d. ANS: A 3. correlation a. b. c. d. ANS: C 4. a. b. c. d. ANS: D 5. The mathematical equation relating the independent variable to the expected value of the dependent variable; that is, E(y) = β0 + β1x, is known as a. regression equation b. correlation equation c. estimated regression equation d. regression model ANS: A 6. a. b. c. d. ANS: C 7. a. b. c. d. In regression analysis, the unbiased estimate of the variance is coefficient of correlation coefficient of determination mean square error slope of the regression equation The model developed from sample data that has the form of is known as regression equation correlation equation estimated

Moreover, once SMB and HML are defined, the corresponding coefficients bs and bv are determined by linear regressions and can take negative values as well as positive values. The signs of the coefficients suggested that small cap and value portfolios have higher expected returns and arguably higher expected risk than those of large cap and growth portfolios.

I need help to create a multiple regression anlysis for this problem. Please provide as much explanation as you can. Please see attached files.

Cheng-Few Lee, John C. Lee, Alice C. Lee. (2013). Statistics for Business and Financial Economics. Retrieved from

Even though the CAPM still is relevant, several empirical tests have figured out a flatter SML than expected as well as a contrary return-beta relationship. Researchers have tried to explain this anomaly. One is the betting against beta factor mentioned by Frazzini and Pedersen and should be discussed in this essay.

3)Accountatns at the firm walker and walker believed that several travleing executives submit unusualy high travel voucherswhen they return from trips. The accounts. took a smaple of 200 vouchs sumbited last year the then developed the following mulitiple regression equation relating expected travel cost (Y) to number days on the road (X1) and distance traveled (X2) in miles:

McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for Business and Economics (11th ed.). Boston, MA: Prentice Hall.

This provided support for the CAPM and the approach for beta as the only predictor for differences in expected return (Haugen, 1999, p.238-239).

This is also reported by two recent studies that a dissention is a function of firm characteristics (Jiang et al., 2016; Ma and Khanna, 2015). It is also a strong assumption in general because it is very difficult to find and control all possible confounding variables.

We define uncertainty as the possibility of asset price variable coefficients being zero ( φ = 0, β = 0).

There are quite number of methods of estimating regression functions, the generally used ones being the ordinary least squares (OLS) and the maximum likelihood (ML). This paper will use (OLS) over (ML) because of the properties of (OLS) that is its ability to produce best linear unbiased estimate thus