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Need Econ or bus major writer, write 1000 words based on my data.This is one economic report. I already finished the data, now need at least 1000 words to description of your methods and results, and a short conclusion summarizing my findings.The writing requirement is post in the attached file. The assignment questions are also post, please check.Use the data i did and finish the report. my_data.docx assignment_.pdf questions.pdf Unformatted Attachment Preview IBM MARKET 0.15 0.1 0.05 0 -0.05 -0.1 -0.15 -0.2 0.15 0.1 0.05 0 -0.05 -0.1 -0.15 -0.2 -0.25 -0.3 40 60 80 100 120 140 40 60 80 100 120 140 100 120 140 Scatter Plot, IBM vs. MARKET 0.15 0.1 0.05 0 -0.05 -0.1 -0.15 -0.2 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 CONED MARKET 0.2 0.15 0.1 0.05 0 -0.05 -0.1 -0.15 0.15 0.1 0.05 0 -0.05 -0.1 -0.15 -0.2 -0.25 -0.3 40 60 80 100 120 140 40 60 80 Scatter Plot, CONED vs. MARKET 0.2 0.15 0.1 0.05 0 -0.05 -0.1 -0.15 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 IBM and Market *********************************************************************** TSM4.50.21-09-18 Run 1 at 16:55:06 on 3-12-2019 Data file is U:\BEEM011\S 2\newbeta.xls ----------------------------------------------------------------------Dependent Variable is IBM 120 observations (25-144) used for estimation. Estimation Method: Ordinary Least Squares Estimate Std. Err. t Ratio p-Value Intercept 0.00328 0.0047 MARKET 0.45302 0.06767 0.697 0.487 6.695 0 Residual Sum of Squares = 0.3005 R-Squared = 0.2752 R-Bar-Squared = 0.2691 Residual SD = 0.0505 Box-Pierce (residuals): Q(12) = 8.1698 {0.772} Box-Pierce (squared residuals): Q(12) = 7.4625 {0.826} Covariance matrix from standard formula. ...Run completed in 0.03 • Confidence interval -0.011636 *********************************************************************** Q4: *********************************************************************** TSM4.50.21-09-18 Run 3 at 17:22:37 on 3-12-2019 Data file is U:\BEEM011\S 2\newbeta.xls ----------------------------------------------------------------------Dependent Variable is IBM 60 observations (25-84) used for estimation with 60 ex-post forecasts. Estimation Method: Ordinary Least Squares Estimate Std. Err. t Ratio p-Value Intercept 0.00531 0.00693 0.767 0.446 MARKET 0.3278 0.08899 3.684 0.001 Residual Sum of Squares = 0.1569 R-Squared = 0.1896 R-Bar-Squared = 0.1756 Residual SD = 0.052 Box-Pierce (residuals): Q(12) = 9.0935 {0.695} Box-Pierce (squared residuals): Q(12) = 2.6293 {0.998} Chow Forecast Test: ChiSq(60) = 58.2463 {0.54} Chow Stability Test: ChiSq(2) = 5.73 {0.057} Covariance matrix from standard formula. ...Run completed in 0.02 *********************************************************************** Q5: *********************************************************************** • Dependent Variable is CONED 60 observations (25-84, dates 1978, Mth 1 to 1982, Mth 12) used for estimation with 60 ex-post forecasts 1983, Mth 1 to 1987, Mth 12. Estimation Method: Ordinary Least Squares Estimate Std. Err. t Ratio p-Value Intercept 0.0381 0.01521 2.505 0.015 MARKET 0.1218 0.08871 D\%1 FRBIND -0.00425 0.00649 -0.654 0.516 D\%1 CPI 1.373 0.175 -0.03031 0.01844 -1.644 0.106 D\%1 RPOIL 0.00011 0.0016 0.067 0.947 Residual Sum of Squares = 0.1465 R-Squared = 0.1033 R-Bar-Squared = 0.038 Residual SD = 0.0516 Box-Pierce (residuals): Q(12) = 15.5716 {0.212} Box-Pierce (squared residuals): Q(12) = 11.0569 {0.524} Chow Forecast Test: ChiSq(60) = 59.7109 {0.486} Chow Stability Test: ChiSq(5) = 3.2755 {0.658} Covariance matrix from standard formula. • Wald Test of Zero Restrictions on: D\%1 FRBIND D\%1 CPI D\%1 RPOIL F(3,55) = 1.3445 {0.269} Covariance matrix from standard formula. Consolidated Edison and Market *********************************************************************** TSM4.50.21-09-18 Run 2 at 16:56:05 on 3-12-2019 Data file is U:\BEEM011\S 2\newbeta.xls ----------------------------------------------------------------------Dependent Variable is CONED 120 observations (25-144) used for estimation. Estimation Method: Ordinary Least Squares Estimate Std. Err. t Ratio p-Value Intercept 0.01723 0.00467 3.691 0 MARKET 0.09102 0.06719 1.355 0.178 Residual Sum of Squares = 0.2961 R-Squared = 0.0153 R-Bar-Squared = 0.007 Residual SD = 0.0501 Box-Pierce (residuals): Q(12) = 13.2386 {0.352} Box-Pierce (squared residuals): Q(12) = 10.0715 {0.61} Covariance matrix from standard formula. ...Run completed in 0.03 • Confidence interval -0.00924786 *********************************************************************** Q4: *********************************************************************** TSM4.50.21-09-18 Run 4 at 17:24:43 on 3-12-2019 Data file is U:\BEEM011\S 2\newbeta.xls ----------------------------------------------------------------------Dependent Variable is CONED 60 observations (25-84) used for estimation with 60 ex-post forecasts. Estimation Method: Ordinary Least Squares Estimate Std. Err. t Ratio p-Value Intercept 0.0153 0.00694 MARKET 0.13391 0.08908 2.205 0.031 Residual Sum of Squares = 0.1572 R-Squared = 0.0375 1.503 0.138 R-Bar-Squared = 0.0209 Residual SD = 0.0521 Box-Pierce (residuals): Q(12) = 14.6955 {0.259} Box-Pierce (squared residuals): Q(12) = 9.2552 {0.681} Chow Forecast Test: ChiSq(60) = 53.4991 {0.711} Chow Stability Test: ChiSq(2) = 0.6685 {0.716} Covariance matrix from standard formula. ...Run completed in 0.02 *********************************************************************** Q5: *********************************************************************** • Dependent Variable is IBM 60 observations (25-84, dates 1978, Mth 1 to 1982, Mth 12) used for estimation with 60 ex-post forecasts 1983, Mth 1 to 1987, Mth 12. Estimation Method: Ordinary Least Squares Estimate Std. Err. t Ratio p-Value Intercept 0.05064 0.01371 MARKET 0.3087 0.07998 D\%1 FRBIND -0.00202 0.00585 -0.345 0.732 D\%1 CPI 3.693 0.001 3.86 0 -0.05798 0.01662 -3.488 0.001 D\%1 RPOIL -0.00093 0.00144 -0.643 0.523 Residual Sum of Squares = 0.119 R-Squared = 0.3851 R-Bar-Squared = 0.3404 Residual SD = 0.0465 Box-Pierce (residuals): Q(12) = 8.9716 {0.705} Box-Pierce (squared residuals): Q(12) = 5.6456 {0.933} Chow Forecast Test: ChiSq(60) = 121.46 {0} Chow Stability Test: ChiSq(5) = 18.0195 {0.003} Covariance matrix from standard formula. • Wald Test of Zero Restrictions on: D\%1 FRBIND D\%1 CPI D\%1 RPOIL F(3,55) = 5.8295 {0.002} Covariance matrix from standard formula. Q5: Assignment Submission Guidelines The following guidelines apply to both assignments you submit for this course. 1. Reports should be created as Word documents using a minimum font size of 11 and line spacing of 1.5 or 2.0. You need to include an assignment cover sheet as the first page of your document. You can download this from the assignment section on the ELE page. Your reports will need to be submitted electronically through the Exeter Learning Environment (ELE). Please do not include your name or student ID number anywhere in the document. 2. The deadline for submission of all your written work is 13th January 2020, which is the Monday following the exam week. This is the final deadline; assessed work will not be accepted after this date. You would be very well advised to complete your assignments well in advance of it – say, your first assignment at the end of November, and the second assignment before you leave for the Christmas break. Then you will be free to concentrate all your efforts on preparing for the examination. 3. Your report should include a short preamble describing the nature of the investigation (enough to show that you understand what’s going on), a description of your methods and results, and a short conclusion summarising your findings. Use a maximum of 1500 words, excluding appendices with the estimation output (see next item). However, please note that just to hand in computer outputs is not sufficient and will receive a failing mark. There must be explanation, interpretation and commentary in your own words. Significant marks are awarded for the quality of your written presentation. 4. Estimation results can be cut-and-pasted directly from your saved TSM outputs, as described in the ‘Tips’ section in Exercise 1. However, be selective! Only report results that are going to be commented on in the text of your report. Applying a bold-face font to the parts of the output that you want to draw attention to is a good idea. However, trying to ‘pad’ the report by including irrelevant results will lose marks. Don’t list the data, and don’t include plots unless they are to be referred to somewhere in the text. 5. Last, but not least: your reports must be your own work, and in your own words. Put simply, this means that although you are very welcome to discuss your exercises with classmates, the writing-up stage must be an individual effort. Turnitin, a very powerful text comparison system, is used by the university to detect plagiarism and copying. If the same text appears in different submissions more frequently than can be accounted for by chance, both pieces of work may be penalised. This could even mean a mark of zero. Don’t let it happen to you! BEEM011 University of Exeter 2019/20 BEEM011 Applied Econometrics Exercise 2 Exercise 2: Estimating and Testing the Capital Asset Pricing Model (CAPM) Before you Begin: In this exercise, we use monthly data to estimate the ‘betas’ of stocks (equities) traded on the New York Stock Exchange. We study equations of the form 𝑟𝑗𝑡 = 𝛼𝑗 + 𝛽𝑗 𝑟𝑚𝑡 + 𝑢𝑗𝑡 (1) where 𝑟𝑗𝑡 represents the actual return to holding company j’s stock in month t,1 𝑟𝑚𝑡 is the return on the market portfolio in month t (i.e., the portfolio consisting of all stocks, held in the same proportion as in the market as a whole) and 𝑢𝑗𝑡 represents other influences on returns. The strict form of the capital asset pricing model (CAPM) predicts that this equation fully ‘explains’ stock returns. Specifically, this means that 𝑢𝑗𝑡 depends only on random effects particular to company j, and is not predictable by macroeconomic variables. According to the CAPM, when markets operate efficiently in response to complete information, market equilibrium implies that 𝑟𝑚𝑡 contains all such information, relevant to individual stock returns. This assumption can be tested econometrically. The parameter 𝛽𝑗 (the ‘beta’) is an indicator of the risk and return associated with the stock. When 𝛽𝑗 = 1, the expected net return is the same as that of the market portfolio. When 𝛽𝑗 > 1 , the expected return exceeds that of the market portfolio, but there is correspondingly greater risk. When 𝛽𝑗 < 1 there is lower return, but also less risk. Thus, market equilibrium ensures the existence of a risk-return trade-off. The model also predicts that 𝛼𝑗 = 0. The Data Copy the file QM_Ex2.tsm from the ELE page to your U:\ drive. Double-click the file to open TSM and create the data file beta.xls. This file contains series of monthly returns on the stocks of 21 American companies, as follows: Industry Company Variable Name Period Oil Mobil Texaco IBM Digital Equipment Co. Data General MOBIL TEXACO IBM DEC DATGEN Computers 1 Jan78 - Dec 87 Jan 78 - Dec 87 Jan 78 - Dec 87 Jan 78 - Dec 87 Jan 78 - Dec 87 Consider the exercise of buying a unit of stock today, and selling it again after one month. The pay-out from one months holding of a unit of stock is d + p1 – p0 where p0 and p1 are this periods and next periods price, respectively, and d is the dividend per unit. The monthly rate of return is accordingly r = (d + p1 – p0)/p0. The variables p1 and d are uncertain ex ante, and r is treated as a random variable in the analysis of the agent’s decision. 2019/20 University of Exeter 1/4 BEEM011 Applied Econometrics Exercise 2 Electricity Consolidated Edison CONED Public Service of PSNH New Hampshire General Public Utilities GPU Forestry Weyerhauser WEYER Boise BOISE Electronics Motorola MOTOR Tandy TANDY Airlines Pan American PANAM Delta DELTA Banks Continental Illinois CONTIL Citicorp CITCRP Food Gerber GERBER General Mills GENMIL Chemicals Dow DOW DuPont DUPONT Conoco CONOCO (Note: Conoco was taken over by DuPont in September 1981.) The data file also contains the following variables: MARKET a measure of the market return RKFREE return on 30 day U.S. Treasury bills GOLD \% change in the gold price CPI U.S. consumer price index POIL price of domestic crude oil FRBIND index of U.S. industrial production Jan 78 - Dec 87 Jan 78 - Dec 87 Jan 76 - Dec 85 Jan 78 - Dec 87 Jan 78 - Dec 87 Jan 76 - Dec 85 Jan 78 - Dec 87 Jan 78 - Dec 87 Jan 78 - Dec 87 Jan 78 - Dec 87 Jan 78 - Dec 87 Jan 78 - Dec 87 Jan 78 - Dec 87 Jan 76 - Dec 85 Jan 76 - Dec 85 Jan 76 - Sep 81 Jan 76 – Dec 87 Jan 76 - Dec 87 Jan 76 - Dec 85 Dec 77 - Dec 87 Dec 77 Dec 87 Dec 77 - Dec 88 The Exercises Choose some stocks to analyse. Look at as many cases as you wish, but you must write up your analysis for two stocks, from different industries. It is also interesting to analyse the return on gold. The measure of 𝑟𝑚𝑡 is MARKET. It is recommended that you divide the sample into two 5-year periods, and choose one of the periods for your analysis. Over a longer time span, a companys beta might change significantly (you can test this). 1. Plot your data set. Check how the returns behave in interesting periods, such as the market crash of October 1987. The scatter plot of 𝑟𝑗𝑡 against 𝑟𝑚𝑡 may also be of interest. 2. Use ordinary least squares to estimate  and . Calculate the approximate 95\% confidence intervals for the parameters, and test the null hypotheses,  = 0, and also 𝛽 = 1 against the alternative hypothesis 𝛽 > 1. Interpret the result of the latter test. 2019/20 University of Exeter 2/4 BEEM011 Applied Econometrics Exercise 2 3. The standard deviation of the OLS residuals measures the individual risk of the stock, and the 𝑅 2 of the regression measures the proportion of the risk attributable to the market (as opposed to individual factors). Comment on the values you observe. 4. Use the Chow stability test to check whether the model is stable over the full 10-year period of the sample. In the Options / Forecasting… dialog, make sure that the Forecast Type is set to ExPost 1 Step. Then select the first 5 years as the sample for estimation, and choose the maximum forecast period. The Chow statistic is computed automatically when you run the regression. 5. Test the strict CAPM against the alternative of the Arbitrage Pricing Model (APM) by testing the ability of macroeconomic variables to predict returns. Use the rate of inflation (RINF), the growth in industrial production (GIND), and changes in the real oil price (ROIL) in a variable addition test. If the test finds significant effects, re-estimate the equation and report how much the value of beta changes as a result of including these variables. The variables you require for this test can be generated from the data series CPI, FRBIND and POIL in the data set. Do the following: a. Go to Setup / Data Transformation and Editing, or press . b. Click the Formula… button. Type RPOIL = POIL/CPI in the text field, and click <<< Go >>>. (This creates the real oil price). c. Now click Transform, and select the option \%-Difference. Highlight CPI, FRBIND and RPOIL and click Go. (This creates the percentage growth rates.) d. (Optional) Click Edit, select Rename, highlight D\%1-CPI, D\%1-FRBIND, and D\%1-RPOIL, and click Go. Supply the names RINF, GIND and ROIL, respectively. (These are the names used in Berndt’s book – see ‘Further Reading’, below.) e. Click “Save Modified Data”. Please satisfy yourself that your new variables RINF, GIND and ROIL contain the percentage monthly changes in CPI, FRBIND and POIL/CPI respectively. Add these variables to the regression. Note their individual significance by considering the t statistics. Also test their joint significance, using an F test. Do as follows: a. In the linear regression dialog, check Wald Test of Constraints, and press the square button next to the checkbox to open the Constraints dialog. b. Make sure that the radio button Zero Restrictions is selected and check the F- Stat. checkbox. c. Press Values, and in the column headed Wald Test, check the boxes of each variable to be tested. d. Run the regression. The test statistic appears following the usual results. 6. The last suggested exercise is a little unorthodox, and is optional, but it could help to illuminate the important idea of “expectation”. Recall the CAPM equation 𝐸(𝑟 )−𝑟𝑓𝑡 𝛽𝑗 = 𝐸(𝑟 𝑗𝑡 )−𝑟 𝑚𝑡 2019/20 𝑓𝑡 University of Exeter (2) 3/4 BEEM011 Applied Econometrics Exercise 2 where 𝑟𝑓𝑡 denotes the risk-free rate, This equation describes the subjective expectations of market traders in equilibrium at date t, which are of course unobservable. However, we might expect that the average values of 𝐸(𝑟𝑚𝑡 ) and 𝐸(𝑟𝑗𝑡 ) over time might be approximated by the sample average realized returns over time, which we do observe. While 𝑟𝑓𝑡 is not constant (although varying much less than the equity returns) we might also expect that the time average of this variable (measured by RKFREE) would measure the average risk-free rate. An informal check on the CAPM might in this case be provided by calculating the ratio on the right-hand side of equation (2) with sample means replacing theoretical values, and comparing this with the OLS estimate of 𝛽𝑗 for the same time period. To get the sample means, go to Setup / Compute Summary Statistics, highlight the variables in the list, select the required sample period, and click Go. Comment on what you find. Further Reading: See for example Keith Cuthbertson, Quantitative Financial Economics, Chapter 2.3, for the full derivation of the CAPM. Additional guidance and theoretical background can be found in Chapter 2 of E. R. Berndt, The Practice of Econometrics, Classic and Contemporary, from which source the data are taken. Please be sure to read the document Project Submission Guidelines, posted on the course ELE page under the Assessments/Assignments section. If you don’t follow the guidelines, you may not get the best grade for your work, and you could be penalized! 2019/20 University of Exeter 4/4 ... Purchase answer to see full attachment