Econ 1133 – Applied Econometrics MSc Business and Financial Economics University of Greenwich Business School Portfolio Tasks (70% weight) Do all tasks Due: 13 April 2017 Task 1: Determinants of wage earnings (35/100) You are given the dataset Wldrg_earnings2, which contains 935 cross-sectional observations on wage earning and a range of factors that are known to be related to an individual’s earning potential. Wldrg_earnings2 935 observations ————————————————————————— —- storage display value variable name type format label variable label ————————————————————————— —- wage int %9.0g monthly earnings hours byte %9.0g average weekly hours iq int %9.0g IQ score kww byte %9.0g knowledge of world work score educ byte %9.0g years of education exper byte %9.0g years of work experience tenure byte %9.0g years with current employer age byte %9.0g age in years married byte %9.0g =1 if married black byte %9.0g =1 if black south byte %9.0g =1 if live in south urban byte %9.0g =1 if live in SMSA sibs byte %9.0g number of siblings brthord byte %9.0g birth order meduc byte %9.0g mother’s education feduc byte %9.0g father’s education lwage float %9.0g log(wage) ————————————————————————— —- Wooldridge data sets: http://fmwww.bc.edu/ec-p/data/wooldridge/datasets.list.html Drawing on the relevant theoretical and empirical literature, specify and estimate 3 different models for determinants of wage earnings. Please address the following tasks: a. Justify your models drawing on relevant theoretical and empirical literature b. Report post-estimation statistics and discuss the strengths and weaknesses of each model c. Identify your preferred model and compare your findings with theoretical predictions and empirical findings d. Conclude Two relevant work to kick off with: Andersson, R., Nabavi, P., & Wilhelmsson, M. (2014). The impact of advanced vocational education and training on earnings in Sweden. International Journal of Training and Development, 18(4), 256-270. Available here Mincer, J. (1975). Education, experience, and the distribution of earnings and employment: an overview. In Education, income, and human behavior (pp. 71-94). NBER. Available here Task 2: Effects of job training grants on scrap (faulty product) rates (35/100) You are given the dataset Wldrg_Jtrain, which contains 471 panel-data observations over 3 years (1987, 1988, 1989). The dataset provides information on whether the firm receives a training grant from government; and on a range of firm characteristics. Obs: 471 1. year 1987, 1988, or 1989 2. fcode firm code number 3. employ # employees at plant 4. sales annual sales, $ 5. avgsal average employee salary 6. scrap scrap rate (per 100 items) 7. rework rework rate (per 100 items) 8. tothrs total hours training 9. union =1 if unionized 10. grant =1 if received grant 11. d89 =1 if year = 1989 12. d88 =1 if year = 1988 13. totrain total employees trained 14. hrsemp tothrs/totrain 15. lscrap log(scrap) 16. lemploy log(employ) 17. lsales log(sales) 18. lrework log(rework) 19. lhrsemp log(1 + hrsemp) 20. lscrap_1 lagged lscrap; missing 1987 21. grant_1 lagged grant; assumed 0 in 1987 22. clscrap lscrap – lscrap_1; year > 1987 23. cgrant grant – grant_1 24. clemploy lemploy – lemploy[t-1] 25. clsales lavgsal – lavgsal[t-1] 26. lavgsal log(avgsal) 27. clavgsal lavgsal – lavgsal[t-1] 28. cgrant_1 cgrant[t-1] 29. chrsemp hrsemp – hrsemp[t-1] 30. clhrsemp lhrsemp – lhrsemp[t-1] Drawing on the relevant theoretical and empirical literature: a. Specify a model for the effect of training grant receipt on scrap rates. Justify your model drawing on relevant empirical and theoretical work. b. Use 3 different panel-data estimators and discuss the strengths and weaknesses of each estimator c. Identify your preferred estimator and compare your findings with theoretical predictions and empirical findings d. Conclude Two relevant work to kick off with: Holzer, H. J., Block, R. N., Cheatham, M., & Knott, J. H. (1993). Are training subsidies for firms effective? The Michigan experience. Industrial & Labor Relations Review, 46(4), 625-636. Available here here Bartel, A. P. (2000). Measuring the employer’s return on investments in training: Evidence from the literature. Industrial relations, 39(3), 502-524. Available here Task 3: Testing for weak-form market efficiency and volatility (30/100) You are given the dataset Wldrg_NYSE, which contains 691 weekly time-series observations on New York Stock Exchange (NYSE) stock price and returns. Obs: 691 1. price NYSE stock price index 2. return 100*(p – p(-1])/p(-1)) 3. return_1 lagged return 4. t time trend: 1 to 691 5. price_1 price(-1) 6. price_2 price(-2) 7. cprice price – price_1 8. cprice_1 cprice(-1) Drawing on the relevant theoretical and empirical literature: a. Test for weak-form market efficiency, using four methods: serial correlation, runs, variance ratio, and unit root. b. Comment on strengths and weaknesses of the efficient market hypothesis and the method used c. Estimate return volatility using different ARCH and GARCH models. d. Comment on strengths and weaknesses of the theory and the method used e. Conclude Two relevant work to kick off with: Degutis, A., & Novickyte, L. (2014). The efficient market hypothesis: A critical review of literature and methodology. Ekonomika, 93(2), 7. Available here Pilbeam, K., & Langeland, K. N. (2015). Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts. International Economics and Economic Policy, 12(1), 127-142. Available here NOTE on producing regression output tables Use esttab to produce output tables where necessary. If not installed install esttab, typing: ssc install esttab Estimate models in sequence After each estimation, type: estimates store [model name, eg. Model1] When estimated all models (could be 1 or many), type: esttab model1 model2 etc using [file name.rtf], b(3) se(3) star(* 0.10 ** 0.05 *** 0.01) /// scalars (N df_m ll aic bic) varwidth(15) compress replace The output will be stored as rtf file in the directory you are working in. If you want to store in a different directory, specify full path before file name.
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