ECON2271 Business Econometrics Student Activities for week 3 To do list for week 3 1 Make sure you re OK with the lecture material for Topic 1 and Topic 2 i use the suggested textbook references to read up further a necessary 2 Complete the pre lab exercises next page which relate to Topic 2 i 3 Attend the week 3 lecture 4 Make sure you ve completed the pre lab exercises and take an extra look at the last question 5 Attend your scheduled lab in the trading room 6 Attempt Quiz 2 on LMS AFTER your lab session The Quiz covers material from the lab as well as the lecture material from Topics 2 i and 2 ii The Quiz can be found in the Assessments area on LMS and will be open from 3pm on Wednesday until 11 59pm on Friday Here is a list of learning outcomes for the first two weeks for your reference Topic 2 i Univariate Regression For the general econometric model Y b0 b1X u Students able to distinguish between model variables and parameters Students able to interpret meaning of model parameters and error term Students able to test simple hypotheses comparing means using dummy regression Students able to explain the basic premise of OLS Students able to evaluate goodness of fit using R squared and F stat Students able to estimate simple univariate regression model and interpret key regression output correctly Topic 2 ii Univariate Regression For the general econometric model Y b0 b1X u Understand meaning of OLS estimates being BLUE Understand the key factors which may prevent OLS estimates being blue specifically Understand the assumptions about Y and its relationship with X Understand key assumptions about disturbances e or u Recognise some common nonlinear functional forms and know how to specify a function of Y which is linear in the model parameters i e a linearized function Able to interpret model estimates for such linearized functions Able to apply techniques required to identify correct functional form and linearized model specification 1 Lab Preparation Exercises 1 I want to find out whether men and women s physical health is different In order to do so I estimate a very simple dummy variable model with physical health PH and gender a dummy variable where 1 female and 0 male The output and most basic summary statistics for the variables are provided below except I have blanked out some of the vital information Complete the table and interpret the results What do you think you can conclude from this R Square Model Total 0 00281 T female 2 353768 0 3551892 6 6268 T cons 76 0 2589333 293 8888 95 CI female 2 353768 1 96xSE 3 04994 1 657597 cons 76 09761 1 96SE 75 5901 76 60512 2 2 Some people tell me people get happier as they get older but my economist friend thinks this is rubbish In order to figure out who is right if any I get some data on life satisfaction LS and age for a large sample of Australians I then estimate the model L Si b0 b1 AG Ei ui Stata provides these estimates reg ls age Source SS df MS Number of obs 17 503 F 1 17501 20 14 Model 42 7214775 1 42 7214775 Prob F 0 0000 Residual 37118 385 17 501 2 12092937 R squared 0 0011 Adj R squared 0 0011 Total 37161 1064 17 502 2 12324914 Root MSE 1 4563 ls Coef Std Err t P t 95 Conf Interval age 002595 0005782 4 49 0 000 0014617 0037284 cons 7 793649 0280274 278 07 0 000 7 738713 7 848586 a What Stata command will produce the results you see Reg LS AGE b How can you interpret these results c Generate a prediction for the life satisfaction of someone aged 15 40 and 70 d Using the Excel spreadsheet provided generate predicted and residual life satisfaction for all individuals in the sample using the estimated model parameters See if you can generate the SSE model SS and SSR residual SS for these estimates and thereby verify the R squared statistic 3 3 I talk to my psychologist friend about my life satisfaction age model She laughs and tells me I should estimate my model twice once for people below age 40 and once for people 40 and above When I get back to my office Stata provides the output below How can you make sense of this What do you think my psychologist friend wanted me to discover reg ls age if age 40 Source SS df MS Number of obs 7 658 F 1 7656 108 10 Model 198 97326 1 198 97326 Prob F 0 0000 Residual 14091 9351 7 656 1 84063938 R squared 0 0139 Adj R squared 0 0138 Total 14290 9083 7 657 1 86638479 Root MSE 1 3567 ls Coef Std Err t P t 95 Conf Interval age 0231913 0022305 10 40 0 000 0275638 0188188 cons 8 555457 0613436 139 47 0 000 8 435206 8 675707 reg ls age if age 40 Source SS df MS Number of obs 9 561 F 1 9559 279 21 Model 629 127809 1 629 127809 Prob F 0 0000 Residual 21539 1157 9 559 2 25328127 R squared 0 0284 Adj R squared 0 0283 Total 22168 2435 9 560 2 31885392 Root MSE 1 5011 ls Coef Std Err t P t 95 Conf Interval age 0206362 001235 16 71 0 000 0182153 0230571 cons 6 676103 0745929 89 50 0 000 6 529885 6 822321 4 Lab exercises to be completed in pairs in the labs Student pairs are recommended to share a computer when doing these exercises Note This time I have only provided this as a Word file which means you can print it out as it is or create spaces for you to enter hand written notes or type and paste Stata output tables straight into the document You need to make sure you save all your output somehow For example you can select output form your results window in Stata right click and select copy as picture Then you can paste the relevant parts into this document so you have a complete report of your lab activities 1 Pair up with another student not the same as last week Open the Stata file entitled Week3lab Make sure you can see the variable descriptions in the variables window and familiarise yourselves with these 2 First take a moment to compare your answers to Q1 of the pre lab exercises Confirm your answers by running the command reg ph female you can also estimate this model by navigating the menu like you would have done in the first lab check that you re able to do this Source SS df MS Number of obs 15 584 F 1 15582 43 91 Model 21499 3579 1 21499 3579 Prob F 0 0000 Residual 7628530 8 15 582 489 573277 R squared 0 0028 Adj R squared 0 0027 Total 7650030 16 15 583 490 921527 Root MSE 22 126 ph Coef Std Err …