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Practical Econometrics Data collection Analysis and Application 1st Edition Hilmer Solutions Manual

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Practical Econometrics Data collection Analysis and Application 1st Edition Hilmer Solutions Manual Practical Econometrics Data collection Analysis and Application 1st Edition Hilmer Solutions Manual Practical Econometrics Data collection Analysis and Application 1st Edition Hilmer Solutions Manual

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April 9, 2023
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PRACTICAL ECONOMETRI CS DATA COLLECTION ANAL YSIS AND APPLICATION 1ST EDITION HILMER SOLUTIONS MAN UAL Chapter 02 - Collecti on and Management of Data CHAPTER 2 Answers to E nd of Chapter Problems 2.1 Cross S ection – Answers vary by student – for example a sample of 200 firm’s p rofits in 2013, a sample of 50 state’s popul ation, etc. Time Se ries – Answers vary by student – for example labor force participation f rom 1980 to 2010, produ ctivity monthly from 1994 to 2013. Panel Data – Answers vary by student – for example high school and beyond that surv eys students which s amples the s ame indiv iduals from when they are in high school and th en every two years for 20 years. 2.2 a. Ans wers vary by student – for example, to find out how m any hours of exercise students at your campus get. b. Stand on campus and ask every third individu al, use su rvey monk ey or a facebook survey c. The email sur vey to friends will suf fer from s election bias as their responses will likely be similar to yours while surveying people at a local grocery store will still suffer from selection bias but not by the same d egree d. If the survey is biased in so me mann er then the analysis that is b ased on the su rvey will also be bi ased (not truly representative of the population). 2.3 D ata management te chniques are important be cause simple mistak es with data can lead to hours upon hours of work to reconstru ct a data set. The goal of data management is to” (1) reduce the chances of overwriting existing data, which would require us to spend unwanted hours re-creating our data from the beginning and (2) provide clear intuition as to our process, thereby enabling us to quick ly get back up to speed if we return to our project after a long hiatu s. We recommend saving one mast er file so that if we make a mistake and overwrite or otherwise change our data, we can easily go back and re constru ct our correct data without having to start at square one with our int ernet search, data do wnloadi ng, and so o n. We recommend including a worksheet identi fying all data sources so that if we make a mistake and overwrite or otherwise change our data, we can more easily go back and reconstru ct our exact data set without having to re-perform internet searches, which can at later dates provide different results and make our reconstru cting the data nearly impossible. 2-1

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