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Midterm 2: Econometrics Name: Answers to each question need to be clearly written to get full credit. An answer with important information missing such as the null and alternative hypothesis, p-value etc will not be given full credit. 1. Economagic.com has many time series data, one of which is the monthly jewelry sales in million of dollars. The data ranges from January 1993 to December 2004. Use this data to answer the following questions: a. From the graph, is there evidence of trend and seasonality? If there is seasonality, which month has the largest sale? 0 1000 2000 3000 4000 5000 6000 7000 1994 1996 1998 2000 2002 2004 JEWELERY Clearly, there is an upward trend and sales skyrocketed every December. b. Based on your answers to part a, create seasonal dummy variable(s) and trend. Detrend, de-seasonalize the data and present the original data, detrended data, detrended and deseasoanlized data in a graph. (Hint: do not create the dummy variables for each month only for the month that has the largest sales in each year.) Have detrending and deseasonalizing made the data smoother? We create a dummy variable for December: genr dec = @seas(12)

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-2000 -1000 0 1000 2000 3000 4000 5000 6000 7000 1994 1996 1998 2000 2002 2004 JEWELERY DETREND DETREND_DESEA Clearly, detrending and deseasonalizing have made the sales smoother as shown in the graph. The standard deviation from the detrended and deseasonalized data is the smallest. 2. “WAGE2.XLS” is from M. Blackburn and D. Neumark (1992), “Unobserved Ability, Efficiency Wages, and Interindustry Wage Differentials,” Quarterly Journal of Economics 107, 1421-1436. It has the following variables: Obs: 935 1. wage monthly earnings 2. hours average weekly hours 3. IQ IQ score 4. KWW knowledge of world work score 5. educ years of education 6. exper years of work experience 7. tenure years with current employer 8. age age in years 9. married =1 if married 10. black =1 if black 11. south =1 if live in south 12. urban =1 if live in SMSA 13. pareduc parent’s education a. Consider the standard linear wage equation: wage = 0 β + 1 educ + 2 exper + 3 tenure + u State and test the null hypothesis that another of general work experience has the same effect on wage as another year of tenure with the current employer. The estimation output: Dependent Variable: WAGE Method: Least Squares Sample: 1 935
Included observations: 935 Variable Coefficient Std. Error t-Statistic Prob. C

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