econ sse
4th Statistical computing assignment
1) Use the data in CPS78_85. Thinking of this estimated regression:
a. How do you interpret the coefficient on ? Does it have an interesting interpretation?
b. Holding other factors fixed, what is the estimated percent increase in nominal wage for a male with 12 years of education? Propose a regression to obtain a confidence interval for this estimate.
c. Re-estimate the above regression, but let all wages be measured in 1978 dollars. In particular, define the real wage as for 1978 and as for 1985. Now, use in place of and estimate the model. Which coefficients differ from the original estimation?
d. Explain why the from your regression in part (c) is not the same as the original estimation?
e. Describe how union participation changed from 1978 to 1985.
f. Starting with the original estimation, test whether the union wage differential changed over time.
g. Do your findings in parts (e) and (f) conflict? Explain
2) Use the data in KIELMC
a. The variable is the distance from each home to the incinerator site, in feet. Consider the model
If building the incinerator reduces the value of homes closer to the site, what is the sign of ? What does it mean in ?
b. Estimate the model in part (a) and report the results in the usual form. Interpret the coefficient on . What do you conclude?
c. Add and to the equation. Now, what do you conclude about the effect of the incinerator on housing values?
d. Why is the coefficient on both positive and statistically significant in part (b), but not in part (c)? What does this say about the controls you used in part (c)?
3) Use CRIME3 data for this exercise
a. In the model (from example 13.6)
Test the hypothesis
b. If , show that the differenced equation can be written as
Where and is the average clear up percentage over the previous two years.
c. Estimate the equation in part (b). Compare the adjusted R-squared with that in equation 13.22 in our text (. Which model would you finally use?
4) Use the data in RENTAL for this exercise. The data on rental prices and other variables for college towns are for the years 1980 and 1990. The idea is to see whether a stronger presence of students affects rental rates. The unobserved effects model is
Where is city population, is average income, and is student population as a percentage of city population.
a. Estimate the equation by pooled OLS and report the results in the standard form. What do you make of the estimate on the 1990 dummy variable? What do you get for ?
b. Are the standard errors you report in (a) valid? Explain
c. Now, difference the equation and estimate by OLS. Compare your estimate of with that from part (a). Does the relative size of the student population appear to affect rental prices?
d. Estimate the model by FE to verify that you get identical estimates and standard errors as those in part (c).
5) In example 13.8 in our text, we used the unemployment claims data from Papke (1994) to estimate the effect of enterprise zones on unemployment claims. Papke also uses a model that allows each city to have its own time trend:
Where and are both unobserved effects. This allows for more heterogeneity across cities.
a. Show that, when the previous equation is first differenced, we obtain
Notice that the differenced equation contains a fixed effect, .
b. Estimate the differenced equation by fixed effects. What is the estimate of ? Is it very different from the estimate obtained in Example 13.8? Is the effect of the enterprise zones still statistically significant?
c. Add a full set of year dummies to the estimation in part (b). What happens to the estimate of ?
6) Please see example 14.4 in our text.
a. In the wage equation in Example 14.4, explain why dummy variables for occupation might be important omitted variables for estimating the union wage premium.
b. If every man in the sample stayed in the same occupation from 1981 through 1987, would you need to include the occupation dummies in a fixed effects estimation? Explain.
c. Using the data in WAGEPAN include 8 of the occupation dummy variables in the equation and estimate the equation using fixed effects. Does the coefficient on union change by much? What about its statistical significance?
7) Add the interaction term to the equation estimated in Table 14.2 to see if wage growth depends on union status. Estimate the equation by random and fixed effects and compare the results. Data Sets- STATA/401K.DTA
Data Sets- STATA/401ksubs.dta
Data Sets- STATA/ADMNREV.DTA
Data Sets- STATA/affairs.dta
Data Sets- STATA/airfare.dta
Data Sets- STATA/alcohol.dta
Data Sets- STATA/APPLE.DTA
Data Sets- STATA/approval.dta
Data Sets- STATA/ATHLET1.DTA
Data Sets- STATA/ATHLET2.DTA
Data Sets- STATA/attend.dta
Data Sets- STATA/AUDIT.DTA
Data Sets- STATA/BARIUM.DTA
Data Sets- STATA/beauty.dta
Data Sets- STATA/benefits.dta
Data Sets- STATA/beveridge.dta
Data Sets- STATA/big9salary.dta
Data Sets- STATA/BWGHT.DTA
Data Sets- STATA/bwght2.dta
Data Sets- STATA/campus.dta
Data Sets- STATA/CARD.DTA
Data Sets- STATA/catholic.dta
Data Sets- STATA/cement.dta
Data Sets- STATA/census2000.dta
Data Sets- STATA/CEOSAL1.DTA
Data Sets- STATA/CEOSAL2.DTA
Data Sets- STATA/charity.dta
Data Sets- STATA/consump.dta
Data Sets- STATA/CORN.DTA
Data Sets- STATA/countymurders.dta
Data Sets- STATA/CPS78_85.DTA
Data Sets- STATA/cps91.dta
Data Sets- STATA/CRIME1.DTA
Data Sets- STATA/CRIME2.DTA
Data Sets- STATA/CRIME3.DTA
Data Sets- STATA/CRIME4.DTA
Data Sets- STATA/discrim.dta
Data Sets- STATA/driving.dta
Data Sets- STATA/EARNS.DTA
Data Sets- STATA/econmath.dta
Data Sets- STATA/elem94_95.dta
Data Sets- STATA/engin.dta
Data Sets- STATA/expendshares.dta
Data Sets- STATA/EZANDERS.DTA
Data Sets- STATA/ezunem.dta
Data Sets- STATA/FAIR.DTA
Data Sets- STATA/FERTIL1.DTA
Data Sets- STATA/FERTIL2.DTA
Data Sets- STATA/FERTIL3.DTA
Data Sets- STATA/FISH.DTA
Data Sets- STATA/FRINGE.DTA
Data Sets- STATA/GPA1.DTA
Data Sets- STATA/gpa2.dta
Data Sets- STATA/GPA3.DTA
Data Sets- STATA/happiness.dta
Data Sets- STATA/hprice1.dta
Data Sets- STATA/HPRICE2.DTA
Data Sets- STATA/HPRICE3.DTA
Data Sets- STATA/HSEINV.DTA
Data Sets- STATA/HTV.DTA
Data Sets- STATA/infmrt.dta
Data Sets- STATA/INJURY.DTA
Data Sets- STATA/intdef.dta
Data Sets- STATA/INTQRT.DTA
Data Sets- STATA/INVEN.DTA
Data Sets- STATA/JTRAIN.DTA
Data Sets- STATA/JTRAIN2.DTA
Data Sets- STATA/jtrain3.dta
Data Sets- STATA/jtrain98.dta
Data Sets- STATA/KIELMC.DTA
Data Sets- STATA/labsup.dta
Data Sets- STATA/LAWSCH85.DTA
Data Sets- STATA/loanapp.dta
Data Sets- STATA/LOWBRTH.DTA
Data Sets- STATA/mathpnl.dta
Data Sets- STATA/meap00_01.dta
Data Sets- STATA/meap01.dta
Data Sets- STATA/MEAP93.DTA
Data Sets- STATA/meapsingle.dta
Data Sets- STATA/minwage.dta
Data Sets- STATA/MLB1.DTA
Data Sets- STATA/MROZ.DTA
Data Sets- STATA/MURDER.DTA
Data Sets- STATA/nbasal.dta
Data Sets- STATA/ncaa_rpi.dta
Data Sets- STATA/NYSE.DTA
Data Sets- STATA/okun.dta
Data Sets- STATA/OPENNESS.DTA
Data Sets- STATA/PENSION.DTA
Data Sets- STATA/phillips.dta
Data Sets- STATA/PNTSPRD.DTA
Data Sets- STATA/PRISON.DTA
Data Sets- STATA/PRMINWGE.DTA
Data Sets- STATA/RDCHEM.DTA
Data Sets- STATA/rdtelec.dta
Data Sets- STATA/recid.dta
Data Sets- STATA/RENTAL.DTA
Data Sets- STATA/RETURN.DTA
Data Sets- STATA/SAVING.DTA
Data Sets- STATA/school93_98.dta
Data Sets- STATA/SLEEP75.DTA
Data Sets- STATA/SLP75_81.DTA
Data Sets- STATA/SMOKE.DTA
Data Sets- STATA/TRAFFIC1.DTA
Data Sets- STATA/TRAFFIC2.DTA
Data Sets- STATA/twoyear.dta
Data Sets- STATA/VOLAT.DTA
Data Sets- STATA/VOTE1.DTA
Data Sets- STATA/VOTE2.DTA
Data Sets- STATA/voucher.dta
Data Sets- STATA/WAGE1.DTA
Data Sets- STATA/WAGE2.DTA
Data Sets- STATA/wagepan.dta
Data Sets- STATA/WAGEPRC.DTA
Data Sets- STATA/wine.dta Data Sets- R/401k.RData
Data Sets- R/401k.RData
Data Sets- R/401ksubs.RData
Data Sets- R/401ksubs.RData
Data Sets- R/admnrev.RData
Data Sets- R/admnrev.RData
Data Sets- R/affairs.RData
Data Sets- R/affairs.RData
Data Sets- R/airfare.RData
Data Sets- R/airfare.RData
Data Sets- R/alcohol.RData
Data Sets- R/alcohol.RData
Data Sets- R/apple.RData
Data Sets- R/apple.RData
Data Sets- R/approval.r
id,month,year,sp500,cpi,cpifood,approve,gasprice,unemploy,katrina,rgasprice,lrgasprice,11-Sep,iraqinvade,lsp500,lcpifood
302,2,2001,1239.94,184.4,171.8,59.24,148.4,4.6,0,80.47723,4.387974,0,0,7.122818,5.146331
303,3,2001,1160.33,185.3,172.2,57.01,144.7,4.5,0,78.08958,4.357857,0,0,7.05646,5.148656
304,4,2001,1249.46,185.6,172.4,60.31,156.4,4.2,0,84.26724,4.433993,0,0,7.130467,5.149817
305,5,2001,1255.82,185.5,172.9,55.82,172.9,4.1,0,93.20755,4.534829,0,0,7.135544,5.152713
306,6,2001,1224.42,185.9,173.4,54.93,164,4.7,0,88.21947,4.479828,0,0,7.110222,5.155601
307,7,2001,1211.23,186.2,174,56.36,148.2,4.7,0,79.59184,4.376912,0,0,7.099391,5.159055
308,8,2001,1133.58,186.6,174.4,56.85,142.7,4.9,0,76.47374,4.336947,0,0,7.033136,5.161352
309,9,2001,1040.94,187.1,174.6,85.6,153.1,4.7,0,81.8279,4.404618,1,0,6.947879,5.162498
310,10,2001,1059.78,187.6,175.3,88.65,136.2,5,0,72.60128,4.284983,1,0,6.965816,5.166499
311,11,2001,1139.45,188.1,175.2,86.35,126.3,5.3,0,67.14513,4.206856,1,0,7.038301,5.165928
312,12,2001,1148.08,187.8,175.2,85.87,113.1,5.4,0,60.22364,4.098065,0,0,7.045846,5.165928
313,1,2002,1130.2,188.2,176.2,83,113.9,6.3,0,60.52072,4.102986,0,0,7.03015,5.17162
314,2,2002,1106.73,189.2,176.4,81.28,113,6.1,0,59.72516,4.089753,0,0,7.009165,5.172754
315,3,2002,1147.39,189.8,176.6,79.02,124.1,6.1,0,65.38461,4.180287,0,0,7.045245,5.173887
316,4,2002,1076.92,190.3,176.7,75.54,140.7,5.7,0,73.93589,4.303198,0,0,6.98186,5.174453
317,5,2002,1067.14,190.2,176.4,75.82,142.1,5.5,0,74.71083,4.313625,0,0,6.972737,5.172754
318,6,2002,989.81,190.1,176.4,73.62,140.4,6,0,73.85587,4.302115,0,0,6.897513,5.172754
319,7,2002,911.62,190.3,176.6,71.38,141.2,5.9,0,74.19863,4.306746,0,0,6.815223,5.173887
320,8,2002,916.07,191,176.6,66.37,142.3,5.7,0,74.50262,4.310834,0,0,6.820093,5.173887
321,9,2002,815.28,191.3,176.9,69.6,142.2,5.4,0,74.33351,4.308562,0,0,6.703532,5.175585
322,10,2002,885.76,191.8,177.1,63,144.9,5.3,0,75.54745,4.324761,0,0,6.786446,5.176714
323,11,2002,936.31,191.8,177.4,65.89,144.8,5.6,0,75.49531,4.32407,0,0,6.841947,5.178407
324,12,2002,879.82,191.4,177.8,62.93,139.4,5.7,0,72.83176,4.288152,0,0,6.779717,5.180659
325,1,2003,855.7,191.8,178.1,59.9,147.3,6.5,0,76.79875,4.341188,0,0,6.75192,5.182345
326,2,2003,841.15,192.5,178.9,59.09,164.1,6.4,0,85.24675,4.44555,0,0,6.73477,5.186827
327,3,2003,848.18,193,179.2,58.97,174.8,6.2,0,90.56995,4.506123,0,1,6.743093,5.188502
328,4,2003,916.92,193.1,179,70.3,165.9,5.8,0,85.91403,4.453347,0,1,6.82102,5.187386
329,5,2003,963.59,193.2,179.4,66.61,154.2,5.8,0,79.81367,4.379695,0,1,6.870666,5.189618
330,6,2003,974.5,193,180.2,62.43,151.4,6.5,0,78.44559,4.362405,0,0,6.881925,5.194067
331,7,2003,990.31,193.2,180.3,59.48,152.4,6.3,0,78.88199,4.367953,0,0,6.898018,5.194622
332,8,2003,1008.01,193.5,180.9,59.27,162.8,6,0,84.13437,4.432415,0,0,6.915733,5.197944
333,9,2003,995.97,193.6,181.3,51.48,172.8,5.8,0,89.2562,4.491511,0,0,6.903717,5.200153
334,10,2003,1050.71,194.3,182.2,55.23,160.3,5.6,0,82.50129,4.412814,0,0,6.957222,5.205105
335,11,2003,1058.2,193.9,182.9,50.66,153.5,5.6,0,79.16452,4.371528,0,0,6.964324,5.20894
336,12,2003,1111.92,193.6,184.1,61.17,149.4,5.4,0,77.16942,4.346003,0,0,7.013844,5.215479
337,1,2004,1131.13,194,184.3,53.06,159.2,6.3,0,82.06186,4.407474,0,0,7.030972,5.216565
338,2,2004,1144.94,194.9,184.5,51,167.2,6,0,85.78758,4.451874,0,0,7.043108,5.217649
339,3,2004,1126.21,196.1,184.9,50.72,176.6,6,0,90.05609,4.500432,0,0,7.026613,5.219815
340,4,2004,1107.3,196.5,185,51.93,183.3,5.4,0,93.28244,4.535632,0,0,7.00968,5.220356
341,5,2004,1120.68,196.5,186.5,46.69,200.9,5.3,0,102.2392,4.627315,0,0,7.021691,5.228431
342,6,2004,1140.84,196.6,186.8,48.6,204.1,5.8,0,103.8148,4.642609,0,0,7.03952,5.230039
343,7,2004,1101.72,196.6,187.2,48.09,193.9,5.7,0,98.62666,4.591341,0,0,7.004628,5.232178
344,8,2004,1104.24,196.8,187.3,50.34,189.8,5.4,0,96.44309,4.568953,0,0,7.006913,5.232712
345,9,2004,1114.58,197.4,187.2,52.19,189.1,5.1,0,95.79534,4.562214,0,0,7.016233,5.232178
346,10,2004,1130.2,198.2,188.4,49.15,202.9,5.1,0,102.3713,4.628607,0,0,7.03015,5.238567
347,11,2004,1173.82,198.1,188.6,52.87,201,5.2,0,101.4639,4.619703,0,0,7.068018,5.239628
348,12,2004,1211.92,197.8,188.9,52,188.2,5.1,0,95.14661,4.555419,0,0,7.099961,5.241218
349,1,2005,1181.27,198.4,189.5,51.43,182.3,5.7,0,91.88508,4.520539,0,0,7.074346,5.244389
350,2,2005,1203.6,199.5,189.3,50.51,191.8,5.8,0,96.14035,4.565809,0,0,7.093072,5.243333
351,3,2005,1180.59,200.7,189.6,51.52,206.5,5.4,0,102.8899,4.633659,0,0,7.07377,5.244916
352,4,2005,1156.85,200.9,190.7,48.81,228.3,4.9,0,113.6386,4.733024,0,0,7.053456,5.250701
353,5,2005,1191.5,200.8,191.1,47.92,221.6,4.9,0,110.3586,4.703735,0,0,7.082968,5.252797
354,6,2005,1191.33,200.6,190.9,46.95,217.6,5.2,0,108.4746,4.686516,0,0,7.082826,5.25175
355,7,2005,1234.18,200.8,191.3,48.72,231.6,5.2,0,115.3386,4.747872,0,0,7.118162,5.253843
356,8,2005,1220.33,201,191.3,43.67,250.6,4.9,0,124.6766,4.825723,0,0,7.106876,5.253843
357,9,2005,1228.81,201.3,191.8,43.33,292.7,4.8,1,145.4049,4.979522,0,0,7.113801,5.256453
358,10,2005,1207.01,202.3,192.5,39.63,278.5,4.6,0,137.6668,4.924837,0,0,7.095901,5.260096
359,11,2005,1249.48,202.3,192.8,37.64,234.3,4.8,0,115.8181,4.752021,0,0,7.130483,5.261653
360,12,2005,1248.29,202.1,193.2,41.6,218.6,4.6,0,108.1643,4.683651,0,0,7.12953,5.263726
361,1,2006,1280.08,202.6,194.5,43.02,231.5,5.1,0,114.2646,4.738516,0,0,7.154678,5.270432
362,2,2006,1280.66,203.6,194.4,39.15,231,5.1,0,113.4578,4.731431,0,0,7.155131,5.269918
363,3,2006,1294.83,204.9,194.5,36.8,240.1,4.8,0,117.1791,4.763704,0,0,7.166135,5.270432
364,4,2006,1310.61,205.5,194.2,35.83,275.7,4.5,0,134.1606,4.899037,0,0,7.178248,5.268888
365,5,2006,1270.09,205.7,194.7,33.57,294.7,4.4,0,143.2669,4.964709,0,0,7.146843,5.27146
366,6,2006,1270.2,205.9,195.1,37.72,291.7,4.8,0,141.6707,4.953506,0,0,7.14693,5.273512
367,7,2006,1276.66,206.2,195.6,38.81,299.9,5,0,145.4413,4.979773,0,0,7.152002,5.276072
368,8,2006,1303.82,206.7,196,40.3,298.5,4.6,0,144.4122,4.972672,0,0,7.173054,5.278115
369,9,2006,1335.85,207.2,196.7,42.97,258.9,4.4,0,124.9517,4.827928,0,0,7.197323,5.28168
370,10,2006,1377.94,207.8,197.5,37.08,227.2,4.1,0,109.3359,4.694425,0,0,7.228345,5.285738
371,11,2006,1400.63,207.6,197.2,34.2,224.1,4.3,0,107.948,4.681649,0,0,7.244678,5.284218
372,12,2006,1418.3,207.3,197.4,37.76,233.4,4.3,0,112.5904,4.723757,0,0,7.257214,5.285232
373,1,2007,1438.24,208.009,199.198,34.67,227.4,5,0,109.3222,4.694299,0,0,7.271175,5.294299
374,2,2007,1406.82,209.112,200.402,33.3,228.5,4.9,0,109.2716,4.693836,0,0,7.249087,5.300325
375,3,2007,1420.86,209.923,200.869,34.86,259.2,4.5,0,123.4738,4.81603,0,0,7.259017,5.302653
376,4,2007,1482.37,210.311,201.292,35.99,286,4.3,0,135.9891,4.912575,0,0,7.301397,5.304757
377,5,2007,1530.62,210.316,202.225,33.02,313,4.3,0,148.8237,5.002762,0,0,7.333428,5.309381
378,6,2007,1503.35,210.474,202.885,31.89,305.2,4.7,0,145.006,4.976775,0,0,7.315451,5.312639
379,7,2007,1455.27,210.756,203.533,30.94,296.1,4.9,0,140.4942,4.945166,0,0,7.282947,5.315828
Data Sets- R/athlet1.RData
Data Sets- R/athlet1.RData
Data Sets- R/athlet2.RData
Data Sets- R/athlet2.RData
Data Sets- R/attend.RData
Data Sets- R/attend.RData
Data Sets- R/audit.RData
Data Sets- R/audit.RData
Data Sets- R/barium.RData
Data Sets- R/barium.RData
Data Sets- R/beauty.RData
Data Sets- R/beauty.RData
Data Sets- R/benefits.RData
Data Sets- R/benefits.RData
Data Sets- R/beveridge.RData
Data Sets- R/beveridge.RData
Data Sets- R/big9salary.RData
Data Sets- R/big9salary.RData
Data Sets- R/bwght.RData
Data Sets- R/bwght.RData
Data Sets- R/bwght2.RData
Data Sets- R/bwght2.RData
Data Sets- R/campus.RData
Data Sets- R/campus.RData
Data Sets- R/card.RData
Data Sets- R/card.RData
Data Sets- R/catholic.r
id,read12,math12,female,asian,hispan,black,motheduc,fatheduc,lfaminc,hsgrad,cathhs,parcath
124902,61.41,49.77,0,0,0,0,14,12,10.30895,1,0,1
124915,58.34,59.84,0,0,0,0,14,14,10.30895,1,0,1
124916,59.33,50.38,1,0,0,0,14,11,10.30895,1,0,1
124932,49.59,45.03,1,0,0,0,12,14,10.30895,1,0,1
124944,57.62,54.26,1,0,0,0,12,12,10.65726,1,0,1
124947,52.53,56.73,1,0,0,0,12,11,11.04292,1,0,1
124966,60.12,55.37,1,0,0,0,14,14,9.433484,1,0,1
124968,64.69,65.07,0,0,0,0,14,14,10.30895,1,0,1
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