Rutgers University Econometric Excel Worksheet need help in anwsering econametric homework on excel , questions are on satisisical regression line and dist

Rutgers University Econometric Excel Worksheet need help in anwsering econametric homework on excel , questions are on satisisical regression line and distripution You have access to data for a large cross section of U.S. companies observed in 2018. We wish
to explain/predict the value of a company’s Tobin’s Q, denoted by . Throughout this exercise,
you may interpret as a measure for the value of a firm. We also observe the of a
company, measured as the ratio of total debt to total assets. Further, we have observations on the
total value of a firm’s assets measured in thousand US$, denoted by . View this variable
as an indicator for the size of a company. Use the data to complete the following set of exercises.
i.
Estimate the model
= 0 + 1 + 2 ln( ) +
ii.
iii.
iv.
v.
vi.
vii.
and report the results in the usual form, including the sample size and the R-squared. Are
the signs of the slope coefficients what you expected? Explain.
What do you make of the intercept you estimated in part i.? In particular, does it make
sense to set the two explanatory variables to zero? [Hint: Recall that ln(1) = 0].
Now run the simple regression of on , and compare the slope coefficient with the
estimate obtained in part i. Is the estimated effect of debt now larger or smaller than in
part i.?
Find the correlation between ln ( ) and . Does its sign make sense to you?
Use part iv. to explain your findings in part iii. [Hint: This question refers to slide 37 of
the lecture slides for Chapter 3.]
Show that you could have obtained the estimated slope coefficient in the regression in
part iii. directly from the results in part i., without estimating the (smaller) regression
model by OLS (use the formula provided on the slides, involving ̂ ).
In the original multiple regression model:
= 0 + 1 + 2 ln( ) +
find the OLS estimate of the effect of ln ( ) on by using the “partialling out”
technique (see lecture slides). Show that the resulting OLS estimate is the same as the one
obtained in part i.
Cross-sectional observations on 1451 companies in the year 2018
(Retrieved from the Compustat database)
Variables:
1
2
3
4
5
gvkey
q
debt
assets
prop
unique company identification code
Tobins’s Q = (market value of equity – book value of equity + book value of assets) / book value of asse
Ratio of book value of total debt to book value of assets
Total assets (measured in thousand US$)
Ratio of property, plants and equipment to book value of assets
Dataset:
gvkey
1045
1076
1440
1920
2818
3158
3505
3735
4498
5047
7637
7648
7882
7922
7923
8214
8215
8358
8543
8606
9850
10840
13342
16478
16890
19517
19518
20020
21238
21816
23084
23225
23810
24617
24783
q
1,246933
1,376546
1,259245
1,523903
1,097415
1,09596
0,954459
1,922475
2,359666
1,112901
1,060052
1,3021
1,059327
1,816791
1,682397
1,05453
1,195643
1,964541
2,397835
1,145672
1,17689
2,128329
1,728967
1,919997
1,046718
1,3916
0,992981
0,758544
7,039256
1,732431
0,865098
2,693997
1,271179
3,073384
2,435815
debt
0,56172
0,150265
0,371289
0,529435
0
0,311135
0,351168
0,203634
0
0,355703
0,422127
0,407808
0,176608
0,340477
0,315268
0,346638
0,550675
0,323564
0,462741
0,546737
0,400713
0
0,048023
0,179265
0,01941
0,289831
0,110214
0,375607
0
0,76886
0,081099
0,504804
0,497412
0,012693
0,141527
assets
60580000
2826692
68802800
3010000
497906
1134002
30109800
47832500
329534
3,09E+08
32734900
7761000
915854
7886000
36239000
9771000
9699000
15320087
55638000
5972903
1,17E+08
1843386
3105065
33934474
3353281
5468642
1547900
1001677
221068
2929057
1899783
2303234
244749
3545283
1959421
prop
0,713915
0,542246
0,800826
0,189435
0,189401
0,065548
0,15305
0,0525
0,322115
0,164168
0,066296
0,046257
0,345353
0,49721
0,857943
0,390032
0,318074
0,121164
0,034832
0,09848
0,691081
0,303559
0,017614
0,827345
0,011564
0,783323
0,136766
0,657802
0,228129
0,169235
0,111041
0,368169
0,353771
0,777073
0,022087
25167
26157
28195
29156
29734
30260
133367
144437
148391
148470
150279
155394
158743
174390
176592
178798
1078
1449
1661
1706
2820
2942
3028
3504
4060
4462
4990
5252
5862
6268
6831
6867
7154
7437
7993
8247
10407
10443
10567
10795
11856
11925
12252
14311
14563
14824
21431
1,346146
1,115297
1,241993
1,307818
1,303551
2,546721
1,190643
1,323965
1,062931
0,957082
1,024825
1,238721
1,32656
3,177321
2,103165
0,749192
2,435992
1,07798
0,749192
1,835236
1,744192
4,380377
2,629863
2,537962
1,142392
3,426778
1,753301
5,183039
1,729565
1,229753
0,749192
1,301438
5,342186
1,342733
1,303285
2,216489
2,490005
1,03697
3,110928
1,281411
1,026577
0,749192
2,429116
1,921723
1,05025
1,217657
1,166247
0,639861
0,758197
0,209096
0,139386
0,450531
0,002745
0,328055
0
0
0,241219
0,148295
0,378333
0,535512
0
0,41063
0,354948
0,291278
0,041152
0,456643
0,359604
0,048558
0,204732
0,512578
0,330912
0,2152
0,454257
0,155175
0,188407
0,34976
0,225321
0,197386
0,301755
0,947094
0,287706
0,294158
0,313206
0,343872
0,41513
0,836805
0,328809
0,12799
0,418503
0,352418
0,475784
0,542061
0,03948
0,720034
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1021663
3953300
225905
277148
843941
9725769
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3054799
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858053
1084647
67173000
1,4E+08
7853944
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1,88E+08
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1472741
2780666
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199989
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2,26E+08
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2005
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14256
3,72329
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1,857121
1,509916
2,6189
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4,2383
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0
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0
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