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ECON321 Lecture 17: 321w11p2.pdf

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blueraven607
5 Dec 2014
School
University of Waterloo
Department
Economics
Course
ECON321
Professor
Kathleen Rybczynski

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Document Summary

Suppose that we wish to estimate the following regression. If we know the function h(x), then we can use. Weighted least squares (wls) to account for heteroskedasticity. Suppose i so in this case our function h(x) = x. And the standard deviation of our error is xi. If we divide our entire equation by xi : Then the variance of our (new) error is constant. ui. You can use the same method for any h(x). This method is called weighted least squares because we are weighting our residuals and minimizing the weighted sum of squared residuals. n. Our estimators will be different from the ols estimators of the original regression. But we interpret the coefficient estimates and tests statistics in the same manner as with ols. If we incorrectly choose the function h(x), then our resulting test statistics will not be valid. If we have no theoretical foundation for h(x) we can use feasible gls to estimate.

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Related Questions

The data for this assignment comes from household surveys carried out on a small island in Eastern Indonisa. The island, Selayar Island, is located in South Sulawesi, has two main industries: fishing and agriculture. We will be focusing of fishing activities occurring nearshore. Nearshore fishing takes place over coral reef, seagrass, and mangrove habitat. The most common fishing gear include spear guns, pole and lines, and nets. The boats used for fishing range from small canoes to small boats. The data was collected using a randomn sampling technique, and includes 152 fishing households.

Each fishing household was allowed to describe up to 6 different types of fishing trips. Researchers collected information on the ‘averge trip’. A subset of the data has been provided, in a .csv file named: ‘PS3_NearshoreFishing.csv’.

Description of variables:

HouseholdID: Unique identification number used to protect the identity of respondents

Poor: Indicates poor and non-poor households, as defined by the Indonesian National Poverty Line ($0.83 per capita per day).

Poor = 1 if household income is less than the INPL

= 0 if household income is more than the INPL

Quantity: The quantity of average harvest during one trip, in 10,000 Rp (currency)

For example, if average trip harvest is 2 kg of Grouper, and Grouper price is 20,000 Rp/kg, then Quantity of the trip is 40

Hours: The number of person hours spent fishing per trip, in hours.

For example, if average trip includes 3 fishermen, and each fishermen spends 3 hours fishing, then Hours of the trip is 9 person hours.

Capital: The value of capital used up during the average trip, in 10,000 Rp.

You can think of this like depreciation of all gear and boats used during a trip

Part 1:

(Model 1)

log(Quantityi)= b0 + b1log(Capitali) + b2log(Hoursi) + ei

1. Estimate the basic production model and report regression results. Include coefficients, standard errors, and t-statics. (Note: You will have to create new variables for estimation)

2. Based on your results, what is the marginal reurns to hours spent fishing? Be careful to interpret coefficient on log-transformed variables correctly.

3. Does the estimated production function have constant returns to scale, decreasing returns to scale, or increasing returns to scale? How do you know?

4. Using single hypothesis tests, which coefficients are significantly different than zero at a 5% significiance level?

5. Use White’s Test to test for heteroskedasticity. Report results and evaluate the results.

Help with the following problems that deal with the program stata and with econometrics.

Below is the data needed.

HouseholdID Poor Quantity Hours Capital
125465 0 25.4 8 0.796489877
130552 0 28.6 6 1.174603175
130552 0 9.1 4 2.222222222
127524 0 5 1 0.36544481
130538 1 10 6 2.008032129
130558 1 18.75 28 14.17848124
130462 1 14.4 2 0.487684729
126466 0 14.1 6 4.619435291
127474 1 13 3 0.367063492
127498 1 6 3 0.568124297
127471 0 12 5 0.115421137
127493 1 2.1 8 0.123444444
130561 0 39.6 3 0.289115646
130562 0 8 6 1.554278416
127532 1 41.7 6 0.738153469
127532 1 62.9 6 4.832562392
127495 0 7.65 3.5 0.116631206
127495 0 11.6 4 0.148772937
130524 1 2.95 10 0.706882255
130524 1 2.95 10 0.706882255
130564 0 14 14 1.495345632
127505 0 21.6 4 1.252053872
135460 1 21.5 4 0.757665094
131482 0 42.5 10 1.959625576
130560 0 15.3 4 0.697751323
128510 0 6 3 0.002311604
128510 0 4 2.5 0.416666667
128480 1 3.9 10 1.237449199
128480 1 4.4 10 1.237449199
128480 1 3.5 6 0.722079449
132456 0 8.5 5 0.177372685
132456 0 16.25 3 0.106423611
135486 0 21.5 11 3.665322581
135486 0 52.5 6 0.836021505
134524 0 33 7 1.242139722
135488 0 19.8 5 1.217105263
134460 0 84.4 3 3.824833703
131479 0 4 4.5 0.061111111
131465 0 8.5 1.5 0.39040198
128491 0 3.8 6 2.53815261
141514 1 1.4 6 0.946825397
134484 0 151.2 7 0.552428601
134484 0 71.2 6 0.479205754
141459 0 6.7 4 1.955605159
128473 0 70.7 5.5 0.553802416
128475 0 14.5 4 0.807217474
135520 0 2.5 4 1.041666667
135512 0 13.1 1 5.561594203
135512 0 3.3 2 0.650164062
134513 0 16.4 1 3.680053548
135469 1 18 13 1.979166667
132520 1 5.0625 2 0.395197395
134523 1 32.2 7 0.684749501
134497 0 10.76 5 3.18869165
129478 0 45.5 5.5 2.511574074
141461 0 25.6 2.5 4.849261849
141461 0 36.6 2.5 0.599912165
134503 0 30.6 8 1.214055483
128506 0 13.8 4 9.222222222
128506 0 19 4 9.222222222
130563 0 183.5 4 27.46449975
129488 0 215.1 1134 168.6111111
128487 0 4.5 1 0.000559284
129461 0 244 360 35.33333333
128496 0 1150 504 475.7380952
173468 0 2455 3366 1729.189322
134499 0 199 4 1.204772678
129466 0 4.5 3 2.773530545
145508 1 15 4 0.01460387
145508 1 15 4 0.01460387
134488 0 175 7 2.463414634
129484 0 275.1 2592 201.2685185
129484 0 175.1 1134 167.9891975
141467 0 16.5 2.5 0.270061728
141457 0 8.1 3 0.12021137
141457 0 24.1 4 0.499612792
135475 0 4.75 11 0.229402703
129498 0 616 830 362.7
134522 0 86.9 3 1.51324121
128477 0 9.4 5 1.626364087
141468 0 91.7 0.5 0.983115468
135489 1 18 8 3.206349206
134510 0 644.2 210 175.3837719
128457 0 3.5 2 0.509259259
134462 0 84.6 6 2.877641939
141509 1 23.2 2 2.640595716
129463 0 331 384 47.38180577
135514 1 18.6 2 8.338907469
135514 1 36.6 1 2.719893807
129485 0 4 7 0.833333333
129485 0 71.5 80 5.716746662
132529 1 7.8 3 1.024350649
132529 1 3.5 2 1.794733045
132529 1 1.5 3 0.861688312
145478 1 10.1 6 0.125
139510 1 12.5 10 6.041666667
134470 0 112 6 2.621527778
134496 0 279.7 4 4.799723021
134495 0 69 6 0.693735987
141460 0 60 2 1.272727273
135509 0 132.6 5 1.686077644
135506 0 10.5 3 0.295375197
134514 0 147.55 4 0.369502642
134514 0 147.55 4 0.369502642
134474 0 234.8 6 1.269918699
133503 0 6 3 1.095290252
133503 0 5.6 3 0.111111111
128460 0 15.5 5 0.306275676
128460 0 11.5 4 0.312922038
129501 0 500.5 483 74.36528749
129479 1 91 85 10.75104488
123490 1 15.75 5 1.35
123463 0 35.5 6 0.5
125465 0 18.5 6 0.568024685
127468 0 5.5 3.5 0.111111111
130552 0 13.1 2 1.166666667
130514 0 6.5 4 0.680555556
127524 0 14.5 5 2.532393505
130558 1 28 32 14.18542569
130516 0 3.5 4 0.208333333
130462 1 6.8 3 0.674055829
126466 0 12.6 2 4.592549518
127485 0 4.2 12 2.191666667
127485 0 10.2 6 1.186111111
130457 0 15 8 7.291666667
127474 1 10.5 3 0.724206349
127498 1 4.25 3 0.053412073
127498 1 3.5 3 0.053412073
127471 0 17.1 8 4.690142913
127471 0 12.3 4 2.311659648
127471 0 2.5 2 1.666666667
127493 1 2.1 8 0.123444444
130561 0 38.7 3 0.289115646
130561 0 16.5 3 11.33333333
127478 0 119.5 10 3.857877016
127478 0 30.5 6 2.170875421
127532 1 42.7 6 0.622768853
127532 1 38.7 6 6.971699297
127467 0 4.5 4 0.499600497
127467 0 9.1 3.5 0.366892361
127495 0 3.1 3.5 0.110797872
127495 0 9.9 3 0.114859845
130524 1 2.45 10 0.706882255
130524 1 2.45 10 0.706882255
130564 0 3814 3332 854.4818928
127505 0 14.5 3.5 2.491380471
135460 1 10 4 0.757665094
135460 1 20 14 0.484052111
131482 0 23.6 5 0.360957057
131482 0 67.9 6 0.416712483
130560 0 14 3 0.922110297
128510 0 504.6 1440 228.8873478
128510 0 6.3 2.5 0.416666667
128480 1 3.5 8 0.928789147
128480 1 49 70 33.6678911
132456 0 14.5 5 0.474991733
132456 0 8.75 5 0.177372685
132531 0 1.75 7 0.333333333
134524 0 33 7 1.116770991
134524 0 17 8 1.318727966
132513 0 1.5625 5 0.083333333
133494 0 2.875 2.5 0.5
135488 0 19.8 5 1.217105263
134460 0 40.8 3 3.824833703
128509 0 4216 2880 975.4285714
131479 0 2.35 4.5 0.42333225
131465 0 73 11.5 3.398356695
128491 0 6.4 3 1.871485944
141514 1 7.75 6 0.946825397
134484 0 91.7 7 0.962685011
134484 0 141.2 6 0.479205754
141459 0 12.2 4 1.955605159
128473 0 31.8 11 5.15297619
128481 0 852 810 49.09259259
128475 0 15.4 4 0.807217474
140461 1 4.7 5 1.183343855
128476 0 47 14 1.406476684
144544 0 21.2 2 0.866714015
135496 0 157.5 5 2.321399233
135496 0 59.8 5 2.003217415
134513 0 15.4 1 3.680053548
134523 1 28 40 3.667270345
144562 0 18.5 3 3.87254902
144562 0 11.4 3 2.086834734
134497 0 16.4 4 2.740795529
141461 0 34.1 2.5 4.849261849
141461 0 91.1 10 2.327184892
134503 0 29.6 8 1.214055483
128506 0 1271 1152 801
135497 0 0.75 1 0.221153846
141495 0 29 5 1.555555556
130563 0 252.5 8 36.62834591
141505 0 0.8 3 0.488855699
141505 0 0.8 3 0.513748468
133497 0 10 3 0.011904762
129488 0 210.5 1134 168.6111111
129461 0 722 720 70.66666667
128496 0 1150 504 475.7380952
173468 0 2882.5 1600 786.019471
145508 1 18.5 5 0.018254838
145508 1 16.65 5 0.018254838
134488 0 35 7 2.463414634
129484 0 371 2430 188.7268519
141467 0 25.65 2 1.158035714
141467 0 12.15 6.5 2.02808642
141457 0 19.8 3 0.167764213
141457 0 14.1 2 0.267971028
135475 0 5.75 10.5 0.22358875
129498 0 1068 830 362.7
134522 0 42.4 4 1.889213987
128477 0 7.6 6 1.919229497
144504 0 20.4 6 1.648461358
141468 0 35.65 0.5 0.983115468
135489 1 17.8 8 3.206349206
134510 0 384.7 90 75.16447368
128457 0 10 2 0.509259259
134462 0 114.6 7 2.918652438
141509 1 7.9 2 5.150635877
131485 0 6.75 8 2.973157134
131485 0 6.5 9 3.271105631
129463 0 1977.4 1848 224.9850657
135514 1 121.2 10 7.021349862
129485 0 1815 1296 80.22182224
132529 1 2.35 2.5 2.223124098
132529 1 7.6 2 1.875901876
132529 1 8.5 7 1.577705628
135502 0 31.2 4 0.320555556
132508 0 3.125 2.5 0.083333333
133479 0 26.7 2 4.126984127
134470 0 96 6 0.520833333
134496 0 114.8 7 2.416368587
134495 0 43 9 0.63703001
141460 0 23 2 1.272727273
132498 0 8 2 5.555555556
129497 0 2206 2880 955.3703704
129497 0 9.1 1.5 3.888888889
135509 0 2.6 5 0.942994199
135506 0 23.75 3 0.318339994
134514 0 37.55 7 0.596927834
134514 0 37.55 5 0.445311039
134474 0 114.75 6 1.269918699
133503 0 9 4 1.460387003
133503 0 6 3 0.111111111
128460 0 13.5 5 0.306275676
144502 0 6 3 0.644824673
129501 0 1280.2 672 123.9083139
129479 1 112 833 100.6137228
129511 0 231 60 0.02