Добавил:
Upload Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз: Предмет: Файл:

Wooldridge_-_Introductory_Econometrics_2nd_Ed

.pdf
Скачиваний:
126
Добавлен:
21.03.2016
Размер:
4.36 Mб
Скачать

Appendix G Statistical Tables

TABLE G.1 (concluded )

z

0

1

2

3

4

5

6

7

8

9

0.6 0.2743 0.2709 0.2676 0.2643 0.2611 0.2578 0.2546 0.2514 0.2483 0.24510.5 0.3085 0.3050 0.3015 0.2981 0.2946 0.2912 0.2877 0.2843 0.2810 0.27760.4 0.3446 0.3409 0.3372 0.3336 0.3300 0.3264 0.3228 0.3192 0.3156 0.31210.3 0.3821 0.3783 0.3745 0.3707 0.3669 0.3632 0.3594 0.3557 0.3520 0.34830.2 0.4207 0.4168 0.4129 0.4090 0.4052 0.4013 0.3974 0.3936 0.3897 0.38590.1 0.4602 0.4562 0.4522 0.4483 0.4443 0.4404 0.4364 0.4325 0.4286 0.42470.0 0.5000 0.4960 0.4920 0.4880 0.4840 0.4801 0.4761 0.4721 0.4681 0.4641

0.00.5000 0.5040 0.5080 0.5120 0.5160 0.5199 0.5239 0.5279 0.5319 0.5359

0.10.5398 0.5438 0.5478 0.5517 0.5557 0.5596 0.5636 0.5675 0.5714 0.5753

0.20.5793 0.5832 0.5871 0.5910 0.5948 0.5987 0.6026 0.6064 0.6103 0.6141

0.30.6179 0.6217 0.6255 0.6293 0.6331 0.6368 0.6406 0.6443 0.6480 0.6517

0.40.6554 0.6591 0.6628 0.6664 0.6700 0.6736 0.6772 0.6808 0.6844 0.6879

0.50.6915 0.6950 0.6985 0.7019 0.7054 0.7088 0.7123 0.7157 0.7190 0.7224

0.60.7257 0.7291 0.7324 0.7357 0.7389 0.7422 0.7454 0.7486 0.7517 0.7549

0.70.7580 0.7611 0.7642 0.7673 0.7704 0.7734 0.7764 0.7794 0.7823 0.7852

0.80.7881 0.7910 0.7939 0.7967 0.7995 0.8023 0.8051 0.8078 0.8106 0.8133

0.90.8159 0.8186 0.8212 0.8238 0.8264 0.8289 0.8315 0.8340 0.8365 0.8389

1.00.8413 0.8438 0.8461 0.8485 0.8508 0.8531 0.8554 0.8577 0.8599 0.8621

1.10.8643 0.8665 0.8686 0.8708 0.8729 0.8749 0.8770 0.8790 0.8810 0.8830

1.20.8849 0.8869 0.8888 0.8907 0.8925 0.8944 0.8962 0.8980 0.8997 0.9015

1.30.9032 0.9049 0.9066 0.9082 0.9099 0.9115 0.9131 0.9147 0.9162 0.9177

1.40.9192 0.9207 0.9222 0.9236 0.9251 0.9265 0.9279 0.9292 0.9306 0.9319

1.50.9332 0.9345 0.9357 0.9370 0.9382 0.9394 0.9406 0.9418 0.9429 0.9441

1.60.9452 0.9463 0.9474 0.9484 0.9495 0.9505 0.9515 0.9525 0.9535 0.9545

1.70.9554 0.9564 0.9573 0.9582 0.9591 0.9599 0.9608 0.9616 0.9625 0.9633

1.80.9641 0.9649 0.9656 0.9664 0.9671 0.9678 0.9686 0.9693 0.9699 0.9706

1.90.9713 0.9719 0.9726 0.9732 0.9738 0.9744 0.9750 0.9756 0.9761 0.9767

2.00.9772 0.9778 0.9783 0.9788 0.9793 0.9798 0.9803 0.9808 0.9812 0.9817

2.10.9821 0.9826 0.9830 0.9834 0.9838 0.9842 0.9846 0.9850 0.9854 0.9857

2.20.9861 0.9864 0.9868 0.9871 0.9875 0.9878 0.9881 0.9884 0.9887 0.9890

2.30.9893 0.9896 0.9898 0.9901 0.9904 0.9906 0.9909 0.9911 0.9913 0.9916

2.40.9918 0.9920 0.9922 0.9925 0.9927 0.9929 0.9931 0.9932 0.9934 0.9936

2.50.9938 0.9940 0.9941 0.9943 0.9945 0.9946 0.9948 0.9949 0.9951 0.9952

2.60.9953 0.9955 0.9956 0.9957 0.9959 0.9960 0.9961 0.9962 0.9963 0.9964

2.70.9965 0.9966 0.9967 0.9968 0.9969 0.9970 0.9971 0.9972 0.9973 0.9974

2.80.9974 0.9975 0.9976 0.9977 0.9977 0.9978 0.9979 0.9979 0.9980 0.9981

2.90.9981 0.9982 0.9982 0.9983 0.9984 0.9984 0.9985 0.9985 0.9986 0.9986

3.00.9987 0.9987 0.9987 0.9988 0.9988 0.9989 0.9989 0.9989 0.9990 0.9990

Examples: If Z ~ Normal(0,1) then P(Z 1.32) .0934 and P(Z 1.84) .9671. Source: This table was generated using the Stata® function normd.

779

Appendix G

Statistical Tables

TABLE G.2

Critical Values of the t Distribution

Significance Level

1-Tailed:

.10

.05

.025

.01

.005

2-Tailed:

.20

.10

.050

.02

.010

 

 

 

 

 

 

 

 

1

3.078

6.314

12.706

31.821

63.657

 

2

1.886

2.920

4.303

6.965

9.925

 

3

1.638

2.353

3.182

4.541

5.841

 

4

1.533

2.132

2.776

3.747

4.604

 

5

1.476

2.015

2.571

3.365

4.032

 

 

 

 

 

 

 

 

6

1.440

1.943

2.447

3.143

3.707

D

7

1.415

1.895

2.365

2.998

3.499

8

1.397

1.860

2.306

2.896

3.355

e

9

1.383

1.833

2.262

2.821

3.250

g

10

1.372

1.812

2.228

2.764

3.169

r

 

 

 

 

 

 

11

1.363

1.796

2.201

2.718

3.106

e

12

1.356

1.782

2.179

2.681

3.055

e

13

1.350

1.771

2.160

2.650

3.012

s

14

1.345

1.761

2.145

2.624

2.977

 

o

15

1.341

1.753

2.131

2.602

2.947

 

 

 

 

 

 

 

 

 

 

 

 

f

16

1.337

1.746

2.120

2.583

2.921

 

17

1.333

1.740

2.110

2.567

2.898

F

18

1.330

1.734

2.101

2.552

2.878

r

19

1.328

1.729

2.093

2.539

2.861

20

1.325

1.725

2.086

2.528

2.845

e

 

 

 

 

 

 

e

21

1.323

1.721

2.080

2.518

2.831

d

22

1.321

1.717

2.074

2.508

2.819

o

23

1.319

1.714

2.069

2.500

2.807

m

24

1.318

1.711

2.064

2.492

2.797

 

25

1.316

1.708

2.060

2.485

2.787

 

 

 

 

 

 

 

 

26

1.315

1.706

2.056

2.479

2.779

 

27

1.314

1.703

2.052

2.473

2.771

 

28

1.313

1.701

2.048

2.467

2.763

 

29

1.311

1.699

2.045

2.462

2.756

 

30

1.310

1.697

2.042

2.457

2.750

 

 

 

 

 

 

 

 

40

1.303

1.684

2.021

2.423

2.704

 

60

1.296

1.671

2.000

2.390

2.660

 

90

1.291

1.662

1.987

2.368

2.632

 

120

1.289

1.658

1.980

2.358

2.617

 

 

1.282

1.645

1.960

2.326

2.576

 

 

 

 

 

 

 

Examples: The 1% critical value for a one-tailed test with 25 df is 2.485. The 5% critical for a two-tailed test with large ( 120) df is 1.96.

Source: This table was generated using the Stata® function invt.

780

Appendix G

Statistical Tables

TABLE G.3a

10% Critical Values of the F Distribution

Numerator Degrees of Freedom

 

 

1

2

3

4

5

6

7

8

9

10

 

 

 

 

 

 

 

 

 

 

 

 

 

10

3.29

2.92

2.73

2.61

2.52

2.46

2.41

2.38

2.35

2.32

D

11

3.23

2.86

2.66

2.54

2.45

2.39

2.34

2.30

2.27

2.25

12

3.18

2.81

2.61

2.48

2.39

2.33

2.28

2.24

2.21

2.19

e

n

13

3.14

2.76

2.56

2.43

2.35

2.28

2.23

2.20

2.16

2.14

o

14

3.10

2.73

2.52

2.39

2.31

2.24

2.19

2.15

2.12

2.10

m

 

 

 

 

 

 

 

 

 

 

 

i

15

3.07

2.70

2.49

2.36

2.27

2.21

2.16

2.12

2.09

2.06

n

16

3.05

2.67

2.46

2.33

2.24

2.18

2.13

2.09

2.06

2.03

a

17

3.03

2.64

2.44

2.31

2.22

2.15

2.10

2.06

2.03

2.00

t

o

18

3.01

2.62

2.42

2.29

2.20

2.13

2.08

2.04

2.00

1.98

r

19

2.99

2.61

2.40

2.27

2.18

2.11

2.06

2.02

1.98

1.96

 

D

 

 

 

 

 

 

 

 

 

 

 

20

2.97

2.59

2.38

2.25

2.16

2.09

2.04

2.00

1.96

1.94

e

21

2.96

2.57

2.36

2.23

2.14

2.08

2.02

1.98

1.95

1.92

g

22

2.95

2.56

2.35

2.22

2.13

2.06

2.01

1.97

1.93

1.90

r

e

23

2.94

2.55

2.34

2.21

2.11

2.05

1.99

1.95

1.92

1.89

e

24

2.93

2.54

2.33

2.19

2.10

2.04

1.98

1.94

1.91

1.88

s

 

 

 

 

 

 

 

 

 

 

 

o

25

2.92

2.53

2.32

2.18

2.09

2.02

1.97

1.93

1.89

1.87

26

2.91

2.52

2.31

2.17

2.08

2.01

1.96

1.92

1.88

1.86

f

27

2.90

2.51

2.30

2.17

2.07

2.00

1.95

1.91

1.87

1.85

F

28

2.89

2.50

2.29

2.16

2.06

2.00

1.94

1.90

1.87

1.84

r

29

2.89

2.50

2.28

2.15

2.06

1.99

1.93

1.89

1.86

1.83

e

 

 

 

 

 

 

 

 

 

 

 

e

30

2.88

2.49

2.28

2.14

2.05

1.98

1.93

1.88

1.85

1.82

d

40

2.84

2.44

2.23

2.09

2.00

1.93

1.87

1.83

1.79

1.76

o

60

2.79

2.39

2.18

2.04

1.95

1.87

1.82

1.77

1.74

1.71

m

 

90

2.76

2.36

2.15

2.01

1.91

1.84

1.78

1.74

1.70

1.67

 

120

2.75

2.35

2.13

1.99

1.90

1.82

1.77

1.72

1.68

1.65

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2.71

2.30

2.08

1.94

1.85

1.77

1.72

1.67

1.63

1.60

 

 

 

 

 

 

 

 

 

 

 

 

Example: The 10% critical value for numerator df 2 and denominator df 40 is 2.44. Source: This table was generated using the Stata® function invfprob.

781

Appendix G

Statistical Tables

TABLE G.3b

5% Critical Values of the F Distribution

Numerator Degrees of Freedom

 

 

1

2

3

4

5

6

7

8

9

10

 

 

 

 

 

 

 

 

 

 

 

 

 

10

4.96

4.10

3.71

3.48

3.33

3.22

3.14

3.07

3.02

2.98

D

11

4.84

3.98

3.59

3.36

3.20

3.09

3.01

2.95

2.90

2.85

12

4.75

3.89

3.49

3.26

3.11

3.00

2.91

2.85

2.80

2.75

e

13

4.67

3.81

3.41

3.18

3.03

2.92

2.83

2.77

2.71

2.67

n

o

14

4.60

3.74

3.34

3.11

2.96

2.85

2.76

2.70

2.65

2.60

m

 

 

 

 

 

 

 

 

 

 

 

i

15

4.54

3.68

3.29

3.06

2.90

2.79

2.71

2.64

2.59

2.54

n

16

4.49

3.63

3.24

3.01

2.85

2.74

2.66

2.59

2.54

2.49

a

17

4.45

3.59

3.20

2.96

2.81

2.70

2.61

2.55

2.49

2.45

t

18

4.41

3.55

3.16

2.93

2.77

2.66

2.58

2.51

2.46

2.41

o

r

19

4.38

3.52

3.13

2.90

2.74

2.63

2.54

2.48

2.42

2.38

D

 

 

 

 

 

 

 

 

 

 

 

20

4.35

3.49

3.10

2.87

2.71

2.60

2.51

2.45

2.39

2.35

e

21

4.32

3.47

3.07

2.84

2.68

2.57

2.49

2.42

2.37

2.32

g

22

4.30

3.44

3.05

2.82

2.66

2.55

2.46

2.40

2.34

2.30

r

23

4.28

3.42

3.03

2.80

2.64

2.53

2.44

2.37

2.32

2.27

e

e

24

4.26

3.40

3.01

2.78

2.62

2.51

2.42

2.36

2.30

2.25

s

 

 

 

 

 

 

 

 

 

 

 

25

4.24

3.39

2.99

2.76

2.60

2.49

2.40

2.34

2.28

2.24

o

26

4.23

3.37

2.98

2.74

2.59

2.47

2.39

2.32

2.27

2.22

f

27

4.21

3.35

2.96

2.73

2.57

2.46

2.37

2.31

2.25

2.20

 

F

28

4.20

3.34

2.95

2.71

2.56

2.45

2.36

2.29

2.24

2.19

r

29

4.18

3.33

2.93

2.70

2.55

2.43

2.35

2.28

2.22

2.18

e

 

 

 

 

 

 

 

 

 

 

 

e

30

4.17

3.32

2.92

2.69

2.53

2.42

2.33

2.27

2.21

2.16

d

40

4.08

3.23

2.84

2.61

2.45

2.34

2.25

2.18

2.12

2.08

o

60

4.00

3.15

2.76

2.53

2.37

2.25

2.17

2.10

2.04

1.99

m

90

3.95

3.10

2.71

2.47

2.32

2.20

2.11

2.04

1.99

1.94

 

 

120

3.92

3.07

2.68

2.45

2.29

2.17

2.09

2.02

1.96

1.91

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3.84

3.00

2.60

2.37

2.21

2.10

2.01

1.94

1.88

1.83

 

 

 

 

 

 

 

 

 

 

 

 

Example: The 5% critical value for numerator df 4 and large denominator df ( ) is 2.37. Source: This table was generated using the Stata® function invfprob.

782

Appendix G

Statistical Tables

TABLE G.3c

1% Critical Values of the F Distribution

Numerator Degrees of Freedom

 

 

1

2

3

4

5

6

7

8

9

10

 

 

 

 

 

 

 

 

 

 

 

 

 

10

10.04

7.56

6.55

5.99

5.64

5.39

5.20

5.06

4.94

4.85

D

11

9.65

7.21

6.22

5.67

5.32

5.07

4.89

4.74

4.63

4.54

12

9.33

6.93

5.95

5.41

5.06

4.82

4.64

4.50

4.39

4.30

e

13

9.07

6.70

5.74

5.21

4.86

4.62

4.44

4.30

4.19

4.10

n

14

8.86

6.51

5.56

5.04

4.69

4.46

4.28

4.14

4.03

3.94

o

m

 

 

 

 

 

 

 

 

 

 

 

15

8.68

6.36

5.42

4.89

4.56

4.32

4.14

4.00

3.89

3.80

i

16

8.53

6.23

5.29

4.77

4.44

4.20

4.03

3.89

3.78

3.69

n

a

17

8.40

6.11

5.18

4.67

4.34

4.10

3.93

3.79

3.68

3.59

t

18

8.29

6.01

5.09

4.58

4.25

4.01

3.84

3.71

3.60

3.51

o

19

8.18

5.93

5.01

4.50

4.17

3.94

3.77

3.63

3.52

3.43

r

 

 

 

 

 

 

 

 

 

 

 

 

D

20

8.10

5.85

4.94

4.43

4.10

3.87

3.70

3.56

3.46

3.37

21

8.02

5.78

4.87

4.37

4.04

3.81

3.64

3.51

3.40

3.31

e

g

22

7.95

5.72

4.82

4.31

3.99

3.76

3.59

3.45

3.35

3.26

r

23

7.88

5.66

4.76

4.26

3.94

3.71

3.54

3.41

3.30

3.21

e

24

7.82

5.61

4.72

4.22

3.90

3.67

3.50

3.36

3.26

3.17

e

s

 

 

 

 

 

 

 

 

 

 

 

25

7.77

5.57

4.68

4.18

3.85

3.63

3.46

3.32

3.22

3.13

 

o

26

7.72

5.53

4.64

4.14

3.82

3.59

3.42

3.29

3.18

3.09

f

27

7.68

5.49

4.60

4.11

3.78

3.56

3.39

3.26

3.15

3.06

F

28

7.64

5.45

4.57

4.07

3.75

3.53

3.36

3.23

3.12

3.03

29

7.60

5.42

4.54

4.04

3.73

3.50

3.33

3.20

3.09

3.00

r

e

 

 

 

 

 

 

 

 

 

 

 

30

7.56

5.39

4.51

4.02

3.70

3.47

3.30

3.17

3.07

2.98

e

40

7.31

5.18

4.31

3.83

3.51

3.29

3.12

2.99

2.89

2.80

d

o

60

7.08

4.98

4.13

3.65

3.34

3.12

2.95

2.82

2.72

2.63

m

90

6.93

4.85

4.01

3.54

3.23

3.01

2.84

2.72

2.61

2.52

 

 

120

6.85

4.79

3.95

3.48

3.17

2.96

2.79

2.66

2.56

2.47

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6.63

4.61

3.78

3.32

3.02

2.80

2.64

2.51

2.41

2.32

 

 

 

 

 

 

 

 

 

 

 

 

Example: The 1% critical value for numerator df 3 and denominator df 60 is 4.13. Source: This table was generated using the Stata® function invfprob.

783

Appendix G

Statistical Tables

TABLE G.4

Critical Values of the Chi-Square Distribution

 

 

 

Significance Level

 

 

 

 

 

 

 

 

.10

 

.05

.01

 

 

 

 

 

 

 

1

2.71

 

3.84

6.63

 

2

4.61

 

5.99

9.21

 

3

6.25

 

7.81

11.34

 

4

7.78

 

9.49

13.28

 

5

9.24

 

11.07

15.09

 

6

10.64

 

12.59

16.81

 

7

12.02

 

14.07

18.48

D

8

13.36

 

15.51

20.09

9

14.68

 

16.92

21.67

e

 

g

10

15.99

 

18.31

23.21

r

11

17.28

 

19.68

24.72

e

 

e

12

18.55

 

21.03

26.22

s

13

19.81

 

22.36

27.69

o

14

21.06

 

23.68

29.14

15

22.31

 

25.00

30.58

f

 

16

23.54

 

26.30

32.00

F

 

17

24.77

 

27.59

33.41

r

 

18

25.99

 

28.87

34.81

e

 

e

19

27.20

 

30.14

36.19

d

20

28.41

 

31.41

37.57

o

 

 

 

 

 

21

29.62

 

32.67

38.93

m

 

 

22

30.81

 

33.92

40.29

 

23

32.01

 

35.17

41.64

 

24

33.20

 

36.42

42.98

 

25

34.38

 

37.65

44.31

 

26

35.56

 

38.89

45.64

 

27

36.74

 

40.11

46.96

 

28

37.92

 

41.34

48.28

 

29

39.09

 

42.56

49.59

 

30

40.26

 

43.77

50.89

Example: The 5% critical value with df 8 is 15.51.

Source: This table was generated using the Stata® function invchi.

784

R E F E R E N C E S

Angrist, J. D. (1990), “Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records,” American Economic Review 80, 313–336.

Angrist, J. D., and A. B. Krueger (1991), “Does Compulsory School Attendance Affect Schooling and Earnings?” Quarterly Journal of Economics 106, 979–1014.

Ashenfelter, O., and A. B. Krueger (1994), “Estimates of the Economic Return to Schooling from a New Sample of Twins,” American Economic Review 84, 1157–1173.

Averett, S., and S. Korenman (1996), “The Economic Reality of the Beauty Myth,” Journal of Human Resources 31, 304–330.

Ayers, I., and S. D. Levitt (1998), “Measuring Positive Externalities from Unobservable Victim Precaution: An Empirical Analysis of Lojack,” Quarterly Journal of Economics 108, 43–77.

Banerjee, A. , J. Dolado, J. W. Galbraith, and D. F. Hendry (1993), Co-Integration, Error-Correction, and the Econometric Analysis of Non-Stationary Data. Oxford: Oxford University Press.

Bartik, T. J. (1991), “The Effects of Property Taxes and Other Local Public Policies on the Intrametropolitan Pattern of Business Location,” in Industry Location and Public Policy. Ed. H. W. Herzog and A. M. Schlottmann, 57–80. Knoxville: University of Tennessee Press.

Becker, G. S. (1968), “Crime and Punishment: An Economic Approach,” Journal of Political Economy

76, 169–217.

Belsley, D., E. Kuh, and R. Welsch (1980), Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Berk, R. A. (1990), “A Primer on Robust Regression,” in

Modern Methods of Data Analysis. Ed. J. Fox and

J. S. Long, 292–324. Newbury Park, CA: Sage Publications.

Betts, J. R. (1995), “Does School Quality Matter? Evidence from the National Longitudinal Survey of Youth,” Review of Economics and Statistics 77, 231–250.

Biddle, J. E., and D. S. Hamermesh (1990), “Sleep and the Allocation of Time,” Journal of Political Economy 98, 922–943.

Biddle, J. E., and D. S. Hamermesh (1998), “Beauty, Productivity, and Discrimination: Lawyers’ Looks and Lucre,” Journal of Labor Economics 16, 172–201.

Blackburn, M., and S. Korenman (1994), “The Declining Marital-Status Earnings Differential,” Journal of Population Economics 7, 247–270.

Blackburn, M., and D. Neumark (1992), “Unobserved Ability, Efficiency Wages, and Interindustry Wage Differentials,” Quarterly Journal of Economics 107, 1421–1436.

Blömstrom, M. , R. E. Lipsey, and M. Zejan (1996), “Is Fixed Investment the Key to Economic Growth?”

Quarterly Journal of Economics 111, 269–276. Bollerslev, T. , R. Y. Chou, and K. F. Kroner (1992),

“ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence,” Journal of Econometrics 52, 5–59.

Bollerslev, T. , R. F. Engle, and D. B. Nelson (1994), “ARCH Models,” Chapter 49 in Handbook of Econometrics, Volume 4. Ed. R. F. Engle and D. L. McFadden, 2959–3038. Amsterdam: NorthHolland.

Bound, J. , D. A. Jaeger, and R. M. Baker (1995), “Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and Endogenous Explanatory Variables is Weak,” Journal of the American Statistical Association 90, 443–450.

785

Breusch, T. S., and A. R. Pagan (1979), “A Simple Test for Heteroskedasticity and Random Coefficient Variation,”

Econometrica 50, 987–1007.

Cameron, A. C., and P. K. Trivedi (1998), Regression Analysis of Count Data. Cambridge: Cambridge University Press.

Campbell, J. Y., and N. G. Mankiw (1990), “Permanent Income, Current Income, and Consumption,” Journal of Business and Economic Statistics 8, 265–279.

Card, D. (1995), “Using Geographic Variation in College Proximity to Estimate the Return to Schooling,” in

Aspects of Labour Market Behavior: Essays in Honour of John Vanderkamp. Ed. L. N. Christophides, E. K. Grant, and R. Swidinsky, 201–222. Toronto: University of Toronto Press.

Card, D., and A. Krueger (1992), “Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States,” Journal of Political Economy 100, 1–40.

Castillo-Freeman, A. J., and R. B. Freeman (1992), “When the Minimum Wage Really Bites: The Effect of the U.S.-Level Minimum on Puerto Rico,” in

Immigration and the Work Force. Ed. G. J. Borjas and R. B. Freeman, 177–211. Chicago: University of Chicago Press.

Clark, K. B. (1984), “Unionization and Firm Performance: The Impact on Profits, Growth, and Productivity,”

American Economic Review 74, 893–919.

Cloninger, D. O. (1991), “Lethal Police Response as a Crime Deterrent: 57-City Study Suggests a Decrease in Certain Crimes,” American Journal of Economics and Sociology 50, 59–69.

Cloninger, D. O., and L. C. Sartorius (1979), “Crime Rates, Clearance Rates and Enforcement Effort: The Case of Houston, Texas,” American Journal of Economics and Sociology 38, 389–402.

Cochrane, J. H. (1997), “Where is the Market Going? Uncertain Facts and Novel Theories,” Economic Perspectives 21, Federal Reserve Bank of Chicago, 3–37.

Cornwell, C., and W. N. Trumbull (1994), “Estimating the Economic Model of Crime Using Panel Data,” Review of Economics and Statistics 76, 360–366.

Currie, J. (1995), Welfare and the Well-Being of Children. Chur, Switzerland: Harwood Academic Publishers.

Currie, J., and N. Cole (1993), “Welfare and Child Health: The Link Between AFDC Participation and Birth Weight,” American Economic Review 83, 971–983.

Currie, J., and D. Thomas (1995), “Does Head Start Make a Difference?” American Economic Review 85, 341–364.

References

Davidson, R., and J. G. MacKinnon (1981), “Several Tests of Model Specification in the Presence of Alternative Hypotheses,” Econometrica 49, 781–793.

Davidson, R., and J. G. MacKinnon (1993), Estimation and Inference in Econometrics. New York: Oxford University Press.

De Long, J. B., and L. H. Summers (1991), “Equipment Investment and Economic Growth,” Quarterly Journal of Economics 106, 445–502.

Dickey, D. A., and W. A. Fuller (1979), “Distributions of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association 74, 427–431.

Diebold, F. X. (1998), Elements of Forecasting. Cincinnati, OH: South-Western.

Downes, T. A., and S. M. Greenstein (1996), “Understanding the Supply Decisions of Nonprofits: Modeling the Location of Private Schools,” Rand Journal of Economics 27, 365–390.

Draper, N., and H. Smith (1981), Applied Regression Analysis. 2d ed. New York: Wiley.

Durbin, J. (1970), “Testing for Serial Correlation in Least Squares Regressions When Some of the Regressors are Lagged Dependent Variables,” Econometrica 38, 410–421.

Durbin, J., and G. S. Watson (1950), “Testing for Serial Correlation in Least Squares Regressions I,” Biometrika 37, 409–428.

Eicker, F. (1967), “Limit Theorems for Regressions with Unequal and Dependent Errors,” Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1, 59–82. Berkeley: University of California Press.

Eide, E. (1994), Economics of Crime: Deterrence and the Rational Offender. Amsterdam: North Holland.

Engle, R. F. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica 50, 987–1007.

Engle, R. F., and C. W. J. Granger (1987), “Co-integration and Error Correction: Representation, Estimation, and Testing,” Econometrica 55, 251–276.

Evans, W. N., and R. M. Schwab (1995), “Finishing High School and Starting College: Do Catholic Schools Make a Difference?” Quarterly Journal of Economics

110, 941–974.

Fair, R. C. (1996), “Econometrics and Presidential Elections,” Journal of Economic Perspectives 10, 89–102.

Friedman, B. M., and K. N. Kuttner (1992), “Money, Income, Prices, and Interest Rates,” American Economic Review 82, 472–492.

786

References

Garen, J. E. (1994), “Executive Compensation and Principal-Agent Theory,” Journal of Political Economy

102, 175–1199.

Geronimus, A. T., and S. Korenman (1992), “The Socioeconomic Consequences of Teen Childbearing Reconsidered,” Quarterly Journal of Economics 107, 1187–1214.

Goldberger, A. S. (1991), A Course in Econometrics. Cambridge, MA: Harvard University Press.

Granger, C. W. J., and P. Newbold (1974), “Spurious Regressions in Econometrics,” Journal of Econometrics 2, 111–120.

Greene, W. (1997), Econometric Analysis. 3rd edition. New York: MacMillan.

Griliches, Z. (1957), “Specification Bias in Estimates of Production Functions,” Journal of Farm Economics

39, 8–20.

Grogger, J. (1990), “The Deterrent Effect of Capital Punishment: An Analysis of Daily Homicide Counts,”

Journal of the American Statistical Association 410, 295–303.

Grogger, J. (1991), “Certainty vs. Severity of Punishment,” Economic Inquiry 29, 297–309.

Hall, R. J. (1988), “The Relation Between Price and Marginal Cost in U. S. Industry,” Journal of Political Economy 96, 921–948.

Hamermesh, D. S., and J. E. Biddle (1994), “Beauty and the Labor Market,” American Economic Review 84, 1174–1194.

Hamilton, J. D. (1994), Time Series Analysis. Princeton, NJ: Princeton University Press.

Hanushek, E. (1986), “The Economics of Schooling: Production and Efficiency in Public Schools,” Journal of Economic Literature, 1141–1177.

Harvey, A. (1990), The Econometric Analysis of Economic Time Series. 2d ed. Cambridge, MA: MIT Press.

Hausman, J. A. (1978), “Specification Tests in Econometrics,” Econometrica 46, 1251–1271.

Hausman, J. A., and D. A. Wise (1977), “Social Experimentation, Truncated Distributions, and Efficient Estimation,” Econometrica 45, 319–339.

Herrnstein, R. J., and C. Murray (1994), The Bell Curve: Intelligence and Class Structure in American Life. New York: Free Press.

Hersch, J., and L. S. Stratton (1997), “Housework, Fixed Effects, and Wages of Married Workers,” Journal of Human Resources 32, 285–307.

Hines, J. R. (1996), “Altered States: Taxes and the Location of Foreign Direct Investment in America,”

American Economic Review 86, 1076–1094.

Holzer, H. (1991), “The Spatial Mismatch Hypothesis: What Has the Evidence Shown?” Urban Studies 28, 105–122.

Holzer, H. , R. Block, M. Cheatham, and J. Knott (1993), “Are Training Subsidies Effective? The Michigan Experience,” Industrial and Labor Relations Review

46, 625–636.

Hoxby, C. M. (1994), “Do Private Schools Provide Competition for Public Schools?” National Bureau of Economic Research Working Paper Number 4978.

Huber, P. J. (1967), “The Behavior of Maximum Likelihood Estimates Under Nonstandard Conditions,”

Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1, 221–233. Berkeley: University of California Press.

Hunter, W. C., and M. B. Walker (1996), “The Cultural Affinity Hypothesis and Mortgage Lending Decisions,” Journal of Real Estate Finance and Economics

13, 57–70.

Hylleberg, S. (1991), Modelling Seasonality. Oxford: Oxford University Press.

Kane, T. J., and C. E. Rouse (1995), “Labor-Market Returns to Twoand Four-Year Colleges,” American Economic Review 85, 600–614.

Kiel, K. A., and K. T. McClain (1995), “House Prices During Siting Decision Stages: The Case of an Incinerator from Rumor Through Operation,” Journal of Environmental Economics and Management 28, 241–255.

Kleck, G., and E. B. Patterson (1993), “The Impact of Gun Control Ownership Levels on Violence Rates,” Journal of Quantitative Criminology 9, 249–287.

Koenker, R. (1981), “A Note on Studentizing a Test for Heteroskedasticity,” Journal of Econometrics 17, 107–112.

Korenman, S., and D. Neumark (1991), “Does Marriage Really Make Men More Productive?” Journal of Human Resources 26, 282–307.

Korenman, S., and D. Neumark (1992), “Marriage, Motherhood, and Wages,” Journal of Human Resources 27, 233–255.

Krueger, A. B. (1993), “How Computers Have Changed the Wage Structure: Evidence from Microdata, 1984–1989,”

Quarterly Journal of Economics 108, 33–60.

Krupp, C. M., and P. S. Pollard (1996), “Market Responses to Antidumping Laws: Some Evidence from the U. S. Chemical Industry,” Canadian Journal of Economics 29, 199–227.

Kwiatkowski, D. , P. C. B. Phillips, P. Schmidt, and Y. Shin (1992), “Testing the Null Hypothesis of

787

Соседние файлы в предмете [НЕСОРТИРОВАННОЕ]