Greene W.H. Econometric Analysis

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INDEX

Aakvik, A., 587n28

Abowd, J., 881n32

Abramovitz, M., 462n2, 583, 607,

1089n2, 1091, 1092, 1093

Abrevaya, J., 619, 721, 795

abrupt change in economic

environment. See structural

change

accelerated failure time model, 867

acceptance region, 111, 1062

Achen, C., 420n30

ADF-GLS procedure, 956

adjusted R

, 42–44, 139

adjustment equation, 422

Adkins, L., 91n14

admissible, 325

Aﬁﬁ, T., 95n18

age-earnings proﬁle, 161

aggregated market share data,

782–784

Ahn, S., 382n24, 402, 497, 499, 502,

503, 505

Ahn and Schmidt estimator, 502

AIC, 139, 140, 533

Aigner, D., 244, 304

Aigner, D. K., 124n4, 833, 839,

840, 841

Aitchison, J., 1024n4

Aitken, A. C., 265

Aitken estimator, 265

Aitken’s theorem, 265

Akaike information criterion

(AIC), 139, 140, 533

Akin, J., 731n35

Albert, J., 671–672

Aldrich, J., 687n5, 689n6

algorithm, 1102

Ali, M., 275n17, 307n20

Allenby, G., 656n2, 761

Allison, P., 95, 96, 815, 818

almost sure convergence, 1070, 1071

alternative hypothesis, 109–110

Amemiya, T., 44, 181n2, 186,

203n10, 260n2, 261, 263,

280n23, 326, 375n15, 449,

450n3, 451, 453, 465, 471n8,

499, 517, 520n3, 524, 542n12,

543, 683, 687n5, 689, 692,

695, 701n14, 702n16, 711,

845n9, 850, 853, 860n14, 913,

1100n18, 1101n20

Amemiya and MaCurdy

estimator, 499

Amemiya’s prediction criterion, 44

analog estimation, 469

analysis of covariance, 153, 358–359

analysis of residuals, 165

analysis of variance, 41, 42, 1037

analytic function, 1090

Andersen, D., 722

Anderson, J., 416

Anderson, R., 313n31

Anderson, T., 261, 263, 328n42, 335,

379n19, 401, 497n14, 567

Anderson and Hsiao estimator,

398–400, 497

Anderson and Rubin statistic, 335

Andrews, D., 171n14, 203n10, 647

Angrist, J., 3, 253, 432, 687n5,

706n20, 710, 890, 892n37,

893n39

Anselin, L., 390, 391

antithetic draw, 625

Antweiler, W., 578

applied econometrics, 3

AR(1) disturbance, 910–912,

926–927

AR(1) process, 909

AR statistic, 335

Arabmazar, A., 858

ARCH-in-Mean (ARCH-M)

model, 932–933

ARCH-M model, 932–933

ARCH model, 930–933

ARCH(1) model, 931–932

ARCH(q) model, 932

Arellano, M., 310, 344n2, 381n23,

386, 400, 403, 408, 497, 499,

505, 587n28, 722, 723n30,

729, 861

Arellano and Bond estimator,

400–409

Arellano-Bond estimator, 464

ARFIMA, 943n2

ARIMA model, 943

Arsanjani, F., 810

art appreciation, 46, 116

Ashenfelter, O., 3, 15, 221, 244

asymptotic covariance matrix, 226,

235, 262, 279, 1085

asymptotic distribution, 67, 68–69,

226, 1084–1088

asymptotic efﬁciency, 69–70,

448–449, 514, 520, 1086

asymptotic expectations,

1087–1088

asymptotic inefﬁciency of median

in normal sampling, 1086

asymptotic negligibility of

innovations, 917

asymptotic normality, 65–67, 448,

519, 912, 1086

asymptotic normality of M

estimators, 452

asymptotic properties, 51

asymptotic unbiasedness, 448

asymptotic uncorrelatedness, 916

asymptotic variance, 520, 521–522

asymptotic vs. true

distribution, 1085

ATE, 889

ATET, 889

AT&T breakup, 345n4

attenuation, 241, 838

attenuation bias, 221

Attﬁeld, C., 564n23

attributes, 762

attrition, 94, 221–222, 886–887

attrition bias, 222, 735

augmented Dickey-Fuller test, 954,

957, 968

autism, 252

1161

1162

Index

autocorrelated least squares

residuals, 904

autocorrelation, 22

estimation, 927–929

GNP deﬂator, 945

maximum likelihood estimation,

557–560

misspeciﬁcation of model,

903–904

panel data, 388

Phillips curve, 904–905

testing for, 922–924

unobserved heterogeneity, 350

autocorrelation coefﬁcient, 909

autocorrelation consistent

covariance estimation, 921

autocorrelation matrix, 909

autocovariance, 909

autocovariance matrix, 909

autonomous variation, 13

autoregressive conditional

heteroscedasticity, 930–938

autoregressive conditionally

heteroscedastic (ARCH)

model, 930–933

autoregressive form, 910

autoregressive integrated moving

average (ARIMA)

model, 943

autoregressive process, 910

average partial effects, 690, 696–699

average treatment effect

(ATE), 889

average treatment effect on the

treated (ATET), 889

Avery, R., 415, 706, 707, 716n27,

720, 754

badly measured data, 97–99

Baker, R., 250

balanced panel, 348

Balestra, P., 376n16, 378, 379n19,

421, 497n14

Baltagi, B., 284, 292n3, 294n6,

344n2, 348, 363n10, 378,

379n19, 379n21, 380, 387,

390, 391, 410, 411, 418, 565,

574, 577, 578, 579n25, 617,

723n30, 971

bandwidth, 205, 447

Bannerjee, A., 411

Bardsen, G., 7n

Barnow, B., 892n36, 894

Bartels, R., 294n6, 304

Barten, A., 309n23

basis vectors, 981, 983

Basmann, R., 334

Bassett, G., 71, 203n10, 204, 276, 453

Battese, G., 375n15

Battese-Coelli form, 843

Bayes factor, 666

Baye’s theorem, 592

Bayesian averaging of classical

estimates, 142–143

Bayesian estimation and inference,

655–680

Bayes factor, 666

Bayes’s theorem, 656

classical regression model,

658–664

computer software, 678–679

conjugate prior, 662

data augmentation, 671

ﬁrm conclusion, 112

Gibbs sampler, 669–670, 672–674

how often used, 433n1

hypothesis testing, 666–667

individual effects models,

674–676

inference, 664–668

informative prior density,

661–663

interval estimation, 665

large-sample results, 668

literature, 656, 656n2

marginal propensity to

consume, 664

Metropolis-Hastings algorithm,

677, 678

noninformative/informative

prior, 659

panel data application, 674–676

point estimation, 664–665

posterior density, 657

prior. See prior

probability, 657–658

proponents of, 655

random parameters model,

676–678

Bayesian inference, 664–668

Bayesian information criterion

(BIC), 139, 140, 533

Bayesian model averaging, 141–143

Bayesian vs. classical testing, 112

Bayes’s theorem, 656

Bazaraa, M., 1093n7

Beach, C., 558, 927

Beach and MacKinnon

algorithm, 560

Beach and MacKinnon

estimator, 927

Beck, N. D., 729n34, 731

Becker, S., 895

behavioral equations, 332

Behrens-Fisher problem, 171n13

Behrman, J., 245

Bek-Akiva, M., 701

Bekker, P., 326

Bell, K., 390, 391

Belsley, D., 90, 100, 101, 1103

Ben-Porath, Y., 344

Bera, A., 277, 858n12

Berndt, E., 18, 124n5, 305n16,

311n25, 311n26, 313, 314n32,

522n4, 533n10, 1100, 1103

Bernoulli distribution, 1026

Bernstein-von Mises theorem, 668

Beron, K., 640

Berry, S., 603, 771, 782

Bertschek, I., 651, 706, 720, 753

Berzeg, K., 378

best linear unbiased (BLU), 258

best linear unbiased estimator

(BLUE), 1060

beta distribution, 1025

beta function, 1092

between-groups estimator, 358

Beyer, A., 967, 968, 969

BFGS method, 1099

Bhat, C., 625, 626, 771

BHHH algorithm, 934

BHHH estimator, 481, 522, 531,

532, 571, 692, 816

BHPS, 344, 794

bias, 222, 1064

attenuation, 221

attrition, 222, 735

bootstrap estimation

technique, 613

nonresponse, 735

omitted variable, 219

sample selection, 221, 735

selection, 881

simultaneous equations, 220,

316n35

survivorship, 221

truncation, 221, 811

underlying model, 143

BIC, 139, 140, 533

Billingsley, P., 914n6

bin, 446

binary choice, 681, 683–759

average partial effects, 696–699

bias reduction, 727–728

bivariate probit model, 738–752

conditional ﬁxed effects

estimator, 722–724

Index

1163

dynamic models, 729–731

endogenous choice-based

sampling, 710, 711

endogenous right-hand-side

variables, 706–710

endogenous sampling, 749–751

estimation and inference,

690–715

ﬁxed effects model, 721–722

functional form and regression,

687–690

goodness of ﬁt, 701–703

heteroscedasticity, 714

hypothesis tests, 703–706

interaction effects, 699–701

IPW estimator, 736, 738

latent regression model,

686–687

logit model. See logit model

marginal effects, 693–694

maximum likelihood estimation,

739–741

MSL, 651–652, 733

multivariate probit model,

752–755

Mundlak’s approach, 727

nonresponse, 735, 737

omitted variables, 713

panel data, 716–738, 744–745

parameter heterogeneity,

733–734

pooled estimator, 717–718

probit model. See probit model

random effects model, 718–720

random utility models, 684–685

robust covariance matrix

estimation, 692–693

semiparametric estimator, 442

semiparametric model of

heterogeneity, 731–733

speciﬁcation analysis, 711–714

structural equations, 685

zero correlation, 742

binary variable, 149–158

difference in differences

regression, 155–158

dummy variable trap, 152

several categories, 152

several groupings, 152–154

threshold effects/categorical

variables, 154–155

treatment effects, 155–158

Binkley, J., 295n9

binomial distribution, 1026

Birkes, D., 203n10

bivariate copula approach, 439

bivariate normal distribution,

1037–1038

bivariate normal probability, 627

bivariate probit model, 738–752

bivariate regression, 29

block bootstrap, 614

block diagonal matrix, 993

BLP random parameters model,

782–784

BLU, 258

BLUE, 1060

Blundell, R., 215, 408, 501n17, 693,

713n25, 858n12

Bockstael, N., 390, 391, 810

Boes, S., 785n8, 799, 801

Bollerslev, T., 930, 933, 934, 937n27,

938n28

Bond, S., 403, 408, 497, 501n17

bond rating agencies, 784

book. See textbook

Boot, J., 942n50

bootstrapping, 72, 205, 611–615

B¨orsch-Supan, A., 628n8, 753n42

Boskin, M., 761

Bound, J., 250

bounds test, 964

Bourguignon, F., 79n11

Bover, O., 310, 381n23, 386, 403,

408, 497, 499, 505, 587n28

Box, G., 193, 607, 910n5, 925n14

box and whisker plot, 1050

Box-Cox transformation, 193–196

Box-Ljung reﬁnement, 923

Box-Muller method, 607

Box-Pierce test, 922–923

Boyes, W., 702, 710, 751, 880

Brannas, K., 820

Brant, R., 791, 795

Brant test for health

satisfaction, 792

Bray, T., 607

Breslaw, J., 628n7

Breusch, T., 276, 298, 304, 348, 376,

417n27, 561, 564, 575, 647,

922

Breusch-Pagan Lagrange multiplier

test, 276. See also Lagrange

multiplier test

British Household Panel Survey

(BHPS), 344, 794

Brock, W., 685n3

Brown, B., 307n19

Brown, C., 858n

Brown, J., 1024n4

Brown, P. A., 416

Browning, M., 215

Broyden-Fletcher-Goldfarb-

Shanno (BFGS)

method, 1099

Broyden’s method, 1099

Brundy, J., 231

Bult, J., 593, 652n10

Burbidge, J., 793

burn in, 669, 678

Burnett, N., 747

Burnside, C., 481n10

Buse, A., 266n5, 524n5

Butler, J., 582, 706, 718, 719, 720,

753, 755, 792, 793, 795

Butler, R., 204n11

Butler and Mofﬁtt method, 582, 719

Cain, G., 892n36, 894

calculus and intermediate

economics courses, 792–793

Caldwell, S., 852

Calzolari, G., 932n19, 936n24

Cameron, A., 7n3, 97, 251n11, 353,

441, 534, 542, 547, 612, 613,

619, 623, 656, 668, 674, 683,

805, 806, 807, 808, 810, 818,

862n15

Cameron, C., 439, 804n12, 805

Campbell, J., 3, 480

CAN estimator, 258

canonical correlation, 966

capital asset pricing model

(CAPM), 290, 932

Cappellari, L., 753

cardholder status and default

behavior, 751–752

Carey, K., 384, 403, 464, 466

Carlin, J., 656n2, 678

Carson, R., 811

Case, A., 390

Casella, G., 669

Cassen, R., 587

Cauchy-Schwarz inequality, 1074

Caudill, S., 687n5

causal effects, 251–253

causal modeling, 251

Caves, D., 194

cdf, 1016

Cecchetti, S., 3, 951, 953

censored normal distribution,

846–848

censored random variable,

847–848

censored regression (tobit) model,

848–850

censored variable, 846

1164

Index

censoring, 833, 845–872

censored normal distribution,

846–848

corner solution model, 855

duration models. See duration

models

estimation, 850–852

event counts, 811, 813

examples of, 844–845

heteroscedasticity, 858–859

nonnormality, 859–860

panel data applications, 860–861

tobit model, 848–855, 858

two-part models, 852–857

centering matrix, 978

central limit theorem, 668, 915–917,

1078–1083

central moments, 459, 1018

central tendency, 1017, 1048

CES production function, 167, 182

Chamberlain, G., 378n28, 379n20,

381n23, 382, 415, 466, 468n3,

586, 587n27, 663n9, 722,

727, 730

Chamberlain’s approach, 381–384

Chambers, R., 307n19

change in economic environment.

See structural change

characteristic equation, 995–996

characteristic roots/vectors,

995, 1001

characteristics, 762

Chatterjee, P., 706, 793

Chavez, J., 307n19

Chebychev inequality, 1018, 1068

Chebychev’s weak law of large

numbers, 1071

Chen, T., 587n28

Chesher, A., 691n9, 858n12, 864

chi-squared distribution, 1021, 1022

chi-squared test, 379

chi-squared variable, 76

Chiappori, R., 729

Chib, S., 433, 671–672, 678

Chintagunta, P., 771

choice based sample, 773

choice-based sampling, 710

choice-based sampling estimator,

711n22

choice methods, 681–832. See also

microeconometric methods

Cholesky decomposition, 608

Cholesky factorization, 629, 1002

Cholesky values, 1002

Chou, R., 933

Chow, G., 168n10, 417n27, 845

Chow test, 168n10, 170, 171

Christensen, L., 18, 106, 107, 124n5,

147, 163, 164, 183, 194, 211,

212, 218, 307n20, 309,

311n25, 311n26, 311n27,

312n29, 312n30, 341, 901

Christoﬁdes, L., 742n40

Chu, C., 971

classical likelihood-based

estimation, 434–436

classical model selection, 140–141

classical normal linear regression

model, 1042–1043

classical regression model, 25

Clayton copula, 438

Cleveland, W., 213

closed-form solution, 1094

cluster estimator, 353, 546–547

cluster (and panel) robust

covariance matrix, 388–389

cluster-robust estimator, 546

clustering and stratiﬁcation,

352–354

Coakley, J., 410

Cobb-Douglas production function,

307–310

electricity generation, 106

functional form for nonlinear

cost function, 162, 163

generalization, 124

LAD estimation, 205–206

Cochrane, D., 926

Cochrane and Orcutt estimator, 926

Cockburn, I., 652n10

coefﬁcient of determination,

41, 1037

cofactor, 987

cointegrating rank, 962

cointegrating vector, 959

cointegration, 959–970

bounds test, 964

common trend, 962–963

consumption and output, 960, 966

error correction and VAR

representations, 963–964

estimating cointegration

relationships, 967

German money demand, 967–970

multiple cointegrating

vectors, 962

several cointegrated series,

960–961

testing for, 965–967

column space/rank, 984, 985

column vector, 973

common trend, 962–963

comparing matrices, 1006–1007

complementary log model, 689

complete data log likelihood, 1104

complete system of equations, 316

completeness condition, 319, 320

comprehensive model, 135

computation and optimization,

1089–1108

approximating integrals by

quadrature, 1092–1093

computing derivatives,

1096–1097

computing integrals, 1090

concentrated log likelihood

function, 1108

convergence, 1103

EM algorithm, 1104–1106

function of one parameter,

1106–1107

function of two parameters,

1107–1108

gamma and related functions,

1091–1092

gradient methods, 1097–1099

iterative algorithm, 1095

maximum likelihood estimation,

1100–1101

multiple solutions/no

solution, 1103

optimization, 1093–1106

optimization with constraints,

1101–1102

standard normal cumulative

distribution function,

1090–1091

step size, 1103

concentrated log likelihood, 394,

551, 586, 1101, 1108

condition number, 90, 999

conditional density, 434

conditional distribution, 1034

conditional ﬁxed effects estimator,

722–724

conditional latent class model,

592–594

conditional likelihood function, 722

conditional logit model, 730,

766–767

conditional mean, 1034

conditional mean assumption, 889

conditional mean estimating

function, 212

conditional mean function, 181

conditional median, 12, 181

conditional moment tests, 860

conditional variance, 264, 1035

Index

1165

conditional variation, 12

conditioning in bivariate

distribution, 1034–1036

conﬁdence interval, 76–80, 1061,

1062, 1065

conﬁdence level, 1061

conﬁrmation of a model, 6

conjugate prior, 661, 662

Conniffe, D., 292n3, 294n8

consistency, 63–65, 67–68, 448

consistency of a mean of functions,

1070

consistency of M estimators, 452

consistency of the sample

mean, 1069

consistency of the test, 111

consistent and asymptotically

normally distributed

(CAN), 258

consistent estimator, 65, 227, 1069

consistent test, 1064, 1065

constant elasticity, 17

constant returns to scale, 311

constant variance, 22

constrained optimization,

1012–1013

consumption data (1940-50), 14

consumption function, 136

ﬁt, 41

gasoline, 172

Hausman test, 237

instrumental variables estimates,

248–249

Keynes, 4, 13–14

contagion property, 1029

contiguity, 390

contiguity matrix, 390

continuous distributions,

607–608

continuous random variable, 1015

Contoyannis, C., 222, 697, 698, 794,

795, 798, 884, 887

contrasts, 363

control function, 709, 894

control group, 156

control observations, 156

convergence

distribution, 1076

functions, of, 1073–1074

lower powers, 1072

mean square, 1067

optimization, 1103

probability, 1067–1070, 1073

quadratic mean, 1067

random vector/matrix, 1067

rth mean, 1072, 1073, 1075

convergence of functions,

1073–1074

convergence of (the) moments, 484,

913–915, 1075

convergence to normality, 915–917

Conway, D., 176

Cooley, T., 315n34

copula functions, 436–439

corner solution model, 855

Cornwell, C., 232, 351, 359n5

Cornwell and Rupert wage

equation, 368–370

correlation, 1049

canonical, 966

causation, and, 251

multiple, 44

partial, 38

residual, 353

serial, 258. See serial correlation

spatial error, 393

tetrachoric, 741

zero, 742

correlation matrix, 1039, 1049

cosine law, 989

cost function (U.S.

manufacturing), 313

cost function model, 307–310

cost shares, 312

Coulson, N., 930

counterfactual, 15

counts of events. See models for

counts of events

covariance, 21, 1035, 1049

covariance matrix, 257, 1039, 1049

covariance matrix estimation, 1100

covariance stationary, 907

covariance structures model, 299

covariate, 11, 12

Cox, D., 135, 193, 865n17, 870

Cox test, 139

CPS, 344

Cragg, J., 242n6, 273n13, 494,

701n14, 930

Cram´er, H., 517, 520

Cramer, J., 701, 702

Cram´er-Rao lower bound, 73, 74,

436, 520, 1059, 1060

Cram´er-Wold device, 1078

Crawford, I., 215

credit card expenditure, 208–210

credit scoring, 711

criterion function, 450, 464

critical region, 1062

cross section, 1048

Culver, S., 410

Cumby, R., 468n3, 494

cumulative distribution function

(cdf), 1016

Current Population Survey

(CPS), 344

test, 481n11

D-i-D estimators, 156

D’Addio, A., 794

Dahlberg, M., 503

Dale, S., 3, 219, 224, 251

Das, M., 794, 795

Dastoor, N., 135n10

data envelopment analysis, 843

data generating mechanism, 434

data generating process (DGP),

946, 1015

data generation, 23

data imputation, 95–97

data problems, 88–102

data sets used in applications,

1109–1114

data smoothing, 214

Davidon-Fletcher-Powell (DFP)

method, 1099

Davidson, A., 611n2

Davidson, J., 433n1, 474

Davidson, R., 135, 136, 181n2, 185,

186, 189, 216, 236, 248, 274,

294n7, 318n37, 327, 329, 449,

450, 453, 468n3, 494, 515,

521, 552, 567, 612, 613, 704,

713n25, 714, 914, 916, 918,

936, 943n2, 946n3

Davidson and MacKinnon J

test, 136

Deaton, A., 135n10, 307n19,

309n23, 311n26

Deb, P., 597

Debreu, G., 839

decomposition, 79

decomposition of variance, 1036

deﬁnite matrix, 1001–1002,

1004–1006

degree of truncation, 836

degrees of freedom, 37

degrees of freedom correction, 353

Dehejia, R., 895

delta method, 1083–1084

asymptotic covariance

matrix, 850

asymptotic distribution, 68–69

Krinsky and Robb technique, 610

standard errors, 194

demand for electricity and gas, 416

demand system, 309

1166

Index

DeMaris, A., 6, 785n8

Dempster, A., 814, 1104, 1106

density, 434

Denton, F., 307n21

dependence parameter, 438

dependent variable, 12, 1007

DeSarbo, W., 593, 652n10

DesChamps, P., 305n16

descriptive statistics, 1048–1051

deseasonalizing the data, 152

determinant (matrix), 1000

deterministic relationship, 13

deterministic theory, 6

detrending, 946

developing countries, 426

deviance, 804

deWitt, G., 942n50

Dezhbaksh, H., 923n13

DFP method, 1099

DGP, 946, 1015

Dhrymes, P., 331n44, 683

diagonal matrix, 973

diagonalization (matrix), 997

Dickey, D., 948, 949, 955, 957

Dickey-Fuller tests, 948–945

D-i-D estimators, 156

Diebold, F., 139, 140

Dielman, T., 344n2

Diewert, E., 311n27

difference in differences, 156

difference in differences regression,

155–158

differencing, 942–946

differential item functioning (DIF),

801, 802

digamma function, 1092

Diggle, R., 716n27

DiNardo, J., 205

direct product, 635

discrepancy vector, 117

discrete change in underlying

process. See structural

change

discrete choice, 681–832. See also

microeconometric methods

discrete populations, 608–609

discrete random variable, 1015

discrete uniform distribution, 608

discriminant analysis, 702n17

distributed lag model, 132

distribution theory. See probability

and distribution theory

disturbance, 13, 26

disturbance processes, 909–912

doctor visits

count data models, 809–810

geometric regression model,

571–573

hurdle model, 826

insurance, 881

panel data models, 821, 822

Dodge, Y., 203n10

Domowitz, I., 930

Doob, J., 914n6

Doppelhofer, G., 142

double-length regression, 936

downhill simplex, 1094

Dreze, J., 166

Duan’s smearing estimator, 83

Dufour, J., 335, 336

dummy variable. See binary

variable

dummy variable trap, 152

Duncan, G., 273n11, 858n12, 892

duration models, 861–872

duration data, 862–863

exogenous variables, 867–868

hazard function, 864–866

heterogeneity, 868–869

maximum likelihood estimation,

866–867

nonparametric/semiparametric

approaches, 869–871

parametric models of duration,

863–869

product limit estimator, 870

proportional hazard model,

870, 871

survival function, 863, 866

survival models (strike duration),

871–872

Durbin, J., 923, 923n12, 926

Durbin-Watson statistic, 923

Durbin-Watson test, 163n7,

923, 924

Durbin’s test, 924

Durlauf, S., 685n3

Dwivedi, T., 292n2, 294, 296n13

dynamic binary choice model,

729–731

dynamic labor supply equation, 408

dynamic model, 320

dynamic panel data models,

398–409, 421–426, 496–506

dynamic SUR model, 305n16

earnings and education, 14–15

earnings equation, 116–117, 126

ECHP, 794

econometric model, 316

econometric modeling, 4–7

econometrics

applied, 3

macroeconometrics, 3

microeconometrics, 2–3

paradigm, 1–3

practice of, 3–4

theoretical, 3

economic returns to schooling, 397

economic time series, 257

education, 15

effect of the treatment on the

treated, 220

efﬁciency of FGLS estimator, 268

efﬁcient estimator, 265

efﬁcient scale, 107

efﬁcient score, 530

efﬁcient two-step estimator, 936

efﬁcient unbiased estimation,

1056, 1058

Efron, B., 611n2, 612, 613, 701n14

Eichenbaum, M., 481n10

Eicker, F., 273n11

eigenvalue, 997

Eisenberg, D., 706n20, 746n41

Eitrheim, 7n

Elashoff, R., 95n18

Elliot, G., 956, 957

EM algorithm, 1104–1106

empirical likelihood function, 440

empirical moment equation,

469, 475

encompassing model, 135–136

encompassing principle, 135

Enders, W., 942n1

endogeneity, 394–411

endogeneity and instrumental

variable estimation, 219–256

assumptions of extended model,

223–224

causal effects, 251–253

consumption function, 248–249

generalized regression model,

262–264

Hausman speciﬁcation test,

234–237

IV estimator, 225–230, 242

least squares, 225

least squares attenuation,

240–242

measurement error, 239–246

nonlinear instrumental variables

estimation, 246–248

overidentiﬁcation, 238–239

overview, 222

problem of endogeneity, 224

proxy variables, 242–246

Index

1167

speciﬁcation tests, 233–239

two-stage least squares (2SLS),

230–232

weak instruments, 249–251

where endogeneity arises,

219–222

Wu speciﬁcation test, 234–237

endogenous, 219

endogenous choice-based sampling,

710, 711

endogenous right hand side

variables, 219–222

endogenous sampling, 749–751

endogenous treatment effects,

219–220

Engle, R., 2, 317, 713n25, 930,

930n16, 932, 959n12, 964,

965, 968, 969

entropy, 441

Epanechnikov kernel, 214

Epstein, D., 729n34, 731

equation systems. See systems of

equations

equilibrium, 291

equilibrium condition, 332

equilibrium error, 961

equilibrium multiplier, 423

ergodic stationarity, 524

ergodic theorem, 475, 914

ergodicity, 914, 915

ergodicity of functions, 915

Ericsson, N., 969

Eriksson, T., 794

Erlang distribution, 1024

error

equilibrium, 961

mean absolute, 88

mean squared, 1057

measurement, 97–99, 221,

239–246

prediction, 81

root mean squared, 88

speciﬁcation, 56, 806, 874

standard, 61, 1055

Type I/II, 1062

error components model, 371

error correction, 959

error function, 1090n4

Estes, E., 211n16

estimated quantile regression

models, 209

estimated random coefﬁcients

models, 419

estimation, 432–454. See also

estimation and inference;

estimator

Bayesian. See Bayesian

estimation and inference

censoring, 850–852

classical likelihood-based,

434–436

copula functions, 436–439

GMM. See generalized method of

moments (GMM) estimation

interval, 665, 1060–1062

IV. See endogeneity and

instrumental variable

estimation

kernel density, 445–447

kernel density methods, 442

LAD. See LAD

estimator/estimation

least squares. See least squares

estimator/estimation

maximum empirical likelihood,

440–441

MDE, 463–468

method of moments. See method

of moments

MLE. See maximum likelihood

estimation (MLE)

nonparametric, 444–447

parametric estimation and

inference, 434–439, 443

point, 664–665, 1055–1060

semiparametric, 439–444

simulation-based. See

simulation-based estimation

estimation and inference,

1047–1066. See also

estimation

binary choice, 690–915

descriptive statistics, 1048–1051

hypothesis testing, 1062–1066.