Ad valorem tax

, 303

Adulterated liquor

, 290, 292, 295

Aggregate shares

, 140

for simulation study

, 157

Analytic inference. See Model-based inference

Applied economic research

, 175

ARMA models

, 110–111

Asymptotic

design-based variance

, 200

linearization representation

, 217

properties of estimators

, 214

theory for M-estimation

, 146–147

Asymptotic distribution

, 216

for matching estimators

, 210

Asymptotic variance

, 230

estimator

, 119

formula

, 118

Auditory mode

, 22

Automated clearing house (ACH)

, 245

Auxiliary variables

, 90

Average bid auctions (ABA)

, 80

Average partial effects

, 119

Bahadur-type representation

, 217

Balanced repeated replication (BRR)

, 96

Bandwidths

, 194–196

selection

, 186–188

Bank of Canada

, 36, 47, 98, 104

Bernoulli distributions

, 99

Bernoulli log likelihood

, 128

Bernoulli sampling. See Variable probability sampling (VP sampling)

Bias function

, 141

Bias-adjustment

, 215

“Biased sampling” problems

, 147

“Big data” sets

, 37–38

Binary responses

, 128

Binary variables

, 311

Black market liquor in Colombia

, 301–302

adulterated sales

, 302

average marginal effects for contraband and adulterated liquor–levels

, 307

average marginal effects for MICE

, 309

coefficients for specifications in salary imputation model

, 308

contraband goods

, 288–289

data

, 290–292

multiple imputation

, 297–301, 310–313

percentage of adulterated purchases

, 304

percentage of contraband purchases

, 303

percentage of illegal purchases

, 289

residual analysis

, 313–314

results

, 292–296

“Blocking” strategy

, 247

Bootstrap

, 100

conventional

, 211

sample

, 211

variance estimation via

, 96–98

wild bootstrap

, 61–84, 211, 219

bsweights command

, 103

Business

heterogeneity

, 240

surveys

, 239–240

Calibration

equations

, 92

procedure

, 88

Canadian Internet Usage Survey (CIUS)

, 98

Card acceptance

, 36, 39, 49

estimates

, 42–43

and usage

, 41

Card payments, regression models of

, 43–46

Card-acceptance model

, 45

Cash on hand variable

, 89, 100–101

cemplik2 function

, 171

Census region

, 15

Checkable deposits (CHKD)

, 241

Choice-based sampling

, 162n6

Classical nearest neighbor imputation

, 213

Classical raking estimator

, 90–92

Cluster-means regression (CMR)

, 74

Cluster-robust inference

, 63

WCB

, 65–67

Cluster-robust standard error

, 67

Cluster-robust test statistics

, 62

Cluster-robust variance estimator (CRVE)

, 64

Colombian National Administrative Department of Statistics (DANE)

, 290–291

Combined inference approach

, 176–177

Commercial banks (CMB)

, 241

Complementary log–log (cloglog)

, 294

Complex

sampling plan

, 174

survey methods

, 122, 176

Computation

, 169–171

Conditional density

, 141

Conditional mean

, 127

functions

, 130

Conditional moment restriction models

, 138

conditional moment equalities

, 139–140

endogenous and exogenous stratification

, 143–146

identification

, 142–143

simulation study

, 156–161

VP sampling

, 140–142

Contactless (tap-and-go) credit card usage binary variable

, 89

Continuous variables

, 89

Conventional bootstrap

, 211

Conventional jackknife variance estimation

, 221

Conventional replication method

, 211

Converged estimator

, 94

Correction factor

, 185

Correlated random effects approach

, 128

County

, 39

data

, 56

effect

, 43

Covariance matrix

, 63

Covariate information

, 215

Cox approach

, 110

Credit cards

, 98

Credit unions (CUS)

, 241

Crimes

, 264, 272

Criminal activity

, 270

Criminal Procedure Law

, 263

Cross-validation function

, 187

Cumulative standard normal (CDF)

, 73

Current Population Survey (CPS)

, 6, 110

socioeconomic variables in

, 11–14

Data-driven approach

, 177, 187

Data-generating processes (DGPs)

, 177, 190

in Monte Carlo simulations

, 188

Degree of “desirability”

, 8–9

Demographic variables

, 11–12

Dependent variable

, 51, 55

Depository and financial institutions

, 240

Descriptive inference. See Design-based inference

Design-based approach

, 180

Design-based estimators

, 174, 176

Design-based inference

, 88, 176

Design-consistent estimator

, 176

Difference in objective functions

, 119

Difference-in-differences (DiD)

, 62

Discounts Asked indicator

, 292, 297

Discounts Offered indicator

, 292

Discrete variables

, 89

Drugs and weapon crimes

, 264

Dynamic regression models

, 110–111

“Econ Plus” set

, 98

Econometric methods

, 62

Econometric models

, 140

Economic theory

, 174, 260

Efficiency bounds

, 148–152

E–M algorithm-based imputation method

, 244

Empirical application of 2013 MOP

, 98–103

Empirical distribution

, 67

Empirical likelihood approach

, 148, 155

Empirical loglikelihood (EL)

, 169

Empirical researchers

, 210

Endogeneity

, 67

Endogenous sampling

, 175

Endogenous stratification

, 143–146, 150–151, 157, 262

median bandwidths for WLC and LC under

, 194

median MSE for WLC, LC, WLS and OLS under

, 190

Estimation approach

, 241

Estrato (strata) variable

, 292, 299

Exogenous stratification

, 111, 126–127, 143–146, 150–151, 157

comparing asymptotic variance of LS and GMM estimators under

, 166–169

median bandwidths for WLC and LC under

, 194

median MSE for WLC, LC, WLS and OLS under

, 191

Explanatory variables

, 41

Fake goods

, 288

Federal Reserve System

, 238, 240

Finite population correction (FPC)

, 291–292

Finite population estimator

, 176

Finite population parameter

, 212

First price auctions (FPA)

, 79–81

Four-digit primary industry code

, 39

Fractional responses

, 128

Full-coverage items

, 247

Gamma distributions

, 129

Gaussian kernel

, 196

Gauss–Markov theorem

, 163n16

General M-estimation

, 111

Generalized method of moments estimator (GMM estimator)

, 147, 150, 151

Generalized regression estimation (GREG)

, 89, 93–94

Geographic information

, 97

Geometric quasi-MLE

, 111

“Group treated” dummy (GTg)

, 64

Hajek estimator

, 217

Hausman specification tests

, 273

Health and Retirement Study (HRS)

, 5–7

socioeconomic variables in

, 11–14

survey outcomes in UAS and

, 14–26

Heckman models

, 299

Heckman two-step estimator

, 312

Heteroskedasticity-robust standard errors

, 63–64, 74

Higher order orthogonal polynomials

, 45

Hit rates test

, 262, 267

Horvitz–Thompson estimator

, 218

Horvitz–Thompson weight (H–T weight)

, 90

Hungarian payments system

, 38

Illegal

alcohol

, 290

cigarettes

, 290

goods

, 288

liquor

, 288

Imputation models

, 238, 239, 297, 299, 310–311

Indicator

function

, 179

variables

, 90, 94

Industry

, 39

effect

, 43

industry-based stratas

, 44

Inference

, 138, 146

efficiency bounds

, 148–152

efficient estimation

, 152–155

modes

, 176

related literature and contribution

, 146–148

testing

, 155–156

see also Conditional moment restriction models

Informative sampling

, 175

Internet match high-quality traditional surveys

health insurance coverage

, 30

home ownership

, 30

HRS

, 6–7

individual earnings

, 31

methods and outline

, 5–6

predicted mean health status by age and survey mode

, 32

predicted probability

, 33

satisfied with life

, 32

self-reported health

, 30–31

socioeconomic variables in HRS, UAS and CPS

, 11–14

survey outcomes in HRS and UAS

, 14–26

UAS

, 7–11

whether retired

, 31

Interview

face-to-face

, 4, 18, 22

mode

, 4–5

online

, 14

telephone

, 4

Inverse Mills ratio

, 56

Inverse probability weighted estimator (IPW estimator)

, 162n12

ipfraking command

, 103

Item response rate

, 244–245

Iterative E–M algorithm approach

, 241

Iterative estimators of regression coefficients

, 146

Iterative proportional fitting (IPF)

, 88

Jackknife method

, 218

Jackknife variance

estimation

, 221

estimator

, 96–97

Kernel function

, 179

assumptions for

, 230–231

“Kernel matching” estimators

, 211

Kernel-based derivative estimation

, 219

Kernel-smoothed p values

, 76

Knowles, Persico, and Todd test (KPT test)

, 261, 278–279n19

Kullback–Leibler Information Criterion (KLIC)

, 110, 112

Labor market duration (LMD)

, 178, 196

Lagrange multipliers

, 154

Large-scale surveys

, 174

Least absolute deviations (LAD)

, 120

Least squares cross-validation (LSCV)

, 177, 189, 204–208

Leave-one-out kernel estimator

, 204

Length biased sampling problem

, 147

Linear regression model

, 63, 139, 144–146

Linearization

of estimator

, 223

methods

, 89

variance estimation via

, 95–96

Local constant estimation (LC estimation)

, 188

median bandwidths

, 194–196

median MSE for

, 190–194

Local constant estimator

, 175–176, 178–180

Local empirical loglikelihood

, 155

Log-likelihood functions

, 110, 270

negative of

, 113–114

Logistic regression analysis

, 37

Logistic regression models

, 39–40, 51

card acceptance

, 51–55

card usage

, 55–57

Logit

, 311

predictive model

, 299

Lognormal distributions

, 129

Lyapunov DoubleArray Central Limit Theorem

, 202–203

Mahalanobis distance

, 213

Marginal density

, 141

Matching

discrepancy

, 215

estimators

, 210

scalar variable

, 221

variable

, 215

Maximum likelihood raking estimator

, 92–93

Median absolute deviations (MAD)

, 190

Medicare

, 17

Misclassification

, 260–261

Missing at random (MAR)

, 213, 297

Missing completely at random (MCAR)

, 297

Missing data process

, 213

Missing not at random (MNAR)

, 297

data

, 312

see also Planned missing data design

Missing-by-design approaches

, 239

Mode effects

, 21–26

Model-assisted estimator

, 176

local constant estimator

, 180

Model-assisted nonparametric regression estimator

, 178

asymptotic properties

, 180–186

local constant estimator

, 178–180

model-assisted local constant estimator

, 180

Model-based inference

, 176, 180

Model-selection statistic

, 122

Model-selection tests

for complex survey samples

, 110

examples

, 128–129

exogenous stratification

, 126–127

nonnested competing models and null hypothesis

, 111–114

with panel data

, 124–126

rejection frequencies

, 131–132

small simulation study

, 129–133

statistics under multistage sampling

, 122–124

under stratified sampling

, 114–122

Modern imputation methods

, 239

Modified kernel density estimator

, 204

Modified plug-in method

, 186

Money market deposits (MMDA)

, 241

Monte Carlo simulations

, 188, 212

bandwidths

, 194–196

DGPs in

, 188, 190

median bandwidths for WLC and LC

, 194–196

median MSE for WLC, LC, WLS and OLS

, 190–193

sample mean squared error

, 189–194

strata borders

, 189

Multiple imputation

, 242, 297–301

See also Nearest neighbor imputation

Multiple Imputation by Chained Equations (MICE)

, 310

average marginal effects for

, 309

binary variables

, 311

imputation models

, 310–311

MNAR data

, 312

ordered variables

, 311–312

Rubin’s rules

, 312–313

Multiple-matrix sampling

, 239

Multiplicative nonresponse models

, 99

Multistage sampling, tests statistics under

, 122–124

Multivariate extensions

, 139

Multivariate normal distribution

, 310

National statistical institutes (NSIs)

, 89

National Survey of Families and Households (NSFH)

, 110, 122

Nearest neighbor imputation

, 210

basic setup

, 212–214

Nearest neighbor imputation estimator

, 210–213, 217–218, 222, 230

proofs of theorems

, 228–234

replication variance estimation

, 218–220

results

, 214–217

simulation study

, 221–224

Nearest neighbor ratio imputation

, 225

New York City Police Department (NYPD)

, 277n1

Non-negligible sampling fraction

, 224–225

Non-sampling errors

, 260

Non-smoothness

, 211, 212

Nonlinear least squares estimators

, 129

Nonlinear regression models

, 139

Nonmandatory survey

, 239

Nonnested competing models

, 111–114

Nonparametric kernel regression

, 174

application

, 196–197

bandwidth selection

, 186–188

model-assisted nonparametric regression estimator

, 178–186

Monte Carlo simulations

, 188–196

proofs

, 199–209

Nonparametric maximum likelihood estimators (NPMLE)

, 147

Nonparametric methods for estimating conditional mean functions

, 174

Nonprobability Internet panels

, 8

Nonrandom weight for stratum-cluster pair

, 122

Nonresponse

, 100

bias

, 240

linearization method

, 101–102

Normalized asymptotic distribution

, 95

Novel bootstrap procedure

, 211

Null hypothesis

, 111–114, 117, 126

OLS

, 189

median MSE for

, 191–193

Online cash register (OCR)

, 49–50

Organisation for Economic Co-operation and Development (OECD)

, 288

Pairs cluster bootstrap

, 65

Panel data, model-selection tests with

, 124–126

Panel Survey of Income Dynamics

, 110

Payment card acceptance and usage

data description

, 38–39

estimates of card acceptance

, 42–43

estimating card acceptance

, 41

Hungarian payments landscape

, 38

key variables

, 39–40

logistic regression models of card acceptance and usage

, 51–57

methodology

, 41

models of card acceptance and usage

, 41

regression models of card payments

, 43–46

review of literature

, 49–51

“Period treated” dummy

, 64

Planned missing data design

, 239, 242, 244

full-coverage and partial-coverage items

, 248

original 2016 sample counts

, 251

problem of dividing survey form

, 248–249

sampling scheme

, 247

survey for subsample

, 244–245

survey form

, 245–246, 250

survey stratification, counts and rates

, 246

Plug-in method for bandwidth

, 186–187

Poisson distribution

, 132

Poisson quasi-MLE

, 111

Polychotomous choice

, 262

Pooled estimation methods

, 125

Pooled nonlinear least squares

, 125

Pooled objective functions

, 126

Population minimization problem

, 112

Post-stratification

factors

, 15

weights

, 10

Post-stratification weights

, 9

Power series estimators

, 219, 231

Predictor variables

, 188

Primacy effect

, 21–22

Primary sampling units (PSUs)

, 88, 290

Probability

density function

, 124

distributions

, 141

models of illegal sales

, 294

probability-based Internet surveys

, 6

probability-based panels

, 8

Product kernel

for continuous variables

, 179

function for vector of discrete variables

, 179

Propensity score matching

, 223

Pseudo-empirical likelihood objective function

, 93

Pseudo-true values

, 112

Pure design-based inference

, 180

Qin’s treatment

, 148

Quantile estimation

, 217, 220

Quasi-MLEs (QMLE)

, 111, 130

Quasi-true values

, 112

Racial discrimination

, 260, 261, 262, 272, 273, 275

Rademacher distribution

, 76

Raking ratio estimator

, 88–89

classical raking estimator

, 90–92

GREG

, 93–94

maximum likelihood raking estimator

, 92–93

RAND American Life Panel

, 8

RAND-HRS indicator of retirement

, 18

Randomization inference (RI)

, 62, 67

problem of interval p values

, 69–71

Randomized planned missing items

, 239

Randomness

, 88

Re-randomizations

, 69

re-randomized test statistics

, 73

“Realized” population

, 138

Recency effects

, 21–22, 24, 27

Regression analysis

, 288–289

Regression models of card payments

, 43

acceptance

, 43–45

usage

, 45–46

Replication method

, 233

Replication variance

estimation

, 211–212, 218–220

estimator

, 218

Resampling method

, 89

Residual analysis

, 313–314

Respondents

, 22

feedback

, 242

Response linearization method

, 101–102

Response quality improvement

business surveys

, 239–240

challenge

, 242–244

mean number of item responses

, 256

planned missing data design

, 239, 244–251

response counts

, 252

response rates by stratum

, 254

standard approach

, 240–242

survey design

, 251

2016 survey stratification, counts and rates

, 255

unit response rate

, 253

unit responses

, 238

Rubin’s rules

, 312–313

Rule-of-thumb method

, 162–163n14, 177

Salary

, 298

imputation model

, 308

ratio of missing values

, 298

Sample mean squared error

, 190–194

Sample selection

correction

, 262

procedure

, 241

Savings institutions (SAV)

, 241

Scalar matching variable

, 211, 228

Scalar population parameter

, 96

Secondary sampling units (SSUs)

, 290

Selection bias

, 138, 147

Selection correction

, 271

model

, 273

Selective crime reporting

characterization of equilibrium

, 269

coefficient estimation

, 283

empirical strategy

, 269–272

estimates of hit rates test on summons

, 284

existence of equilibrium

, 268

hit rates test

, 262

misclassification

, 260–261

Pedestrians

, 267–268

police officers

, 268

reason for Stop and Pedestrian characteristics

, 285

results

, 272–275

robustness checks for sample correction estimates

, 286

Stop-and-Frisk program

, 263–267

theory

, 267

Self-reported health with CPS limited to HH respondents

, 30

Self-reported mammography

, 277

Self-reported Transaction Price (STP)

, 292, 294–295, 301–302

Sequential importance sampling (SIS)

, 8

Shrinkage empirical-likelihood-based estimator

, 94

Sieves estimation

, 231–233

Sieves estimators

, 219

Simple random sampling (SRS)

, 100–101, 174, 210

median bandwidths for WLC and LC under

, 194

median MSE for WLC, LC, WLS and OLS under

, 191

Simulation study

, 156, 160–161, 221–222

aggregate shares for

, 157

design

, 156–157

inconsistency of LS estimator

, 158, 159

Size

, 292

key variables

, 39

size-based stratas

, 44

Small simulation study

, 129–133

Smoothed empirical likelihood estimator (SEL estimator)

, 152, 153, 154, 159, 162n14, 169

Smoothing parameters

, 186

“Snob effect”

, 288

Social desirability effects

, 22, 24–25

Socioeconomic class

, 297

Socioeconomic variables in HRS, UAS and CPS

, 11–14

Split questionnaire survey design

, 239

Standard likelihood ratio testing principle

, 110

Standard ratio estimators

, 241

Standard stratified sampling (SS sampling)

, 111, 114–121

Stata

, 128

implementation under

, 103

Statistical inference

, 138

Stop-and-Frisk program

, 260–262, 263–267

Strata borders

, 189

Strategic money counterfeiting model

, 290

Stratification

, 44–45, 122, 241

approach

, 42–43

in terms of individual income

, 121

Stratified random samples

, 41

Stratified sampling

, 114, 153

SS sampling

, 114–121

testing under

, 114–122

VP sampling

, 121–122

STRATVAR

, 241, 244

Superpopulation

model

, 215

nonparametric regression model

, 175

parameters

, 181

Survey

of banks

, 240

business

, 239–240

cross-validation

, 187

face-to-face

, 4–5

form

, 245

Internet-based

, 5

large-scale

, 174

modes

, 6

nonmandatory

, 239

online

, 4–5

statistics

, 37–38

telephone surveys

, 4–5

web

, 5

See also 2013 methods-of-payment survey (2013 MOP survey); Internet match high-quality traditional surveys

Survey of Labour and Income Dynamics (SLID)

, 196

Survey outcomes in HRS and UAS

, 14

health economics literature

, 20

health insurance coverage

, 17

home ownership

, 16

individual earnings

, 18

mode effects

, 21–26

post-stratification factors

, 15

self-reported health

, 19

whether retired

, 17

“Synthetic controls” method

, 63

TEÁOR variable

, 52

Temporal attributes of store

, 52

Temporal data

, 56

Time series problems

, 110–111

Tng_credit_year variable

, 100

Tourist test

, 49

Trade policy

, 288

Traditional hit rates test

, 269

Traditional survey modes

, 4

Transaction size distribution

, 45–46

Transaction value

, 45, 55

Transaction-level data

, 50–51

aggregated data

, 39

“Trimmed” SEL objective function

, 155

Trimming indicator

, 155

“True” variance

, 100

Two-step method

, 270, 271

2013 methods-of-payment survey (2013 MOP survey)

, 89, 98

empirical application

, 98–103

raking ratio estimator

, 89–94

variance estimation

, 94–98

UF-250 form

, 264

Unconditional empirical probabilities

, 170

Unconditional summary statistics

, 261

Underrepresents high-income households

, 14

Understanding America Study (UAS)

, 5–6, 7

sampling and weighting procedures

, 8–11

socioeconomic variables in

, 11–14

survey outcomes in HRS and

, 14–26

“Unit nonresponse”

, 297

Unit response rate

, 244–245, 254

Unordered multinomial logit model

, 270

Unplanned missing items

, 239

Unweighted HRS race/ethnicity distribution

, 13

Unweighted life-satisfaction distribution

, 21

Unweighted objective function

, 119

Unweighted statistic

, 131

Unweighted tests

, 133

Urban Area to ZIP Code Tabulation Area Relationship File of US Census Bureau

, 9

Value categories

, 51

Value-added tax

, 303

Variable probability sampling (VP sampling)

, 111, 121–122, 138, 140–142

Variance estimation

, 88, 94, 211

via bootstrap

, 96–98

via linearization

, 95–96

Violent crimes

, 264

Vuong’s approach

, 110, 111

Vuong’s statistic, weighted version of

, 129

Weighted local constant estimation (WLC estimation)

, 189, 191

estimator

, 194, 196

median bandwidths

, 194–196

median MSE for

, 191–193

Weighted/weighting

, 6

life-satisfaction distribution

, 21

objective function

, 122

procedure

, 88

tests

, 133

Wild bootstrap method

, 211, 219

Wild bootstrap randomization inference (WBRI)

, 63, 71

alternative procedures

, 72–74

cluster-robust inference

, 63–67

empirical example

, 79–82

RI

, 62, 67–71

simulation experiments

, 74–79

Wild cluster bootstrap (WCB)

, 62, 65–67

WLS

, 190, 192

median MSE for

, 191–193

Zero mean function

, 202