Index

Citation (2019), "Index", The Econometrics of Complex Survey Data (Advances in Econometrics, Vol. 39), Emerald Publishing Limited, Bingley, pp. 315-325. https://doi.org/10.1108/S0731-905320190000039018 Publisher: Emerald Publishing Limited Copyright © 2019 Emerald Publishing Limited INDEX 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 Book Chapters Prelims Part I Sampling Design Can Internet Match High-quality Traditional Surveys? Comparing the Health and Retirement Study and its Online Version Effectiveness of Stratified Random Sampling for Payment Card Acceptance and Usage Part II Variance Estimation Wild Bootstrap Randomization Inference for Few Treated Clusters Variance Estimation for Survey-Weighted Data Using Bootstrap Resampling Methods: 2013 Methods-of-Payment Survey Questionnaire Part III Estimation and Inference Model-Selection Tests for Complex Survey Samples Inference in Conditional Moment Restriction Models When there is Selection Due to Stratification Nonparametric Kernel Regression Using Complex Survey Data Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling Part IV Applications in Business, Household, and Crime Surveys Improving Response Quality with Planned Missing Data: An Application to a Survey of Banks Does Selective Crime Reporting Influence Our Ability to Detect Racial Discrimination in the Nypd’s Stop-and-Frisk Program? Survey Evidence on Black Market Liquor in Colombia Index

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.