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Ïîèñê ïî óêàçàòåëÿì
Hsiao C. — Analysis of panel data
Îáñóäèòå êíèãó íà íàó÷íîì ôîðóìå Íàøëè îïå÷àòêó?
Íàçâàíèå: Analysis of panel dataÀâòîð: Hsiao C. Àííîòàöèÿ: Review
ßçûê: Ðóáðèêà: Computer science /Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö ed2k: ed2k stats Èçäàíèå: Second editionÃîä èçäàíèÿ: 2003Êîëè÷åñòâî ñòðàíèö: 384Äîáàâëåíà â êàòàëîã: 30.05.2006Îïåðàöèè: Ïîëîæèòü íà ïîëêó |
Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
Ïðåäìåòíûé óêàçàòåëü
Acceleration sales model 22 Adaptations in behavior 268—269 Advantages of panel data competing hypotheses 312—313 Advantages of panel data degrees of freedom and multicollinearity 311—312 Advantages of panel data distributed-lag model estimation 5 Advantages of panel data dynamics of change 3—4 Advantages of panel data estimation bias reduction 313—316 Advantages of panel data measurement errors 5—6 Advantages of panel data micro foundations for aggregate data analysis 316 Advantages of panel data two-dimensional nature of data 7—8 Aitken estimator 123 150 155 165 320 Analysis of covariance 14—26 Analysis of covariance descriptive model 14—15 Analysis of covariance example from Kuh 21—26 Analysis of covariance main steps in 15 Analysis of covariance one-way 150 Analysis of covariance problems in tracing heterogeneity 20—21 Analysis of covariance regression over individuals 15—18 23 30—33 Analysis of covariance regression over time 18—20 24—25 Analysis of covariance summary of tests for homogeneity 19 Analysis of variance 14 Argument Dickey — Fuller (ADF) t-ratios 301 Attrition probability 234—238 Autoregressive models in short panels vector autoregressive models 268 275 Autoregressive moving-average (ARMA) 157 Bandwidth parameter 231 249 Bayes estimators 146 170 177—180 Bayes solutions 168—170 174—175 Bayes updating formula 175 Bernoulli models 207 Best linear unbiased estimator (BLUE) in variable-coefficient models 154—155 164 Best linear unbiased estimator (BLUE) in variable-intercept models 31 33 35 45 53 55 Best linear unbiased predictor (BLUP) 170 Between-group estimators 37 Bias attrition 239 Bias Bayes estimator 178—180 Bias covariance estimator 72 Bias fixed-effects probit models 198—199 Bias from dynamic structure 315—316 Bias generalized least-squares estimator 85 Bias generalized method of moments 90 101—102 Bias heterogeneous intercepts 9—10 Bias income-schooling model 113—114 127 Bias IV estimator 101—102 Bias maximum likelihood estimation 91—93 101—102 211 Bias measurement errors 5 304—309 316 Bias minimum-distance estimator 101 —102 Bias OLS estimator 73—74 Bias omitted-variable 313—315 Bias selectivity 9—11 254 Bias simultaneity 316 Box-Jenkins method 163 Capital intensity 183 Cash-flow effect 26 180—181 Categorical models discrete data cell-mean corrected regression models 16—17 23 Censored model, definition truncated and censored models 225 Chamberlain 60—65 Chamberlain minimum-distance estimator 83 Clustering structure 302—304 Cobb — Douglas production functions 28—29 Cohorts 283—285 329 Cointegration rank 323 Competing hypotheses 312—313 COMPUSTAT data 181 Conditional vs. marginal likelihood functions likelihood functions 43—49 Correlations, arbitrary 103—104 Covariance (CV) estimator dynamic panel data models 71—72 Covariance (CV) estimator random-vs. fixed-effects models 69—70 Covariance (CV) estimator simple regression with variable intercept models 35 53 55 Covariance transformation, measurement error and 305—306 Cramer — Rao bounds 50 Cross-sectional data consisting of entire population 329 Cross-sectional data difficulties of describing dynamics of change from 4 Cross-sectional data pooled with time-series data 285—290 329 Cross-sectional data repeated 283—285 Cross-sectional dependence 309—310 Cumulative normal distribution 190 Degrees of freedom 311—312 Depreciation rate 95 Diagonal-path limits approach 296 Discrete data dynamic discrete data models definition 188 Discrete data dependent variable assuming only two values 188—189 Discrete data discrete-response models 188—193 Discrete data dynamic models 206—224 Discrete data existence of a consistent estimator. 195—198 Discrete data female employment example 193 218—222 Discrete data fixed-effects models 194—199 Discrete data household brand choices example 221—224 Discrete data maximum likelihood estimator 194—195 Discrete data Monte Carlo evidence 198—199 Discrete data parametric approach to static models with heterogeneity 193—202 Discrete data random-effects models 199—202 Discrete data semiparametric approach to static models 202—206 Discrete data state dependence vs. heterogeneity 216—218 222 Discrete data unemployment theory 216 Distributed lag estimation in short panels 268—279 Distributed lag estimation in short panels common assumptions 270—271 Distributed lag estimation in short panels, estimation and testing 277—279 Distributed lag estimation in short panels, general distributed-lag model 269 270 271 Distributed lag estimation in short panels, identification using lag coefficients 275—277 Distributed lag estimation in short panels, identification using the exogenous variable 271—275 Distributed lag estimation in short panels, progressive nature of adaptations 268—269 Distributed lag estimation in short panels, rates of return example 278—279 Distributed-lag models 5 269 270 271 Distributions, combination of normal 185—187 Dummy variables, least-squares least-squares dummy-variables Dynamic censored Tobit models 259—265 Dynamic discrete data models 206—224 Dynamic discrete data models conditional probability approach 211—216 Dynamic discrete data models general model 106—108 Dynamic discrete data models, household brand choices example 221—224 Dynamic discrete data models, initial conditions 208—211 Dynamic discrete data models, state dependence vs. heterogeneity 216—218 222 Dynamic discrete data models, unemployment theory 216 Dynamic models with variable intercepts fixed-effects models; random-effects models 69—112 Dynamic models with variable intercepts, arbitrary correlations in the residuals 103—104 Dynamic models with variable intercepts, asymptotic covariance matrix derivation 111—112 Dynamic models with variable intercepts, covariance estimator 69—70 71—72 Dynamic models with variable intercepts, fixed-effects models 72 95—103 Dynamic models with variable intercepts, fixed-effects vector autoregressive models 105—111 Dynamic models with variable intercepts, initial conditions 70 85—86 90—92 Dynamic models with variable intercepts, maximum likelihood estimator 78—83 Dynamic models with variable intercepts, random-effects models 73—92 Dynamic models, bias induced by 315—316 Dynamic random-coefficient models 175—180 Dynamic sample selection Tobit models 265—267 Earnings dynamics example 265 Economic distance 310 Efficiency of estimates 317—318 Elasticity estimates 28—29 Electricity demand example 172—173 Employment examples female 193 218—222 Employment examples income-schooling model 113—114 127—128 136—138 313 Employment examples truncated or censored data 229—230 Employment examples unemployment theory 216 Employment examples wage equations 41—42 Endogenously determined sample selection model 253—255 Error terms Chamberlain 60—65 Error terms discrete-response models 192—193 206—207 220 Error terms quadratic loss functions 169 Error terms serially correlated 57—59 Error terms simultaneous-equations models 122—123 126 138 Error terms truncated and censored models 226 228 Error terms variable-coefficient models 146 153—155 160 167—170 Error-component three-stage least squares (EC2SLS) estimator 126 280 Error-component two-stage least squares (EC2SLS) estimator 123 126 Errors of measurement 5 304—309 316 Estimation bias 313—316 Europe, panel data sets from 1—3 European Community Household Panel (ECHP) 3 319 Event histories 328 Exogeneity, strict 43—44 49 69 70 95 104 203 265 Exogeneity, weak 95 Female employment examples 193 218—222 Filtering 158 Fixed-effects models 95—103 Fixed-effects models, discrete data 194 199 Fixed-effects models, efficiency of the estimates 317 Fixed-effects models, likelihood-based estimator and GMM relations 99—101 Fixed-effects models, logit models 327 Fixed-effects models, minimum-distance estimator 98—99 Fixed-effects models, omitted variable bias 314 Fixed-effects models, probit models 198—199 Fixed-effects models, random-vs. fixed-effects specification 101—103 Fixed-effects models, semiparametric two-step estimator 253—255 Fixed-effects models, transformed likelihood approach 96—98 101—103 Fixed-effects models, truncated regression 243—249 Fixed-effects models, vector autoregressive models 105—111 Food demand example 288—290 Gary income-maintenance project, attrition in 238—240 242 Gaussian quadrature formula 201 Generalized method of moments (GMM) estimator kernel-weighted 266—267 Generalized method of moments (GMM) estimator likelihood-based estimators and 99—101 Generalized method of moments (GMM) estimator, measurement errors 306—309 Generalized method of moments (GMM) estimator, random-effects models 86—90 95—96 Generalized method of moments (GMM) estimator, simple regression with variable intercepts 60 Generalized method of moments (GMM) estimator, simulated 294—295 Generalized method of moments (GMM) estimator, truncated or censored models 264—265 Generalized method of moments (GMM) estimator, vector autoregressive models 107—108 Generalized-least-squares (GLS) estimator dynamic random-effects models 84—85 323 Generalized-least-squares (GLS) estimator, multilevel structures 303 Generalized-least-squares (GLS) estimator, rotating data 281—282 Generalized-least-squares (GLS) estimator, simple regression with variable intercepts 35—38 45 53 55 58—59 320 Generalized-least-squares (GLS) estimator, simultaneous-equations models 117—118 125—126 Generalized-least-squares (GLS) estimator, variable-coefficients models 145—146 154 164 170 Gibbs sampler 177—178 Group membership matrices 304 Growth-rate regression model 6—7 Grunfeld investment function 147 Hausman test of misspecification 50—51 102 321 Hausman wage equations 42 Hausman — Wise model of attrition 237—240 242 Heckman sample selection correction 254 Heckman two-step estimator 227 230 236 241 Heckman — Willis model 219 221 Hermite integration formula 201 Heterogeneity bias 8—10 Heterogeneity female employment example 218—222 Heterogeneity problems in tracing 20—21 Heterogeneity state dependence and 216—218 222 Heteroscedasticity in random-effects models 89 Heteroscedasticity in simple regression with variable intercepts models 55—57 Heteroscedasticity in simultaneous-equations models 124 Heteroscedasticity in single-equation structural models 120 122 Heteroscedasticity in truncated or censored models 255 Heteroscedasticity in variable-coefficient models 148 156 162 Heteroscedasticity individual 320 Heteroscedasticity Lagrange-multiplier test 148 156 162 325 Heteroscedasticity unit-root tests 299 Hierarchical structure 302—304 Hildreth — Houck estimator 325 Honore — Kyriazidou estimator 223—224 Household brand choices example 221—224 Housing expenditure example 255—259 Identification dynamic random-coefficients models 326 Identification triangular system of simultaneous equations 127—129 Identification using prior structure of the lag coefficients 275—277 Identification using prior structure of the process of the exogenous variable 271—275 Incidental-parameters 48—49 81 95 96 107 194—196 204 244 Income elasticity example 285—286 289 Income-schooling model 113—114 127—128 136—138 313 Incomplete panel data 268—290 Incomplete panel data distributed lag estimation in short panels 268—279 Incomplete panel data, food demand example 288—290 Incomplete panel data, income elasticity example 285—286 289 Incomplete panel data, pooling of cross-section and time-series data 285—290 Incomplete panel data, pseudopanels 283—285 Incomplete panel data, rotating or randomly missing data 279—283 Independence of irrelevant alternatives 192 Individual time-invariant variables 27 Individual time-varying variables 27 Individual-mean corrected regression model 16—17 Information matrix (IV) 148—149 197 Initial conditions common vs. difference means 91 Initial conditions, dynamic discrete data models 208—211 Initial conditions, dynamic models with variable intercepts 70 85—86 90—91 Initial conditions, incomplete panel data 283 Initial conditions, likelihood functions and 90—92 Instrumental-variable (IV) estimator dynamic fixed-effects models 100 Instrumental-variable (IV) estimator dynamic random-effects models 85—86 95—96 Instrumental-variable (IV) estimator purged, in triangular simultaneous-equations models 130—133 Inverse gamma (IG) distribution investment functions analysis of covariance of 21—26 Investment functions variable-coefficient models 141—142 147 180—185 Investment functions, importance of increase in sales 26 Joint limits approach 296 Kalman filter 158—161 Kernel density functions 214 231—233 255 Kernel-weighted generalized method-of-moments (KGMM) estimator 266—267 Koyck lag model 276—277 Kronecker product of two matrices 54 321 Kyriazidou estimator 253—255 256 266 Labor supply, life-cycle 6 Lagged dependent variables 70 74 85 Lagrange-multiplier test for heteroscedasticity 148 156 162 325 Latent response functions 225 292 Latent variables 127 Least-absolute deviation (LAD) estimator 243—253 Least-squares dummy-variables (LSDV) dummy variables for omitted variables 30 Least-squares dummy-variables (LSDV) dynamic panel data models 71—72 Least-squares dummy-variables (LSDV) fixed-effects models 30—33 Least-squares dummy-variables (LSDV) random-effects models 37 Least-squares dummy-variables (LSDV) simultaneous-equations models 118 Least-squares dummy-variables (LSDV) variable-coefficient models 142 Least-squares estimators generalized-least-squares estimator joint generalized-least-squares estimation 116—119 Least-squares estimators ordinary 31—33 73—74 Least-squares estimators, pairwise trimmed 243—253 Least-squares estimators, symmetrically trimmed 228—229 Least-squares estimators, three-stage 64 83 124—126 Least-squares estimators, two-stage 120—123 Least-squares estimators, variable-coefficient models 146 Least-squares regression 5—6 Lee estimator 205—206 Likelihood functions maximum likelihood estimation Likelihood functions GMM and 99—101 Likelihood functions pooled cross-sectional and time-series data 286—290 Likelihood functions, conditional vs. marginal 43—49 Likelihood functions, discrete-response models 190—192 194—198 Likelihood functions, dynamic discrete data models 209—211 Likelihood functions, fixed effects models 209 Likelihood functions, random-effects models 199—202 209 Likelihood functions, randomly missing data 283 Likelihood functions, rotating samples 280 Likelihood functions, testing initial condition hypotheses 90—91 Likelihood functions, transformed approach 96—98 101—103 Likelihood functions, truncated and censored models 226 230 237—238 240—242 Limited-information principle 120 122 Linear regression 296 314 Linear-probability models 189—190 195—196 Liquidity constraints example 180—185 Logistic distribution 190 Logit models 189—190 195—198 215 222—223 327 Long-haul long-distance service 171—172 Manheim Innovation Panel (MIP) 3 Manheim Innovation Panel—Service Sector (MIP-S) 3 Manski maximum score estimator 203—205 212 214 Manufacturing price equations 150—151 Markov processes 207—208 210—211 219—220 327 Maximum likelihood estimation (MLE) bias simulation evidence 91—93 Maximum likelihood estimation (MLE) consistency properties 80 Maximum likelihood estimation (MLE) discrete data models 191 194—196 217 219
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