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Hsiao C. — Analysis of panel data
Hsiao C. — Analysis of panel data



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Íàçâàíèå: Analysis of panel data

Àâòîð: Hsiao C.

Àííîòàöèÿ:

Review
"Cheng Hsiao has made many significant and important contributions to panel data econometrics, both methodological and applied, beginning with his 1972 dissertation, in numerous articles, and in his masterful and magisterial 1986 monograph, long a standard reference and popular graduate text. (cont.)

Not only has Hsiao significantly revised the material covered in his original monograph, he has added major new chapters on nonlinear panel models of discrete choice and sample selection, and included new material on the Bayesian treatment of models with fixed and random coefficients, pseudopanels, simulation methods of estimation, and more extensive treatment of dynamic models. Throughout, Hsiao provides applied examples, which greatly enhance the reader's understanding and intuition. The clarity of his exposition and organization is exemplary. All of us who work in the field of panel data econometrics have been, and will now more than ever continue to be in Hsiao's debt." Marc Nerlove, University of Maryland

"The literature on panel data modeling has seen unprecendented growth over the past decade and Cheng Hsiao, himself one of the leading contributors to this literature, is to be congratulated for providing us with a comprehensive and timely update of his classic text. This version not only presents a substantial revision of the 1986 edition, but also offers major additions covering non-linear panel data models dealing with useful overviews of unit root and cointegration in dynamic heterogeneous panels. It should prove invaluable to students and teachers of advanced undergraduate and graduate economic courses." Hashem Perasan, Trinity College, Cambridge

"The first edition of Analysis of Panel Data by Cheng Hsiao has been necessary reading and a landmark for 15 years. The revised and much expanded second edition splendidly integrates the important new developments in the field. One can be sure it will stay a landmark for 15 years to come." Jacques Mairesse, ENSAE, France

"Cheng Hsiao has done a great service to the profession by expanding his highly successful first edition to include the important results that have been obtained by him and other researchers since the publication of the first edition. Escpecially noteworthy in this edition is the application of panel data analysis to qualitative response and sample selection models. Cheng has admirably succeeded in presenting the mathematical results both rigorously and lucidly. Many theoretical results are illustrated by interesting empirical examples. This edition should prove to be an extremely useful reference for the experts in the field as well as graduate students." Takeshi Amemiya, Stanford University

Book Description
Panel data models have become increasingly popular among applied researchers due to their heightened capacity for capturing the complexity of human behavior, as compared to cross-sectional or time series data models. This second edition represents a substantial revision of the highly successful first edition (1986). Recent advances in panel data research are presented in an accessible manner and are carefully integrated with the older material. The thorough discussion of theory and the judicious use of empirical examples make this book useful to graduate students and advanced researchers in economics, business, sociology and political science.


ßçûê: en

Ðóáðèêà: Computer science/

Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö

ed2k: ed2k stats

Èçäàíèå: Second edition

Ãîä èçäàíèÿ: 2003

Êîëè÷åñòâî ñòðàíèö: 384

Äîáàâëåíà â êàòàëîã: 30.05.2006

Îïåðàöèè: Ïîëîæèòü íà ïîëêó | Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
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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 $\pi$ approach      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 $\pi$ approach      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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