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Hsiao C. Ч Analysis of panel data
Hsiao C. Ч Analysis of panel data

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Ќазвание: Analysis of panel data

јвтор: Hsiao C.

јннотаци€:

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.


язык: en

–убрика: Computer science/

—татус предметного указател€: √отов указатель с номерами страниц

ed2k: ed2k stats

»здание: Second edition

√од издани€: 2003

 оличество страниц: 384

ƒобавлена в каталог: 30.05.2006

ќперации: ѕоложить на полку | —копировать ссылку дл€ форума | —копировать ID
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ѕредметный указатель
Acceleration sales model      22
Adaptations in behavior      268Ч269
Advantages of panel data competing hypotheses      312Ч313
Advantages of panel data degrees of freedom and multicollinearity      311Ч312
Advantages of panel data distributed-lag model estimation      5
Advantages of panel data dynamics of change      3Ч4
Advantages of panel data estimation bias reduction      313Ч316
Advantages of panel data measurement errors      5Ч6
Advantages of panel data micro foundations for aggregate data analysis      316
Advantages of panel data two-dimensional nature of data      7Ч8
Aitken estimator      123 150 155 165 320
Analysis of covariance      14Ч26
Analysis of covariance descriptive model      14Ч15
Analysis of covariance example from Kuh      21Ч26
Analysis of covariance main steps in      15
Analysis of covariance one-way      150
Analysis of covariance problems in tracing heterogeneity      20Ч21
Analysis of covariance regression over individuals      15Ч18 23 30Ч33
Analysis of covariance regression over time      18Ч20 24Ч25
Analysis of covariance summary of tests for homogeneity      19
Analysis of variance      14
Argument Dickey Ч Fuller (ADF) t-ratios      301
Attrition probability      234Ч238
Autoregressive models in short panels      vector autoregressive models 268 275
Autoregressive moving-average (ARMA)      157
Bandwidth parameter      231 249
Bayes estimators      146 170 177Ч180
Bayes solutions      168Ч170 174Ч175
Bayes updating formula      175
Bernoulli models      207
Best linear unbiased estimator (BLUE) in variable-coefficient models      154Ч155 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      178Ч180
Bias covariance estimator      72
Bias fixed-effects probit models      198Ч199
Bias from dynamic structure      315Ч316
Bias generalized least-squares estimator      85
Bias generalized method of moments      90 101Ч102
Bias heterogeneous intercepts      9Ч10
Bias income-schooling model      113Ч114 127
Bias IV estimator      101Ч102
Bias maximum likelihood estimation      91Ч93 101Ч102 211
Bias measurement errors      5 304Ч309 316
Bias minimum-distance estimator      101 Ч102
Bias OLS estimator      73Ч74
Bias omitted-variable      313Ч315
Bias selectivity      9Ч11 254
Bias simultaneity      316
Box-Jenkins method      163
Capital intensity      183
Cash-flow effect      26 180Ч181
Categorical models      discrete data cell-mean corrected regression models 16Ч17 23
Censored model, definition      truncated and censored models 225
Chamberlain $\pi$ approach      60Ч65
Chamberlain minimum-distance estimator      83
Clustering structure      302Ч304
Cobb Ч Douglas production functions      28Ч29
Cohorts      283Ч285 329
Cointegration rank      323
Competing hypotheses      312Ч313
COMPUSTAT data      181
Conditional vs. marginal likelihood functions      likelihood functions 43Ч49
Correlations, arbitrary      103Ч104
Covariance (CV) estimator dynamic panel data models      71Ч72
Covariance (CV) estimator random-vs. fixed-effects models      69Ч70
Covariance (CV) estimator simple regression with variable intercept models      35 53 55
Covariance transformation, measurement error and      305Ч306
Cramer Ч 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      285Ч290 329
Cross-sectional data repeated      283Ч285
Cross-sectional dependence      309Ч310
Cumulative normal distribution      190
Degrees of freedom      311Ч312
Depreciation rate      95
Diagonal-path limits approach      296
Discrete data      dynamic discrete data models definition 188
Discrete data dependent variable assuming only two values      188Ч189
Discrete data discrete-response models      188Ч193
Discrete data dynamic models      206Ч224
Discrete data existence of a consistent estimator.      195Ч198
Discrete data female employment example      193 218Ч222
Discrete data fixed-effects models      194Ч199
Discrete data household brand choices example      221Ч224
Discrete data maximum likelihood estimator      194Ч195
Discrete data Monte Carlo evidence      198Ч199
Discrete data parametric approach to static models with heterogeneity      193Ч202
Discrete data random-effects models      199Ч202
Discrete data semiparametric approach to static models      202Ч206
Discrete data state dependence vs. heterogeneity      216Ч218 222
Discrete data unemployment theory      216
Distributed lag estimation in short panels      268Ч279
Distributed lag estimation in short panels common assumptions      270Ч271
Distributed lag estimation in short panels, estimation and testing      277Ч279
Distributed lag estimation in short panels, general distributed-lag model      269 270 271
Distributed lag estimation in short panels, identification using lag coefficients      275Ч277
Distributed lag estimation in short panels, identification using the exogenous variable      271Ч275
Distributed lag estimation in short panels, progressive nature of adaptations      268Ч269
Distributed lag estimation in short panels, rates of return example      278Ч279
Distributed-lag models      5 269 270 271
Distributions, combination of normal      185Ч187
Dummy variables, least-squares      least-squares dummy-variables
Dynamic censored Tobit models      259Ч265
Dynamic discrete data models      206Ч224
Dynamic discrete data models conditional probability approach      211Ч216
Dynamic discrete data models general model      106Ч108
Dynamic discrete data models, household brand choices example      221Ч224
Dynamic discrete data models, initial conditions      208Ч211
Dynamic discrete data models, state dependence vs. heterogeneity      216Ч218 222
Dynamic discrete data models, unemployment theory      216
Dynamic models with variable intercepts      fixed-effects models; random-effects models 69Ч112
Dynamic models with variable intercepts, arbitrary correlations in the residuals      103Ч104
Dynamic models with variable intercepts, asymptotic covariance matrix derivation      111Ч112
Dynamic models with variable intercepts, covariance estimator      69Ч70 71Ч72
Dynamic models with variable intercepts, fixed-effects models      72 95Ч103
Dynamic models with variable intercepts, fixed-effects vector autoregressive models      105Ч111
Dynamic models with variable intercepts, initial conditions      70 85Ч86 90Ч92
Dynamic models with variable intercepts, maximum likelihood estimator      78Ч83
Dynamic models with variable intercepts, random-effects models      73Ч92
Dynamic models, bias induced by      315Ч316
Dynamic random-coefficient models      175Ч180
Dynamic sample selection Tobit models      265Ч267
Earnings dynamics example      265
Economic distance      310
Efficiency of estimates      317Ч318
Elasticity estimates      28Ч29
Electricity demand example      172Ч173
Employment examples female      193 218Ч222
Employment examples income-schooling model      113Ч114 127Ч128 136Ч138 313
Employment examples truncated or censored data      229Ч230
Employment examples unemployment theory      216
Employment examples wage equations      41Ч42
Endogenously determined sample selection model      253Ч255
Error terms Chamberlain $\pi$ approach      60Ч65
Error terms discrete-response models      192Ч193 206Ч207 220
Error terms quadratic loss functions      169
Error terms serially correlated      57Ч59
Error terms simultaneous-equations models      122Ч123 126 138
Error terms truncated and censored models      226 228
Error terms variable-coefficient models      146 153Ч155 160 167Ч170
Error-component three-stage least squares (EC2SLS) estimator      126 280
Error-component two-stage least squares (EC2SLS) estimator      123 126
Errors of measurement      5 304Ч309 316
Estimation bias      313Ч316
Europe, panel data sets from      1Ч3
European Community Household Panel (ECHP)      3 319
Event histories      328
Exogeneity, strict      43Ч44 49 69 70 95 104 203 265
Exogeneity, weak      95
Female employment examples      193 218Ч222
Filtering      158
Fixed-effects models      95Ч103
Fixed-effects models, discrete data      194 199
Fixed-effects models, efficiency of the estimates      317
Fixed-effects models, likelihood-based estimator and GMM relations      99Ч101
Fixed-effects models, logit models      327
Fixed-effects models, minimum-distance estimator      98Ч99
Fixed-effects models, omitted variable bias      314
Fixed-effects models, probit models      198Ч199
Fixed-effects models, random-vs. fixed-effects specification      101Ч103
Fixed-effects models, semiparametric two-step estimator      253Ч255
Fixed-effects models, transformed likelihood approach      96Ч98 101Ч103
Fixed-effects models, truncated regression      243Ч249
Fixed-effects models, vector autoregressive models      105Ч111
Food demand example      288Ч290
Gary income-maintenance project, attrition in      238Ч240 242
Gaussian quadrature formula      201
Generalized method of moments (GMM) estimator kernel-weighted      266Ч267
Generalized method of moments (GMM) estimator likelihood-based estimators and      99Ч101
Generalized method of moments (GMM) estimator, measurement errors      306Ч309
Generalized method of moments (GMM) estimator, random-effects models      86Ч90 95Ч96
Generalized method of moments (GMM) estimator, simple regression with variable intercepts      60
Generalized method of moments (GMM) estimator, simulated      294Ч295
Generalized method of moments (GMM) estimator, truncated or censored models      264Ч265
Generalized method of moments (GMM) estimator, vector autoregressive models      107Ч108
Generalized-least-squares (GLS) estimator dynamic random-effects models      84Ч85 323
Generalized-least-squares (GLS) estimator, multilevel structures      303
Generalized-least-squares (GLS) estimator, rotating data      281Ч282
Generalized-least-squares (GLS) estimator, simple regression with variable intercepts      35Ч38 45 53 55 58Ч59 320
Generalized-least-squares (GLS) estimator, simultaneous-equations models      117Ч118 125Ч126
Generalized-least-squares (GLS) estimator, variable-coefficients models      145Ч146 154 164 170
Gibbs sampler      177Ч178
Group membership matrices      304
Growth-rate regression model      6Ч7
Grunfeld investment function      147
Hausman test of misspecification      50Ч51 102 321
Hausman wage equations      42
Hausman Ч Wise model of attrition      237Ч240 242
Heckman sample selection correction      254
Heckman two-step estimator      227 230 236 241
Heckman Ч Willis model      219 221
Hermite integration formula      201
Heterogeneity bias      8Ч10
Heterogeneity female employment example      218Ч222
Heterogeneity problems in tracing      20Ч21
Heterogeneity state dependence and      216Ч218 222
Heteroscedasticity in random-effects models      89
Heteroscedasticity in simple regression with variable intercepts models      55Ч57
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      302Ч304
Hildreth Ч Houck estimator      325
Honore Ч Kyriazidou estimator      223Ч224
Household brand choices example      221Ч224
Housing expenditure example      255Ч259
Identification dynamic random-coefficients models      326
Identification triangular system of simultaneous equations      127Ч129
Identification using prior structure of the lag coefficients      275Ч277
Identification using prior structure of the process of the exogenous variable      271Ч275
Incidental-parameters      48Ч49 81 95 96 107 194Ч196 204 244
Income elasticity example      285Ч286 289
Income-schooling model      113Ч114 127Ч128 136Ч138 313
Incomplete panel data      268Ч290
Incomplete panel data distributed lag estimation in short panels      268Ч279
Incomplete panel data, food demand example      288Ч290
Incomplete panel data, income elasticity example      285Ч286 289
Incomplete panel data, pooling of cross-section and time-series data      285Ч290
Incomplete panel data, pseudopanels      283Ч285
Incomplete panel data, rotating or randomly missing data      279Ч283
Independence of irrelevant alternatives      192
Individual time-invariant variables      27
Individual time-varying variables      27
Individual-mean corrected regression model      16Ч17
Information matrix (IV)      148Ч149 197
Initial conditions common vs. difference means      91
Initial conditions, dynamic discrete data models      208Ч211
Initial conditions, dynamic models with variable intercepts      70 85Ч86 90Ч91
Initial conditions, incomplete panel data      283
Initial conditions, likelihood functions and      90Ч92
Instrumental-variable (IV) estimator dynamic fixed-effects models      100
Instrumental-variable (IV) estimator dynamic random-effects models      85Ч86 95Ч96
Instrumental-variable (IV) estimator purged, in triangular simultaneous-equations models      130Ч133
Inverse gamma (IG) distribution investment functions analysis of covariance of      21Ч26
Investment functions variable-coefficient models      141Ч142 147 180Ч185
Investment functions, importance of increase in sales      26
Joint limits approach      296
Kalman filter      158Ч161
Kernel density functions      214 231Ч233 255
Kernel-weighted generalized method-of-moments (KGMM) estimator      266Ч267
Koyck lag model      276Ч277
Kronecker product of two matrices      54 321
Kyriazidou estimator      253Ч255 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      243Ч253
Least-squares dummy-variables (LSDV) dummy variables for omitted variables      30
Least-squares dummy-variables (LSDV) dynamic panel data models      71Ч72
Least-squares dummy-variables (LSDV) fixed-effects models      30Ч33
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 116Ч119
Least-squares estimators ordinary      31Ч33 73Ч74
Least-squares estimators, pairwise trimmed      243Ч253
Least-squares estimators, symmetrically trimmed      228Ч229
Least-squares estimators, three-stage      64 83 124Ч126
Least-squares estimators, two-stage      120Ч123
Least-squares estimators, variable-coefficient models      146
Least-squares regression      5Ч6
Lee estimator      205Ч206
Likelihood functions      maximum likelihood estimation
Likelihood functions GMM and      99Ч101
Likelihood functions pooled cross-sectional and time-series data      286Ч290
Likelihood functions, conditional vs. marginal      43Ч49
Likelihood functions, discrete-response models      190Ч192 194Ч198
Likelihood functions, dynamic discrete data models      209Ч211
Likelihood functions, fixed effects models      209
Likelihood functions, random-effects models      199Ч202 209
Likelihood functions, randomly missing data      283
Likelihood functions, rotating samples      280
Likelihood functions, testing initial condition hypotheses      90Ч91
Likelihood functions, transformed approach      96Ч98 101Ч103
Likelihood functions, truncated and censored models      226 230 237Ч238 240Ч242
Limited-information principle      120 122
Linear regression      296 314
Linear-probability models      189Ч190 195Ч196
Liquidity constraints example      180Ч185
Logistic distribution      190
Logit models      189Ч190 195Ч198 215 222Ч223 327
Long-haul long-distance service      171Ч172
Manheim Innovation Panel (MIP)      3
Manheim Innovation PanelЧService Sector (MIP-S)      3
Manski maximum score estimator      203Ч205 212 214
Manufacturing price equations      150Ч151
Markov processes      207Ч208 210Ч211 219Ч220 327
Maximum likelihood estimation (MLE) bias simulation evidence      91Ч93
Maximum likelihood estimation (MLE) consistency properties      80
Maximum likelihood estimation (MLE) discrete data models      191 194Ч196 217 219
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