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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
Maximum likelihood estimation (MLE) dynamic random-effects models      70 78—83 90—93 95
Maximum likelihood estimation (MLE) fixed-effects models      98—101 327
Maximum likelihood estimation (MLE) fixed-effects probit models      198—199
Maximum likelihood estimation (MLE) hierarchical structures      304
Maximum likelihood estimation (MLE) iterative algorithm for solving      136
Maximum likelihood estimation (MLE) pooled cross-sectional and time-series data      286—290
Maximum likelihood estimation (MLE) random-effects models      101—103
Maximum likelihood estimation (MLE) rotating data      282—283
Maximum likelihood estimation (MLE) semiparametric approach      204
Maximum likelihood estimation (MLE) simple regression with variable intercepts      39—41 57
Maximum likelihood estimation (MLE) simulated      294—295
Maximum likelihood estimation (MLE) simultaneous-equations models      133—136 323
Maximum likelihood estimation (MLE) truncated and censored models      226—227 230 236—237 242
Maximum likelihood estimation (MLE) variable-coefficient models      161—162
Maximum likelihood estimation (MLE) vector autoregressive models      109—111
Maximum score estimators      203—205 212 214—215
Measurement errors      5 304—309 316
Method of moments estimator      generalized method of moments (GMM) estimator 87 266—267
Minimum sufficient statistic      327
Minimum-chi-square estimator      191
Minimum-distance estimator (MDE) asymptotic covariance matrix derivation      111—112
Minimum-distance estimator (MDE) consistency and asymptotic normality of.      65—67
Minimum-distance estimator (MDE) fixed-effects dynamic models      98—101
Minimum-distance estimator (MDE) random-effects models      101—103
Minimum-distance estimator (MDE) simple regression with variable intercepts      63—65 321
Minimum-distance estimator (MDE) simultaneous-equations models      124
Minimum-distance estimator (MDE) single-equation structural models      120
Minimum-distance estimator (MDE) vector autoregressive models      109—111
Minimum-mean-squared-error forecasts      160
Misspecification, tests for      49—51
Monte Carlo design discrete data models      198—199
Monte Carlo design dynamic discrete data models      210—211
Monte Carlo design dynamic fixed-effects models      100—101
Monte Carlo design Gibbs sampler      177—178
Multicollinearity      311—312
Multilevel structures data      302—304
Multivariate probit models      192 200
Mundlak's random-effects formulation      44—46 50
National Longitudinal Surveys of Labor Market Experience (NLS)      1—2
Natural gas demand study      92—95
Newton — Raphson iterative procedure      40 191
Neyman — Scott principle      314
Normal distributions, combination of two      185—187
Normal-distribution theory      158
Omitted variables advantage of panel data in reducing bias      313—315
Omitted variables distributed-lag models      270
Omitted variables dummy variables for      30
Omitted variables pooled time-series and cross-sectional data      286
Omitted variables simultaneous-equation models      127
Ordered variables      191 —192
Ordinary-least-squares (OLS) estimator      31—33 73—74
Orthogonality conditions, in truncated data      264
Pairwise trimmed least-squares (LS) estimator      243—253
Panel data      advantages of panel data attrition problem 234
Panel data (contd.) in regression models      5—7
Panel data (contd.) measurement errors and      5—6
Panel data (contd.) pseudopanels      283—285
Panel data (contd.) selectivity bias      9—11
Panel data (contd.) time-series analysis      7
Panel data before-after effects from      4
Panel data definition      1
Panel data distributed-lag models      5
Panel data efficiency of the estimates      317—318
Panel data heterogeneity bias      8—10
Panel data individual outcome predictions      7—8
Panel data international      3
Panel Study of Income Dynamics (PSID)      1
Panel unit-root tests      297 298—301 329
Panel VAR (PVAR) models      105—111
Panels with large N and T      295—298
Partitioned inverse formula      36
Penn — World tables      295
Period individual-invariant variables      27
Persistence      207
Pooled regression      16—17 319
Pooling cross-section and time-series data      285—290 329
Posterior distributions      168—169
Prediction      158
Predictive density      174
Pricing behavior      150—151
Probability density functions      189—190 201 244—247
Probability distributions discrete data models      189 192—193 196
Probability distributions of attrition      234—238
Probability distributions variable-coefficient models      168—170
Probit models discrete data models      189—190 192 198 200 210 222
Probit models probability of attrition      235
Probit models random-effects      292—293
Qualitative-choice models      discrete data quantal-response
Random-coefficient models      variable-coefficient models
Random-coefficient models coefficients correlated with explanatory variables      149—150
Random-coefficient models dynamic      175—180
Random-coefficient models estimation from      145—147
Random-coefficient models fixed or random coefficients      149—150 153—156
Random-coefficient models manufacturing price equation example      150—151
Random-coefficient models mixed fixed-and random-coefficients models      165—168 184—185
Random-coefficient models model descriptions      144—145 151—153
Random-coefficient models predicting individual coefficients      147
Random-coefficient models stationary vs. nonstationary      142
Random-coefficient models testing for coefficient variation      147—149
Random-effects models bias in the OLS estimator      73—74
Random-effects models discrete data      199—202
Random-effects models efficiency of the estimates      317—318
Random-effects models generalized least-squares estimator      84—85
Random-effects models generalized method of moments estimator      86—90
Random-effects models initial condition hypotheses      90—92
Random-effects models instrumental-variable estimator.      85—86
Random-effects models maximum likelihood estimator      78—83
Random-effects models model formulation      75—78
Random-effects models simulation evidence      91—93
Random-walk model of Cooley and Prescott      157
Regression models      simple regression with variable intercepts censored 249—253
Regression models growth-rate      6—7
Regression models linear      296
Regression models simple      5—6
Regression models truncated      243—249
Regression models variable intercept models      27—30
Regression models, covariance of coefficient homogeneity      21 23
Repeated cross-sectional data (pseudopanels)      283—285
Representative agent assumption      316
Return-to-normality model of Rosenberg      157
Root mean square error (RMSE)      100—102 172—173
Root-N consistent semiparametric estimator      205—206
Rotation of samples      279
Selectivity bias      9—11 235—238
Semiparametric approach to static models      202—206
Semiparametric two-step estimator      253—255
Sequential limits approach      296
Simple regression with variable intercepts      27—68
Simple regression with variable intercepts covariance estimation      35
Simple regression with variable intercepts elasticity estimates      29
Simple regression with variable intercepts firm effects      28—30
Simple regression with variable intercepts fixed or random effects differences      41—49
Simple regression with variable intercepts fixed-effects: least-squares dummy-variable approach      30—33 37
Simple regression with variable intercepts generalized-least-squares estimation      35—38
Simple regression with variable intercepts heteroscedasticity      55—57
Simple regression with variable intercepts Malinvaud minimum-distance estimator      63—65 65—67
Simple regression with variable intercepts maximum likelihood estimation      39—41
Simple regression with variable intercepts models with arbitrary error structure      60—65
Simple regression with variable intercepts models with both individual and time effects      53—55
Simple regression with variable intercepts models with individual-specific variables      51—53
Simple regression with variable intercepts models with serially correlated errors      57—59
Simple regression with variable intercepts Mundlak's formulation      44—46 50
Simple regression with variable intercepts random-effects: variance-components      34—41
Simple regression with variable intercepts tests for misspecification      49—51
Simple regression with variable intercepts types of variables causing inhomogeneities      27
Simple regression with variable intercepts variance-covariance matrix for a three component model      67—68
Simple regression with variable intercepts wage equations example      41—42
Simple regression with variable intercepts, Cobb — Douglas production functions      28—30
Simple regression with variable intercepts, conditional vs. unconditional inferences      43—49
Simple regression with variable intercepts, correlation between individual effects and attributes      46—49
Simulated generalized method of moments (SGMM) estimator      294—295
Simulated maximum likelihood estimator (SMLE)      294—295
Simulation methods      291—295
Simultaneity bias      316
Simultaneous-equations models      113—140
Simultaneous-equations models, attrition models      238
Simultaneous-equations models, estimation of complete structural systems      124—126
Simultaneous-equations models, estimation of single structural equations      199—124
Simultaneous-equations models, income-schooling model      113—114 127—128 136—138
Simultaneous-equations models, instrumental-variable method      130—133
Simultaneous-equations models, joint generalized-least-squares estimation      116—119
Simultaneous-equations models, maximum-likelihood method      133—136
Simultaneous-equations models, triangular system      127—138
Single-equation models      119—124 143
Smoothing parameter      158 205 231
Solow growth model      143
Specification problem      313
Spectral decomposition      302
State dependence, true vs. spurious      216—218 222
Stochastic-parameter models      variable-coefficient models 163—165 166—167 207 322
Stock of capital construction      278—279
Structural equations      119—124 124
Structural parameters      48—49
Survey methodology, of MIP and MIP-S      3
Survey of Income and Program Participation      279—280
Symmetric censoring      228—229
Symmetrically trimmed least-squares estimator      228—229
Three-stage least squares 3SLS) estimator      64 83 124—126
Time-series analysis of nonstationary data      7
Tobin's q      181
Tobit models dynamic censored      7 259—265
Tobit models dynamic sample selection      265—267
Tobit models origin of      225
Tobit models type II      230 241
Tobit models with random individual effects      240—242 292—294
Transformed likelihood approach      96—98 101—103
Triangular system of simultaneous equations estimation      129—136
Triangular system of simultaneous equations identification      127—129
Triangular system of simultaneous equations income-schooling example      136—138
Triangular system of simultaneous equations instrumental-variable method      130—133
Triangular system of simultaneous equations maximum-likelihood method      133— 136
Truncated and censored models      225—267
Truncated and censored models definitions      226
Truncated and censored models dynamic censored Tobit models      7 259—265
Truncated and censored models, censored regression      249—253
Truncated and censored models, dynamic sample selection models      265—267
Truncated and censored models, earnings dynamics example      265
Truncated and censored models, fixed-effects estimator      243—255
Truncated and censored models, Gary income-maintenance project attrition      238—240 242
Truncated and censored models, Heckman two-step estimator      227
Truncated and censored models, housing expenditure example      255—259
Truncated and censored models, nonrandomly missing data example      234—240
Truncated and censored models, overview      225—234
Truncated and censored models, selection factors      231—232 253—255 257—258
Truncated and censored models, semiparametric two-step estimator      253—255
Truncated and censored models, symmetric censoring      228—229
Truncated and censored models, symmetry restoration      260—263
Truncated and censored models, Tobit models with random individual effects      240—242
Truncated and censored models, truncated at zero      227—228
Truncated and censored models, truncated regression      243—249
Truncated model, definition      225
Two-stage least-squares (2SLS) estimator      120—123
Unemployment theory      216
Union-nonunion effects      4
Unit-root tests      297 298—301 329
Unordered variables      191 192
Variable-coefficient models      141—187
Variable-coefficient models, Bayes solution      168—170
Variable-coefficient models, coefficients that are functions of other exogenous variables      163—165
Variable-coefficient models, coefficients that evolve over time      156—163
Variable-coefficient models, coefficients that vary over cross-sectional units      143—151
Variable-coefficient models, coefficients that vary over time and cross-sectional units      151—156
Variable-coefficient models, correlation with explanatory variables      149
Variable-coefficient models, dynamic random-coefficient models      175—180
Variable-coefficient models, electricity demand example      172—173
Variable-coefficient models, Kalman filter      158—161
Variable-coefficient models, liquidity constraints and firm investment expenditure example      180—185
Variable-coefficient models, long-haul long-distance service example      171—172
Variable-coefficient models, manufacturing price equation example      150—151
Variable-coefficient models, maximum likelihood estimation      161—162
Variable-coefficient models, mixed fixed and random-coefficients models      165—168 184—185
Variable-coefficient models, model selection      173—175
Variable-coefficient models, testing for coefficient variation      147—149 156 162—163 172—175 182
Variance components      34
Variance-covariance matrix for rotating data      281
Variance-covariance matrix for simultaneous-equations models      125—126
Variance-covariance matrix for three component models      67—68
Variance-covariance matrix for variable-coefficient models      154 163—164
Variance-covariance matrix in single equation structural models      121
Variance-covariance matrix, discrete data      327
Variance-covariance matrix, minimum-distance estimator      109—111
Variance-covariance matrix, model formulation      105—107
Variance-covariance matrix, multilevel structures      302
Variance-covariance matrix, no restrictions in simultaneous-equations models      116—119
Variance-covariance matrix, pooled cross-sectional and time-series data      287—288
Variance-covariance matrix, restrictions in simultaneous-equations models      127—129 132—133
Variance-covariance matrix, transformed MLE      109
Vector autoregressive (VAR) models GMM estimation      107—108
Wage equations      41—42
Wishart distribution      177
Within-group estimates      16 19 320
World Bank, panel surveys sponsored by      3
Yogurt brand loyalty      221—224
Zellner regression model      144
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