Panel Analysis for Metric Data

A cross-sectional data set refers to observations on a number of individuals at a given time. A time-series data set refers to observations made over time on a given unit. A panel (or longitudinal or temporal cross-sectional) data set follows a number of individuals over time. In recent years empirical studies that use panel data have become common. This is partly because the cost of developing panel or longitudinal data sets is no longer prohibitive. In some cases, computerized matching of existing administrative records can produce inexpensive longitudinal information, such as the Social Security Administration’s Continuous Work History Sample (CWHS). In other cases, valuable longitudinal data bases can be generated by computerized matching of existing administrative and survey data, such as the University of Michigan’s Panel Study of Income Dynamics (PSID) and the U.S. Current Population Survey. Even in cases where the desired longitudinal information can be collected only by initiating new surveys, such as the series of negative income tax experiments in the United States and Canada, the advance of computerized data management systems has made longitudinal data development cost-effective in the last 20 years (Ashenfelter and Solon 1982).

[1]  Cheng Hsiao,et al.  A general framework for panel data models with an application to Canadian customer-dialed long distance telephone service , 1993 .

[2]  Edwin Kuh,et al.  Capital stock growth : a micro-econometric Approach , 1963 .

[3]  T. Wansbeek,et al.  A Note on Spectral Decomposition and Maximum Likelihood Estimation in ANOVA Models with Balanced Data , 1983 .

[4]  Cheng Hsiao A Mixed Fixed and Random Coefficients Framework for Pooling Cross-Section and Time Series Data , 1991 .

[5]  Jerry A. Hausman,et al.  Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment , 1979 .

[6]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[7]  T. Amemiya Tobit models: A survey , 1984 .

[8]  B. D. Finetti,et al.  Foresight: Its Logical Laws, Its Subjective Sources , 1992 .

[9]  Nicholas M. Kiefer,et al.  Population Heterogeneity and Inference from Panel Data on the Effects of Vocational Education , 1979, Journal of Political Economy.

[10]  Heckman Simple Statistical Models for Discrete Panel Data Developed and Applied to Test the Hypothesis of True State Dependence against the Hypothesis of Spurious State Dependence , 1978 .

[11]  G. Judge,et al.  The Theory and Practice of Econometrics (2nd ed.). , 1986 .

[12]  Cheng Hsiao,et al.  Statistical Inference for a Model with Both Random Cross-Sectional and Time Effects , 1974 .

[13]  Orley Ashenfelter,et al.  Longitudinal Labor Market Data: Sources, Uses, and Limitations , 1982 .

[14]  Jerry A. Hausman,et al.  Errors in Variables in Panel Data , 1984 .

[15]  J. Heckman Sample selection bias as a specification error , 1979 .

[16]  Cheng Hsiao Measurement Error in a Dynamic Simultaneous Equations Model with Stationary Disturbances , 1979 .

[17]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[18]  G. Judge,et al.  The Theory and Practice of Econometrics , 1981 .

[19]  Cheng Hsiao,et al.  Identification for a Linear Dynamic Simultaneous Error-Shock Model , 1977 .

[20]  A. Zellner An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias , 1962 .

[21]  Cheng Hsiao,et al.  Estimation of Dynamic Models with Error Components , 1981 .

[22]  Cheng Hsiao,et al.  Latent variable models in econometrics , 1984 .

[23]  J. Hausman Specification tests in econometrics , 1978 .

[24]  J. Neyman,et al.  Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability , 1963 .

[25]  A. F. Smith A General Bayesian Linear Model , 1973 .

[26]  Henry E. Kyburg,et al.  Studies in Subjective Probability , 1965 .

[27]  Cheng Hsiao,et al.  MODELING ONTARIO REGIONAL ELECTRICITY SYSTEM DEMAND USING A MIXED FIXED AND RANDOM COEFFICIENTS APPROACH , 1989 .

[28]  Chamberlain Omitted Variable Bias in Panel Data: Estimating the Returns to Schooling , 1978 .

[29]  Adrian Pagan,et al.  The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics , 1980 .

[30]  O. Ashenfelter,et al.  Estimating the Effect of Training Programs on Earnings , 1976 .

[31]  Zvi Griliches,et al.  Sibling Models and Data in Economics: Beginnings of a Survey , 1979, Journal of Political Economy.

[32]  Cheng Hsiao,et al.  Some Estimation Methods for a Random Coefficient Model , 1975 .

[33]  Maddala Selectivity Problems in Longitudinal Data , 1978 .

[34]  Alok Bhargava,et al.  Serial Correlation and the Fixed Effects Model , 1982 .

[35]  Marno Verbeek,et al.  The efficiency of rotating panel designs in an analysis of variance model , 1991 .

[36]  C. Stein,et al.  Estimation with Quadratic Loss , 1992 .

[37]  Cheng Hsiao,et al.  Benefits and limitations of panel data , 1985 .

[38]  Cheng Hsiao,et al.  Analysis of Panel Data , 1987 .

[39]  Marc Nerlove,et al.  Pooling Cross-section and Time-series Data in the Estimation of a Dynamic Model , 1966 .

[40]  Arnold Zellner,et al.  Bayesian analysis in econometrics , 1988 .

[41]  Sherwin Rosen Studies in Labor Markets , 1981 .

[42]  Ashiq Hussain,et al.  The Use of Error Components Models in Combining Cross Section with Time Series Data , 1969 .

[43]  G. Maddala,et al.  THE USE OF VARIANCE COMPONENTS MODELS IN POOLING CROSS SECTION AND TIME SERIES DATA , 1971 .

[44]  T. J. Wansbeek,et al.  The separation of individual variation and systematic change in the analysis of panel survey data , 1978 .

[45]  D. Lindley The Use of Prior Probability Distributions in Statistical Inference and Decisions , 1961 .

[46]  Tom Wansbeek,et al.  Estimation of the error-components model with incomplete panels , 1989 .

[47]  J. Neyman,et al.  Consistent Estimates Based on Partially Consistent Observations , 1948 .

[48]  Geert Ridder,et al.  Panel Data and Labor Market Studies , 1990 .

[49]  Tom Wansbeek,et al.  A simple way to obtain the spectral decomposition of variance components models for balanced data , 1982 .

[50]  Arnold Zellner,et al.  Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates , 1993 .

[51]  Cheng Hsiao,et al.  Identification and Estimation of Simultaneous Equation Models with Measurement Error , 1976 .

[52]  D. Lindley,et al.  Bayes Estimates for the Linear Model , 1972 .

[53]  Badi H. Baltagi,et al.  A transformation that will circumvent the problem of autocorrelation in an error-component model , 1991 .

[54]  P. Swamy Efficient Inference in a Random Coefficient Regression Model , 1970 .

[55]  H. Jeffreys,et al.  Theory of probability , 1896 .

[56]  D. Cochrane,et al.  Application of Least Squares Regression to Relationships Containing Auto-Correlated Error Terms , 1949 .

[57]  Y. Mundlak On the Pooling of Time Series and Cross Section Data , 1978 .

[58]  J. Heckman The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models , 1976 .

[59]  Cheng Hsiao,et al.  Some remarks on measurement errors and the identification of panel data models , 1991 .

[60]  Lawrence R. Klein,et al.  The statistical approach to economics , 1988 .

[61]  Wagner A. Kamakura,et al.  Book Review: Structural Analysis of Discrete Data with Econometric Applications , 1982 .

[62]  Cheng Hsiao,et al.  Econometric modelling of Canadian long distance calling: A comparison of aggregate time series versus point-to-point panel data approaches , 1992 .

[63]  Marinus Jacobus Catharina Maria Verbeek The design of panel surveys and the treatment of missing observations , 1991 .

[64]  Cheng Hsiao,et al.  Formulation and estimation of dynamic models using panel data , 1982 .

[65]  J. Heckman Heterogeneity and State Dependence , 1981 .