ABSTRACT
CONTENTS 23.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 23.2 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 23.3 An Algorithm for Full Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . 389 23.4 Application of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 23.5 Fixed Coefficient Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 23.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395
Appendix: A1 Sampling Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 Appendix: A2 The Measurements of the Variables . . . . . . . . . . . . . . . . . . . . . . . . 397 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397
In the last few years there has been a significant growth of research in mining complex databases, which have been generated from pattern, longitudal, time series, panel and/or sample surveys and their application in econometrics and other social sciences. Indeed, in the last few years, the tremendous growth in the use of sample surveys methods in data mining in the presence of cluster and/or block effects has dichotomized the subject of econometrics due to the nature and problems in the economics data and continuing innovation in computing technology.
