An information theoretic approach to econometrics
George G. Judge · 2011
על הספר
"This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic models and methods. Because most data are observational, practitioners work with indirect noisy observation and ill-posed econometric in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of pwer divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-models problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family"--
פרטי הספר
- שנה
- 2011
- שפה
- English
- הוצאה
- Cambridge University Press
- מהדורות
- 1
- ISBN
- 9780521869591
מקור הנתונים: Wikidata
