Applications of Differential Geometry to EconometricsPaul Marriott, Mark Salmon Although geometry has always aided intuition in econometrics, more recently differential geometry has become a standard tool in the analysis of statistical models, offering a deeper appreciation of existing methodologies and highlighting the essential issues which can be hidden in an algebraic development of a problem. Originally published in 2000, this volume was an early example of the application of these techniques to econometrics. An introductory chapter provides a brief tutorial for those unfamiliar with the tools of Differential Geometry. The topics covered in the following chapters demonstrate the power of the geometric method to provide practical solutions and insight into problems of econometric inference. |
What people are saying - Write a review
We haven't found any reviews in the usual places.
Contents
Nested models orthogonal projection and encompassing | 64 |
Exact properties of the maximum likelihood estimator | 85 |
Empirical likelihood estimation and inference | 119 |
115 | 149 |
Efficiency and robustness in a geometrical perspective | 151 |
Measuring earnings differentials with frontier functions | 184 |
Other editions - View all
Applications of Differential Geometry to Econometrics Paul Marriott,Mark Salmon No preview available - 2011 |
Common terms and phrases
affine alternatives Amari application approach approximation assumed asymptotic boundary chapter components connection consider consistent constant corresponding critical curvature curved defined definition denote density depends derivative determined differential direction discussion distance distribution econometric efficient encompassing equal equation equivalent estimator example exists expected expressed fact Figure Fisher full exponential family function further geodesic geometry given gives Hence important income independent integration limiting linear M₁ manifold mapping matrix maximum likelihood mean measure method metric natural nested normal Note null observed obtained orthogonal parameter parameterisation particular plots positive possible probability problem projection Proof properties provides random region respect restrictions result sample satisfies score sequence shows standard statistic structure sufficient tangent space tangent vector tensor Theorem theory tion variables variance vector Wald zero