Vor Ort
Merkzettel Warenkorb
  • Produktbild: Introduction to the Mathematical and Statistical Foundations of Econometrics
  • Produktbild: Introduction to the Mathematical and Statistical Foundations of Econometrics

Introduction to the Mathematical and Statistical Foundations of Econometrics

178,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

20.10.2010

Abbildungen

19 b/w illus. 12 tables

Verlag

Cambridge University Press

Seitenzahl

344

Maße (L/B/H)

23,5/15,7/2,5 cm

Gewicht

710 g

Sprache

Englisch

ISBN

978-0-521-83431-5

Beschreibung

Rezension

'The objective of this book is to use it as an introductory text for a Ph.D. level course in Econometrics. ... Appendixes are self contained with review which are easy to learn and understand. As a whole, I consider this book as unique and self-contained and it will be a great resource for researchers in the area of Econometrics.' Zentralblatt MATH

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

20.10.2010

Abbildungen

19 b/w illus. 12 tables

Verlag

Cambridge University Press

Seitenzahl

344

Maße (L/B/H)

23,5/15,7/2,5 cm

Gewicht

710 g

Sprache

Englisch

ISBN

978-0-521-83431-5

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Introduction to the Mathematical and Statistical Foundations of Econometrics
  • Produktbild: Introduction to the Mathematical and Statistical Foundations of Econometrics
  • Part I. Probability and Measure: 1. The Texas lotto; 2. Quality control; 3. Why do we need sigma-algebras of events?; 4. Properties of algebras and sigma-algebras; 5. Properties of probability measures; 6. The uniform probability measures; 7. Lebesque measure and Lebesque integral; 8. Random variables and their distributions; 9. Density functions; 10. Conditional probability, Bayes's rule, and independence; 11. Exercises: A. Common structure of the proofs of Theorems 6 and 10, B. Extension of an outer measure to a probability measure; Part II. Borel Measurability, Integration and Mathematical Expectations: 12. Introduction; 13. Borel measurability; 14. Integral of Borel measurable functions with respect to a probability measure; 15. General measurability and integrals of random variables with respect to probability measures; 16. Mathematical expectation; 17. Some useful inequalities involving mathematical expectations; 18. Expectations of products of independent random variables; 19. Moment generating functions and characteristic functions; 20. Exercises: A. Uniqueness of characteristic functions; Part III. Conditional Expectations: 21. Introduction; 22. Properties of conditional expectations; 23. Conditional probability measures and conditional independence; 24. Conditioning on increasing sigma-algebras; 25. Conditional expectations as the best forecast schemes; 26. Exercises; A. Proof of theorem 22; Part IV. Distributions and Transformations: 27. Discrete distributions; 28. Transformations of discrete random vectors; 29. Transformations of absolutely continuous random variables; 30. Transformations of absolutely continuous random vectors; 31. The normal distribution; 32. Distributions related to the normal distribution; 33. The uniform distribution and its relation to the standard normal distribution; 34. The gamma distribution; 35. Exercises: A. Tedious derivations; B. Proof of theorem 29; Part V. The Multivariate Normal Distribution and its Application to Statistical Inference: 36. Expectation and variance of random vectors; 37. The multivariate normal distribution; 38. Conditional distributions of multivariate normal random variables; 39. Independence of linear and quadratic transformations of multivariate normal random variables; 40. Distribution of quadratic forms of multivariate normal random variables; 41. Applications to statistical inference under normality; 42. Applications to regression analysis; 43. Exercises; A. Proof of theorem 43; Part VI. Modes of Convergence: 44. Introduction; 45. Convergence in probability and the weak law of large numbers; 46. Almost sure convergence, and the strong law of large numbers; 47. The uniform law of large numbers and its applications; 48. Convergence in distribution; 49. Convergence of characteristic functions; 50. The central limit theorem; 51. Stochastic boundedness, tightness, and the Op and op-notations; 52. Asymptotic normality of M-estimators; 53. Hypotheses testing; 54. Exercises: A. Proof of the uniform weak law of large numbers; B. Almost sure convergence and strong laws of large numbers; C. Convergence of characteristic functions and distributions; Part VII. Dependent Laws of Large Numbers and Central Limit Theorems: 55. Stationary and the world decomposition; 56. Weak laws of large numbers for stationary processes; 57. Mixing conditions; 58. Uniform weak laws of large numbers; 59. Dependent central limit theorems; 60. Exercises: A. Hilbert spaces; Part VIII. Maximum Likelihood Theory; 61. Introduction; 62. Likelihood functions; 63. Examples; 64. Asymptotic properties if ML estimators; 65. Testing parameter restrictions; 66. Exercises.