Probability Theory and Statistical Inference: Econometric Modeling with Observational Data
This major textbook from a distinguished econometrician is intended for students taking introductory courses in probability theory and statistical inference. No prior knowledge other than a basic familiarity with descriptive statistics is assumed. The primary objective of this book is to establish the framework for the empirical modelling of observational (non-experimental) data. This framework known as 'Probabilistic Reduction' is formulated with a view to accommodating the peculiarities of observational (as opposed to experimental) data in a unifying and logically coherent way. Probability Theory and Statistical Inference differs from traditional textbooks in so far as it emphasizes concepts, ideas, notions and procedures which are appropriate for modelling observational data. Aimed at students at second-year undergraduate level and above studying econometrics and economics, this textbook will also be useful for students in other disciplines which make extensive use of observational data, including finance, biology, sociology and psychology and climatology.
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Contents
III
|
1 |
V
|
3 |
VI
|
13 |
VII
|
16 |
VIII
|
19 |
IX
|
20 |
X
|
29 |
XI
|
30 |
LXXX
|
403 |
LXXXI
|
410 |
LXXXII
|
420 |
LXXXIII
|
426 |
LXXXIV
|
431 |
LXXXV
|
433 |
LXXXVI
|
435 |
LXXXVII
|
438 |
XII
|
31 |
XIV
|
33 |
XV
|
39 |
XVI
|
42 |
XVII
|
45 |
XVIII
|
48 |
XIX
|
69 |
XX
|
73 |
XXI
|
74 |
XXII
|
75 |
XXIII
|
77 |
XXIV
|
78 |
XXV
|
85 |
XXVI
|
89 |
XXVII
|
97 |
XXVIII
|
104 |
XXIX
|
109 |
XXX
|
131 |
XXXI
|
132 |
XXXII
|
133 |
XXXIII
|
135 |
XXXIV
|
136 |
XXXV
|
138 |
XXXVI
|
145 |
XXXVIII
|
147 |
XXXIX
|
155 |
XL
|
158 |
XLI
|
167 |
XLII
|
171 |
XLIII
|
175 |
XLIV
|
181 |
XLV
|
184 |
XLVII
|
185 |
XLVIII
|
190 |
L
|
193 |
LI
|
195 |
LII
|
197 |
LIII
|
212 |
LIV
|
217 |
LV
|
229 |
LVI
|
258 |
LVII
|
259 |
LVIII
|
260 |
LXI
|
263 |
LXII
|
269 |
LXIII
|
272 |
LXIV
|
282 |
LXV
|
290 |
LXVI
|
309 |
LXVII
|
330 |
LXVIII
|
335 |
LXIX
|
337 |
LXXI
|
339 |
LXXII
|
356 |
LXXIII
|
366 |
LXXIV
|
368 |
LXXV
|
377 |
LXXVI
|
397 |
LXXVIII
|
400 |
LXXXVIII
|
444 |
LXXXIX
|
458 |
XC
|
460 |
XCI
|
462 |
XCIII
|
465 |
XCIV
|
469 |
XCV
|
476 |
XCVI
|
481 |
XCVII
|
482 |
XCVIII
|
491 |
XCIX
|
495 |
C
|
503 |
CI
|
510 |
CIII
|
512 |
CIV
|
514 |
CV
|
520 |
CVI
|
528 |
CVII
|
541 |
CVIII
|
546 |
CIX
|
556 |
CXI
|
558 |
CXII
|
559 |
CXIII
|
568 |
CXIV
|
570 |
CXV
|
575 |
CXVI
|
578 |
CXVII
|
584 |
CXVIII
|
594 |
CXIX
|
600 |
CXX
|
602 |
CXXII
|
603 |
CXXIII
|
607 |
CXXIV
|
615 |
CXXV
|
621 |
CXXVI
|
627 |
CXXVII
|
635 |
CXXIX
|
637 |
CXXXI
|
639 |
CXXXII
|
648 |
CXXXIII
|
654 |
CXXXIV
|
659 |
CXXXV
|
678 |
CXXXVI
|
681 |
CXXXVII
|
682 |
CXXXVIII
|
692 |
CXXXIX
|
713 |
CXL
|
720 |
CXLI
|
727 |
CXLIII
|
729 |
CXLIV
|
733 |
CXLV
|
739 |
CXLVI
|
753 |
CXLVII
|
765 |
CXLVIII
|
783 |
CXLIX
|
784 |
CL
|
787 |
806 | |
Other editions - View all
Probability Theory and Statistical Inference: Econometric Modeling with ... Aris Spanos No preview available - 1999 |
Probability Theory and Statistical Inference: Econometric Modeling with ... Aris Spanos No preview available - 1999 |
Common terms and phrases
asymptotic Bernoulli Bernoulli distribution bivariate chance regularity patterns chapter concepts consider context convergence defined denotes density function derive discussion distribution assumption ecdf econometrics empirical modeling event space Example Exponential figure Fisher H₁ histogram Identically Distributed independent intuitive joint density joint distribution lemma limit theorems marginal distributions Markov martingale mathematical method misspecification testing Normal distribution NOTE notion observed data outcomes set p-value Pearson postulated model postulated statistical model probabilistic structure probability model probability space probability theory properties pseudo-random numbers Q-Q plot random experiment random sample random variables regression result sampling distribution Sampling model simple Normal model simple statistical model skedastic Spanos specification Statistical GM statistical inference statistical model stochastic process Student's Student's t distribution subset sufficient statistic t-plot takes the form test statistic tion unknown parameters values Var(X variance WLLN X₁ Y₁