Probability theory and statistical inference : econometric modeling with observational data
This major textbook is intended for students taking introductory courses in Probability Theory and Statistical Inference. The text is extremely student-friendly, with pathways designed for semester usage, and although aimed primarily at students of econometrics and economics, will have considerable...
Uložené v:
| Hlavný autor: | |
|---|---|
| Médium: | E-kniha Kniha |
| Jazyk: | English |
| Vydavateľské údaje: |
Cambridge
Cambridge University Press
1999
Cambridge Univ. Press |
| Vydanie: | 1 |
| Predmet: | |
| ISBN: | 9780521413541, 0521413540, 0521424089, 9780521424080 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | This major textbook is intended for students taking introductory courses in Probability Theory and Statistical Inference. The text is extremely student-friendly, with pathways designed for semester usage, and although aimed primarily at students of econometrics and economics, will have considerable utility for courses in all disciplines using observational data. |
|---|---|
| AbstractList | This major textbook is intended for students taking introductory courses in Probability Theory and Statistical Inference. The text is extremely student-friendly, with pathways designed for semester usage, and although aimed primarily at students of econometrics and economics, will have considerable utility for courses in all disciplines using observational data. This major new 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. |
| Author | Spanos, Aris |
| Author_xml | – sequence: 1 fullname: Spanos, Aris |
| BackLink | https://cir.nii.ac.jp/crid/1130282270957415936$$DView record in CiNii http://www.econis.eu/PPNSET?PPN=250730197$$DView this record in ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften |
| BookMark | eNotkE1PHDEMhoMoCLrsb2gOqFIPK8X5mCTcYAW0ElJ7qHrpYZTJeLopswkkKWj_fbMsPtiy_PiV_X4kH2KKeESWVhumAEBxEPb4vecgQSgJJ-TMGsaZ5uyUnEutLGhrxBlZlvKXteBcG8POye8fOQ1uCHOoO1o3mPKOujjSUl0NpQbvZhrihBmjR3pF0aeYtlhz8HSbRpxD_ENfQ93QNBTML20rxbYzuuouyMnk5oLL97ogv-5uf66_rh6-339bXz-snBHSiBUykJPrhnEyyKd2PlfIBiuUtR123X44KnBW6k5qb4F32ktptRDOgkcQC_LlIOzKI76WTZpr6V9mHFJ6LH0zBoDpZpW2rLGfDuz-k1D6pxy2Lu96rpgWDJrqgnw-EE85Pf_DUvs3IY-xZjf3tzdrzrgUe_DyAMYQeh_2GUAwbpq5zCotQVnRif-bGXrp |
| ContentType | eBook Book |
| DBID | RYH OQ6 |
| DEWEY | 330.015195 |
| DatabaseName | CiNii Complete ECONIS |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Economics |
| EISBN | 9780511152139 0511152132 9781107111790 110711179X |
| Edition | 1 1. publ. |
| ExternalDocumentID | 9781107111790 250730197 EBC202437 BA42858856 |
| GroupedDBID | -G2 -VX 089 20A 38. A4I A4J AAAAZ AABBV AAHFW AAQLY ABARN ABESS ABFFC ABMRC ABZUC ACCTN ACLGV ACNOG AD4 ADCGF ADQZK ADVEM ADWXY AEDFS AERYV AEWAL AEWQY AGSJN AHAWV AHJKY AHWGJ AIAQS AJFER AJXXZ ALMA_UNASSIGNED_HOLDINGS AMJDZ ANGWU ASYWF AUKZS AYSPE AZZ BBABE BFIBU BOIVQ CCHSS COBLI COXPH CZZ DLNRW DUGUG EBSCA ECOWB EDVGL FH2 FVPQW HF4 ICERG IDFYU IVN JJU JNA MWOYL MYL OLDIN OTBUH OZASK OZBHS PP- PQQKQ RYH S2E SACVX SN- XI1 ZXKUE AADVP OQ6 |
| ID | FETCH-LOGICAL-a83483-e014fa6bdf8e2f35425e0b935996e6614fad51a947647c91267c449733a91ce13 |
| ISBN | 9780521413541 0521413540 0521424089 9780521424080 |
| IngestDate | Mon Sep 15 23:48:52 EDT 2025 Sat Mar 08 17:07:33 EST 2025 Wed Dec 10 11:12:57 EST 2025 Fri Jun 27 00:22:01 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| LCCN | 98020720 |
| LCCallNum_Ident | HB139 .S66 1999 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a83483-e014fa6bdf8e2f35425e0b935996e6614fad51a947647c91267c449733a91ce13 |
| Notes | Includes bibliographical references (p. 787-805) and index |
| OCLC | 475917983 |
| PQID | EBC202437 |
| PageCount | 843 |
| ParticipantIDs | askewsholts_vlebooks_9781107111790 econis_primary_250730197 proquest_ebookcentral_EBC202437 nii_cinii_1130282270957415936 |
| PublicationCentury | 1900 |
| PublicationDate | 1999 1999-09-02 |
| PublicationDateYYYYMMDD | 1999-01-01 1999-09-02 |
| PublicationDate_xml | – year: 1999 text: 1999 |
| PublicationDecade | 1990 |
| PublicationPlace | Cambridge |
| PublicationPlace_xml | – name: Cambridge – name: Cambridge [u.a.] |
| PublicationYear | 1999 |
| Publisher | Cambridge University Press Cambridge Univ. Press |
| Publisher_xml | – name: Cambridge University Press – name: Cambridge Univ. Press |
| SSID | ssj0000227880 ssib029093651 |
| Score | 1.7868078 |
| Snippet | This major textbook is intended for students taking introductory courses in Probability Theory and Statistical Inference. The text is extremely... This major new textbook from a distinguished econometrician is intended for students taking introductory courses in probability theory and statistical... |
| SourceID | askewsholts econis proquest nii |
| SourceType | Aggregation Database Index Database Publisher |
| SubjectTerms | Econometrics Mathematical statistics Probabilities Schätztheorie Theorie Wahrscheinlichkeitsrechnung Ökonometrielehrbuch |
| TableOfContents | 10.3 Attempts to build a bridge between probability and observed data -- 10.4 Toward a tentative bridge -- 10.5 The probabilistic reduction approach to specification -- 10.6 Parametric versus non-parametric models -- 10.7 Summary and conclusions -- 10.8 Exercises -- 11 An introduction to statistical inference -- 11.1 Introduction -- 11.2 An introduction to the classical approach -- 11.3 The classical versus the Bayesian approach -- 11.4 Experimental versus observational data -- 11.5 Neglected facets of statistical inference -- 11.6 Sampling distributions -- 11.7 Functions of random variables -- 11.8 Computer intensive techniques for approximating sampling distributions* -- 11.9 Exercises -- 12 Estimation I: Properties of estimators -- 12.1 Introduction -- 12.2 Defining an estimator -- 12.3 Finite sample properties -- 12.4 Asymptotic properties -- 12.5 The simple Normal model -- 12.6 Sufficient statistics and optimal estimators* -- 12.7 What comes next? -- 12.8 Exercises -- 13 Estimation II: Methods of estimation -- 13.1 Introduction -- 13.2 Moment matching principle -- 13.3 The least-squares method -- 13.4 The method of moments -- 13.5 The maximum likelihood method -- 13.6 Exercises -- 14 Hypothesis testing -- 14.1 Introduction -- 14.2 Leading up to the Fisher approach -- 14.3 The Neyman-Pearson framework -- 14.4 Asymptotic test procedures* -- 14.5 Fisher versus Neyman-Pearson -- 14.6 Conclusion -- 14.7 Exercises -- 15 Misspecification testing -- 15.1 Introduction -- 15.2 Misspecification testing: formulating the problem -- 15.3 A smorgasbord of misspecification tests -- 15.4 The probabilistic reduction approach and misspecification -- 15.5 Empirical examples -- 15.6 Conclusion -- 15.7 Exercises -- References -- Index 5.4 Assessing distribution assumptions -- 5.5 Independence and the t-plot -- 5.6 Homogeneity and the t-plot -- 5.7 The empirical cdf and related graphs* -- 5.8 Generating pseudo-random numbers* -- 5.9 Summary -- 5.10 Exercises -- 6 The notion of a non-random sample -- 6.1 Introduction -- 6.2 Non-random sample: a preliminary view -- 6.3 Dependence between two random variables: joint distributions -- 6.4 Dependence between two random variables: moments -- 6.5 Dependence and the measurement system -- 6.6 Joint distributions and dependence -- 6.7 From probabilistic concepts to observed data -- 6.8 What comes next? -- 6.9 Exercises -- 7 Regression and related notions -- 7.1 Introduction -- 7.2 Conditioning and regression -- 7.3 Reduction and stochastic conditioning -- 7.4 Weak exogeneity* -- 7.5 The notion of a statistical generating mechanism (GM) -- 7.6 The biometric tradition in statistics -- 7.7 Summary -- 7.8 Exercises -- 8 Stochastic processes -- 8.1 Introduction -- 8.2 The notion of a stochastic process -- 8.3 Stochastic processes: a preliminary view -- 8.4 Dependence restrictions -- 8.5 Homogeneity restrictions -- 8.6 "Building block" stochastic processes -- 8.7 Markov processes -- 8.8 Random walk processes -- 8.9 Martingale processes -- 8.10 Gaussian processes -- 8.11 Point processes -- 8.12 Exercises -- 9 Limit theorems -- 9.1 Introduction to limit theorems -- 9.2 Tracing the roots of limit theorems -- 9.3 The Weak Law of Large Numbers -- 9.4 The Strong Law of Large Numbers -- 9.5 The Law of Iterated Logarithm* -- 9.6 The Central Limit Theorem -- 9.7 Extending the limit theorems* -- 9.8 Functional Central Limit Theorem* -- 9.9 Modes of convergence -- 9.10 Summary and conclusion -- 9.11 Exercises -- 10 From probability theory to statistical inference* -- 10.1 Introduction -- 10.2 Interpretations of probability Intro -- Contents -- Preface -- Acknowledgments -- Symbols -- Acronyms -- 1 An introduction to empirical modeling -- 1.1 Introduction -- 1.2 Stochastic phenomena, a preliminary view -- 1.3 Chance regularity and statistical models -- 1.4 Statistical adequacy -- 1.5 Statistical versus theory information* -- 1.6 Observed data -- 1.7 Looking ahead -- 1.8 Exercises -- 2 Probability theory: a modeling framework -- 2.1 Introduction -- 2.2 Simple statistical model: a preliminary view -- 2.3 Probability theory: an introduction -- 2.4 Random experiments -- 2.5 Formalizing condition [a]: the outcomes set -- 2.6 Formalizing condition [b]: events and probabilities -- 2.7 Formalizing condition [c]: random trials -- 2.8 Statistical space -- 2.9 A look forward -- 2.10 Exercises -- 3 The notion of a probability model -- 3.1 Introduction -- 3.2 The notion of a simple random variable -- 3.3 The general notion of a random variable -- 3.4 The cumulative distribution and density functions -- 3.5 From a probability space to a probability model -- 3.6 Parameters and moments -- 3.7 Moments -- 3.8 Inequalities -- 3.9 Summary -- 3.10 Exercises -- Appendix A Univariate probability models -- A.1 Discrete univariate distributions -- A.2 Continuous univariate distributions -- 4 The notion of a random sample -- 4.1 Introduction -- 4.2 Joint distributions -- 4.3 Marginal distributions -- 4.4 Conditional distributions -- 4.5 Independence -- 4.6 Identical distributions -- 4.7 A simple statistical model in empirical modeling: a preliminary view -- 4.8 Ordered random samples* -- 4.9 Summary -- 4.10 Exercises -- Appendix B Bivariate distributions -- B.1 Discrete bivariate distributions -- B.2 Continuous bivariate distributions -- 5 Probabilistic concepts and real data -- 5.1 Introduction -- 5.2 Early developments -- 5.3 Graphical displays: a t-plot |
| Title | Probability theory and statistical inference : econometric modeling with observational data |
| URI | https://cir.nii.ac.jp/crid/1130282270957415936 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=202437 http://www.econis.eu/PPNSET?PPN=250730197 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781107111790&uid=none |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pa9swFH5LskJ767aWZP1lxo5ziSxZso5bSSkUSmHZKLsYWVYgrLghSUP63-89yXGSlh166EVYNjzb39OP92R9nwG-miyV1roiLrNMxkLyMi5cStp4PDEGJ3heGP-zCXVzk93d6dvWu2rFhVncq6rKlks9eVNX4zl0NlFnX-HuxiiewGN0OpbodiyfRcRNNXj8dop90-91fQr8xCCtRJQhr8bs5TVqet862Q8CAVvJ_jaP63xrm0bICImLi_NSGtSknklJY7hDHVqrNrSVxFT1_fdf139-NmtSJCeI_bmm9HozTYWk0HStWNTcY6NO12kBycz-4uiMI_d8RtwtTOrHeNCuxuMXs52fwof70CFaxwdoueoj7K742LNP8G0DtyjgFiFu0QZuUYPbAfy-HAwvruL6HxKxybjIeOwwBxwZWZSjzCUjfOgkdf2C-MhaOgpORqZMmdFCSaGsZolUVgitODeaWcf4IXSqh8p1IbJJ3whmyoQ7JlJrDPbCIhVF2efCJYz34MvG2-eLe_-9e5aTrBhm2YzU9_o96AZQ8kmQE8kbr_TgBGHK7ZhKRl-UMXpTGANT1Ke57MHZCsDcm6737-aDHxeJV5b8_H_jR7C3blDH0JlPH90J7NgFQjk9rVvCP3HFGfM |
| linkProvider | ProQuest Ebooks |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.title=Probability+theory+and+statistical+inference&rft.date=1999-01-01&rft.pub=Cambridge+Univ.+Press&rft.isbn=9780521413541&rft.externalDocID=250730197 |
| thumbnail_m | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fvle.dmmserver.com%2Fmedia%2F640%2F97811071%2F9781107111790.jpg |