TAILIEUCHUNG - STATISTICAL THEORY AND ECONOMETRICS

Econometricians, as well as other scientists, are engaged in learning from their experience and data - a fundamental objective of science. Knowledge so obtained may be desired for its own sake, for example to satisfy our curiosity about aspects of economic behavior and/or for use in solving practical problems, for example to improve economic policymaking. In the process of learning from experience and data, description and generalization both play important roles. Description helps us to understand “what is” and what is to be explained by new or old economic generalizations or theories | Chapter 2 STATISTICAL THEORY AND ECONOMETRICS ARNOLD ZELLNER University of Chicago Contents 1. Introduction and overview 68 2. Elements of probability theory 69 . Probability models for observations 70 . Definitions of probability 71 . Axiom systems for probability theory 74 . Random variables and probability models 82 . Elements of asymptotic theory 110 3. Estimation theory 117 . Point estimation 117 . Criteria for point estimation 118 4. Interval estimation Confidence bounds intervals and regions 152 . Confidence bounds 152 . Confidence intervals 154 . Confidence regions 156 5. Prediction 158 . Sampling theory prediction techniques 159 . Bayesian prediction techniques 162 6. Statistical analysis of hypotheses 164 . Types of hypotheses 164 . Sampling theory testing procedures 165 . Bayesian analysis of hypotheses 169 7. Summary and concluding remarks 172 References 174 Research for this paper was financed by NSF Grant SES 7913414 and by income from the . Alexander Endowment Fund Graduate School of Business University of Chicago. Part of this work was done while the author was on leave at the National Bureau of Economic Research and the Hoover Institution Stanford California. Handbook of Econometrics Volume I Edited by Z. Griliches and . Intriligator North-Holland Publishing Company 1983 68 A. Zellner 1. Introduction and overview Econometricians as well as other scientists are engaged in learning from their experience and data-a fundamental objective of science. Knowledge so obtained may be desired for its own sake for example to satisfy our curiosity about aspects of economic behavior and or for use in solving practical problems for example to improve economic policymaking. In the process of learning from experience and data description and generalization both play important roles. Description helps us to understand what is and what is to be explained by new or old economic generalizations or theories. Economic

• Kinh tế học
• Probability models
• Definitions of probability
• Axiom systems
• Random variables
• Elements of asymptotic theory
• Criteria for point estimation
• Distribution models
• Frequency analysis
• Probability distribution models
• Rainfall data
• Frequency analysis of rainfall data
• Lecture Making hard decisions
• Making hard decisions
• Decision tool suite
• Theoretical probability models
• Quantitative uncertainties
• Discrete models
• Binomial distribution
• Artificial Intelligence
• Lecture Artificial Intelligence
• Trí tuệ nhân tạo
• Temporal probability models
• Hidden Markov models
• Kalman filters
• Probability spaces
• Moments
• Bayes theorem
• Conditional probability measures
• Probability measures
• financial models
• Limit theorems
• Lecture Discrete Mathematics I
• Discrete Mathematics I
• Bài giảng Toán rời rạc
• Discrete probability
• Probability rules
• andom variables
• Ebook Probability and statistics for engineers
• Statistics for engineers
• Probability for engineers
• Simple linear regression
• Multiple linear regression
• Certain nonlinear regression models
• Goodness of Fit Tests
• Flow and Precipitation Series
• Probability weighted moments
• Methods of moments
• BMC Bioinformatics
• Sequence analysis
• Nucleosome positioning
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• Probability distributions
• Markov model
• Lecture Advanced Econometrics
• Discrete choice analysis
• Binary outcome models
• Discrete choice model
• Basic types of discrete values
• The probability models
• Monte carlo simulations
• Lattice models
• Lecture Monte carlo simulations
• Phase transition
• Probability theory
• Statistical mechanics
• Quantitative analysis for management
• Quantitative analysis
• Probability concepts
• Network models
• Inventory control models
• Linear programming applications
• Econometrics for Finance
• Introductory Econometrics for Finance
• Financial data
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• Limited dependent variable models
• Linear probability model
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• Poisson process
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• Service rate
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• advanced finance
• Regression model
• Purpose of econometrics
• Fundamentals of algebraic modeling
• Algebraic modeling
• Mathematical modeling
• Additional applications
• Modeling with systems
• Modeling with statistics
• Business statistics
• Categorical data
• Describing quantitative data
• Linear regression
• Factorial experiments
• Nonparametric statistics
• Simulated annealing
• Density function estimation
• Optimal inventory level
• Monte Carlo experiments
• Probability distribution function
• Business analytics
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• Pulmonary embolism
• Wells score
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