Mathematical Statistics for Applied Econometrics

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Absolute Deviation Estimator
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Author_Charles B Moss
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basics of statistical inference
Bivariate Random Variable
Bivariate Uniform Distribution
Borel Set
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Continuous Random Variable
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Cumulative Distribution Function
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Distribution Functions
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econometric formulations
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General Maximum Likelihood Estimator
Introductory Econometrics Text
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Linear Unbiased Estimator
mathematical statistics for analyzing economic decisions under risk and uncertainty
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Maximum Likelihood Estimator
Medicare Drug Plans
nonlinear econometric techniques
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Quantile Regression
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statistical tools and econometric applications
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Product details

  • ISBN 9781032921020
  • Weight: 670g
  • Dimensions: 156 x 234mm
  • Publication Date: 14 Oct 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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An Introductory Econometrics Text

Mathematical Statistics for Applied Econometrics covers the basics of statistical inference in support of a subsequent course on classical econometrics. The book shows students how mathematical statistics concepts form the basis of econometric formulations. It also helps them think about statistics as more than a toolbox of techniques.

Uses Computer Systems to Simplify Computation

The text explores the unifying themes involved in quantifying sample information to make inferences. After developing the necessary probability theory, it presents the concepts of estimation, such as convergence, point estimators, confidence intervals, and hypothesis tests. The text then shifts from a general development of mathematical statistics to focus on applications particularly popular in economics. It delves into matrix analysis, linear models, and nonlinear econometric techniques.

Students Understand the Reasons for the Results

Avoiding a cookbook approach to econometrics, this textbook develops students’ theoretical understanding of statistical tools and econometric applications. It provides them with the foundation for further econometric studies.