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14.30

Introduction to Statistical Methods in Economics

4-0-8 units. REST · Undergraduate

Self-contained introduction to probability and statistics with applications in economics and the social sciences. Covers elements of probability theory, statistical estimation and inference, regression analysis, causal inference, and program evaluation. Couples methods with applications and with assignments involving data analysis. Uses basic calculus and matrix algebra. Students taking graduate version complete additional assignments. May not count toward HASS requirement.

Spring 2009

Topics

  • Sets and events
  • Probabilities and counting rules
  • Conditional probability and independence
  • Bayes theorem and random variables
  • Discrete and continuous random variables
  • Probability distribution functions (PDFs), cumulative distribution functions (CDFs), joint distribution of 2 or more random variables
  • Joint and marginal distributions
  • Functions of random variables
  • Functions of several random variables
  • Order statistics and expectations
  • Median, quantiles, and variance
  • Covariance and conditional expectations
  • Conditional expectations and special distributions
  • Law of large numbers
  • Central limit theorem, estimators, bias, and consistency
  • Constructing estimators
  • Confidence intervals
  • Confidence intervals (cont.)
  • Hypothesis tests
  • Hypothesis tests (cont.)

Readings

  • [LM] = Larsen, R., and M. Marx. Introduction to Mathematical Statistics and Its Applications . 4th ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2005. ISBN: 9780131867932.
    Required
  • [DS] = DeGroot, M., and M. Schervish. Probability and Statistics . 3rd ed. Reading, MA: Addison-Wesley, 2001. ISBN: 9780201524888.
    Recommended
  • [L] = Lindgren, B. Statistical Theory . 4th ed. New York, NY: Chapman and Hall, 1993. ISBN: 9780412041815.
    Recommended
  • [LM] chapter 4.
    Recommended
  • [DS] chapter 5, except 5.7-5.8.
    Recommended
  • [L] chapter 3.
    Recommended
  • [DS] chapter 8.
    Recommended
Spring 2006 · Introduction to Statistical Method in Economics

This course is a self-contained introduction to statistics with economic applications. Elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for {{% resource_link "65e10575-694d-4f78-8007-eac4b5e6fd2b" "_14.32 Econometrics_" %}}. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.

Readings

  • [ROS]: Ross, Sheldon M. Probability and Statistics for Engineers and Scientists . 3rd ed. San Diego, CA: Academic Press, 2004. ISBN: 0125980574.
    Required
  • [LM]: Larsen, Richard J., and Morris L. Marx. An Introduction to Mathematical Statistics and its Applications . 3rd ed. Upper Saddle River, NJ: Prentice Hall, 2001. ISBN: 0139223037.
    Recommended
  • [DS]: DeGroot, Morris H., and Mark J. Schervish. Probability and Statistics . 3rd ed. Boston, MA: Addison-Wesley, 2002.
    Recommended
  • 1 · Set and Probability Theory · Chapter 3 · Chapters 1.1–1.3, 2.1–2.10 · Chapters 1, 2.1–2.3
    Listed reading
  • 2 · Random Variables, Probability Mass/Density Function, Cumulative Distribution Function (Univariate Model) · Chapters 4.1–4.2, 5.1, pp. 160-1 · Chapter 3.1–3.4 · Chapter 3.1–3.3
    Listed reading
  • 3 · Multiple Random Variables, Bivariate Distribution, Marginal Distribution, Conditional Distribution, Independence, Multivariate Distribution (Multivariate Model) · Chapter 4.3 · Chapter 3.5–3.6, 3.9 · Chapter 3.4–3.7
    Listed reading
  • 4 · Expectation (Moments) · Chapter 4.4–4.9 · Chapter 3.10–3.13, 3.15–3.16 · Chapter 4.1–4.7
    Listed reading
  • 6 · Random Variable and Random Vector Transformations (Univariate and Multivariate Models) · Chapter 3.7 · Chapter 3.8–3.9
    Listed reading
  • 7 · Special Distributions (Discrete and Continuous) · Chapter 5.1–5.8 · Chapters 3.3, 4.1–4.3, 4.5–4.6 · Chapter 5.1–5.6, 5.9
    Listed reading
  • 9 · Random Sample, Law of Large Numbers, Central Limit Theorem · Chapters 6, 4.9, 1, 2 · Chapters 3.14, pp. 272-5, 5.1, 5.4 · Chapters 4.8, 5.7, 7.1, 7.7
    Listed reading
  • 10 · Point Estimators and Point Estimation Methods · Chapter 7.7 and 7.1–7.2 · Chapter 5.2 · Chapter 6.5–6.6
    Listed reading
  • 11 · Interval Estimation and Confidence Intervals · Chapters 7.3–7.6, 5.8.2–5.8.3 · Chapter 5.3 · Chapter 7.5
    Listed reading
  • 12 · Hypothesis Testing · Chapter 8 · Chapters 6, 9.1–9.2 · Chapter 8
    Listed reading

Prerequisites & course information

Subject meets with 14.300 Prereq: Calculus II (GIR) U (Fall) 4-0-8 units. REST

Sources