EEC 677 – Econometric Applications in Resource Economics

Course Information

eec677Professor: Staff
Semester: Spring (every other year)
Credits: 3
Prerequisites: EEC 676 or permission of instructor

Catalog Description: Special topics in econometrics as applied to agriculture and natural resources. Topics include time series models, Bayesian analysis, and dichotomous dependent variables.

Course Goals & Outcomes

The primary objective of this course is to expose you to, and give you an operational understanding of, many econometric models used in resource economics.

By the end of the semester students will be able to:

  • Write a likelihood function
  • Estimate models involving environmental resource use using maximum likelihood techniques
  • Generalize these models to any likelihood-based statistical models
Course Syllabus

Class topics:

  • Introduction to computer programming
  • Introduction to Gauss
  • Maximizing a function
  • Maximum likelihood estimation of the moments of probability distributions
  • The linear model & maximum likelihood’s equivalence to OLS
  • Binomial Probit and Logit
  • Truncation and censoring
  • Poisson regression
  • Panel data
    • Fixed effects
    • Random effects
  • Logit extensions
    • Multinomial logit
    • Conditional logit
    • Nested logit
    • Ordered logit
  • Numerical methods
    • Bootstrap
    • Monte Carlo
  • Heterogeneity, Latent Class and Random parameters logit

Supplementary Topics:

  • Why use maximum likelihood? Asymptotic efficiency
    • Parameter search algorithms
    • Statistical inference I: Likelihood ratio tests
    • Statistical inference II: Non-nested models
    • Nonparametric statistics
    • Endogenous (Heckman) Selection Models
    • Program Evaluation and propensity score matching
    • Semiparametric estimation
    • Conditional heteroskedasticity
About The Professor

This course is taught by multiple instructors, please refer to URI Course Schedule.

Reasons To Take This Course

This course will enable you to develop the skills required to write a maximum likelihood function and estimate models using this technique, which can be applied to solving current environmental resource use issues.

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