Instrumental variables estimation

Related lectures (31)

Page 3 of 4

Covers the inconsistency in estimates due to endogeneity and introduces the BLP and Control-Function estimation methods.

Covers the concept of multi-linear regression and the least squares method for model fitting.

Explores the Fubini theorem for integrability in two variables and emphasizes the significance of the order of integration.

Discusses local extrema of functions in two variables around the point (0,0).

Addresses corrections to the implicit functions theorem proof and clarifies the role of specific variables.

Covers the extension of the 0-1 loss to real-valued score functions and logistic regression.

Covers the pitfalls of observational studies, solutions to avoid biases, and the importance of valid conclusions from 'found data'.

Explores Gaussian linear regression, design matrix, least squares estimation, and geometric interpretation in linear regression analysis.

Covers the Taylor polynomial in two variables and the concept of extrema in functions of several variables.

Covers variance reduction techniques in stochastic simulation to improve output quantity estimation accuracy.