Using gret l for Principles of Econometrics, 4th Edition

Random Regressors and Moment Based Estimation

In this chapter you will learn to use instrumental variables to model’s parameters when its independent variables are correlated

10.1 Basic Model

Consider the linear regression model

Уі = ві + в2 Xi + ei i = 1,2,...,N (10.1)

Equation (10.1) suffers from a significant violation of the usual model assumptions when its explana­tory variable is contemporaneously correlated with the random error, i. e., Cov(ei, xi) = E(eixi) = 0. When a regressor is correlated with the model’s errors, the regressor is often referred to as being endogenous.1 If a model includes an endogenous regressor, least squares is known to be both biased and inconsistent.

An instrument is a variable, z, that is correlated with x but not with the error, e. In addition, the instrument does not directly affect y and thus does not belong in the actual model as a separate regressor. It is common to have more than one instrument for x. All that is required is that these instruments, z1, z2,..., zs, be correlated with x, but not with e. Consistent estimation of (10.1) is possible if one uses the instrumental variables or two-stage least squares estimator, rather than the usual OLS estimator.

Where is a certain sloppiness associated with the use of endogenous in this way, but it has become standard practice in econometrics.

Добавить комментарий

Using gret l for Principles of Econometrics, 4th Edition

Simulation

In appendix 10F of POE4, the authors conduct a Monte Carlo experiment comparing the performance of OLS and TSLS. The basic simulation is based on the model y = x …

Hausman Test

The Hausman test probes the consistency of the random effects estimator. The null hypothesis is that these estimates are consistent-that is, that the requirement of orthogonality of the model’s errors …

Time-Varying Volatility and ARCH Models: Introduction to Financial Econometrics

In this chapter we’ll estimate several models in which the variance of the dependent variable changes over time. These are broadly referred to as ARCH (autoregressive conditional heteroskedas - ticity) …

Как с нами связаться:

Украина:
г.Александрия
тел./факс +38 05235  77193 Бухгалтерия
+38 050 512 11 94 — гл. инженер-менеджер (продажи всего оборудования)

+38 050 457 13 30 — Рашид - продажи новинок
e-mail: msd@msd.com.ua
Схема проезда к производственному офису:
Схема проезда к МСД

Партнеры МСД

Контакты для заказов шлакоблочного оборудования:

+38 096 992 9559 Инна (вайбер, вацап, телеграм)
Эл. почта: inna@msd.com.ua