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EXERCISE ON ESTIMATING GMM MODELS IN EVIEWS

Introduction

In this exercise we demonstrate how GMM models are estimated in EViews.

Data from two advanced econometrics texts are used: Hayashi’s Econometrics and Favero’s Applied Macroeconometrics.

Preparations

Enter EViews and choose:

* File

* Open datasets

* Select

* GMM1.wf1 (for Hayashi’s dataset) – Exercise 1

* hmsfit.wf1 (for Favero’s dataset1) – Exercise 2

* cggrfc.wf1 (for Favero’s dataset2) – Exercise 3

Exercise 1: Empirical Exercise from Hayashi Chapter 3: Least Squares and GMM estimation of wage equation (Hayashi suggests using TSP or RATS but this can also be completed in EViews with some manipulation). Questions are reproduced here (sometimes truncated) for practical purposes. This exercise is based on the wage equation discussed in Griliches (1976) and data used in Blackburn and Neumark (1992). This exercise is intended to provide a background on the applications of GMM including tests for over-identifying restrictions. Some questions are omitted and are left as home work to be completed in your spare time.

The data is cross-sectional data on individuals at two points in time: the earliest year in which wages and other variables are available and in 1980. An Excel version of this data is available from Hayashi’s website: http://www.e.u-tokyo.ac.jp/~hayashi/ . A full description of the data set is given on page 250-251 in Hayashi. The variables are:

RNS = dummy for residency in southern states

MTR = dummy for marital status (1 if married)

SMSA = dummy for residency in metropolitan areas

MED = mother’s education

KWW = score on the “Knowledge on the World of Work”

IQ = IQ score

S = completed years of schooling

EXP = experience in years

TENURE = tenure in years

LW = log wage

Variable without “80” are those for the first point and those with “80” are for 1980. Year = the year of the first point in time. There are 758 observations. For this exercise, we use an...