Select Name to name the equation as estimated equation object. Enter the equation specification in the equation box and list of instruments in the instrument list box (all variables are separated by single spaces). Is the relative magnitude of the three different estimates of schooling coefficient consistent with prediction based on the omitted variable and errors-in-variables biases? Answer: TSLS regression are estimated in EViews by entering the TSLS command 2 in the command window or by selecting Quick/Estimate Equation to launch the equation specification dialog box. The set of instruments should consists of the predetermined variables (S and h), the excluded predetermined variables (MED, KWW, MRT, AGE) and the year dummies. Where LW is log wages, S is schooling and Estimate a Two Stage Least Squares (TSLS) regression model for the following wage equation This can also be created by selecting Quick/Generate Series… and by entering y66=(Year=66).ī. To create the Year dummy for 1966 say, enter the following command in the EViews command window: genr y66=(Year=66) where 66 is equivalent to 1966. A quick way to compute pairwise correlation is to use the following command in the EViews command window where x and y are the respective variables. Select View/Descriptive Statistics/Common sample. Generate seven year dummies for Year = 66,….,73 (with exception of 1972, which has no observation) Answer: Select the relevant variables and open as a group. Calculate the means and standard deviation of all the variables (including those for 1980) and the correlations between IQ and S. For this exercise, we use an undated/irregular data format in EViews. Year = the year of the first point in time. Variable without “80” are those for the first point and those with “80” are for 1980. Hayashi (2000), Econometrics, Princeton Favero (2001), Applied Macroeconometrics, Oxford University Press. 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
#Svar eviews 10 full#
A full description of the data set is given on page 250-251 in Hayashi.
An Excel version of this data is available from Hayashi’s website. 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. Some questions are omitted and are left as home work to be completed in your spare time. This exercise is intended to provide a background on the applications of GMM including tests for over-identifying restrictions. This exercise is based on the wage equation discussed in Griliches (1976) and data used in Blackburn and Neumark (1992). Questions are reproduced here (sometimes truncated) for practical purposes. 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). Data from two advanced econometrics texts are used: Hayashi’s Econometrics and Favero’s Applied Macroeconometrics1. EXERCISE ON ESTIMATING GMM MODELS IN EVIEWS Introduction In this exercise we demonstrate how GMM models are estimated in EViews.