Problem 3 [18 marks Suppose we are interested in studying labor force participat

Question

Problem 3 [18 marks Suppose we are interested in studying labor force participation for married women. Let y, be the number of hours worked by individual i and W, be a binary variable that takes value 1 if yi 0 and 0 otherwise. We consider a sample of 753 married women which consists of 428 who worked for a wage outside the home during the year For the woman who worked positive hours, the range is fairly broad, extending from 12 hours to 4,950 hours per year. The primary objective of the analysis is to determine the impact of education, experience and children on the women's decision to enter the labor force. Table1 reports the estimation results for three models. and 325 who worked zero hours. Table 1: Estimation results for number of hours worked y, in Model 1 and Model 2 and labor market participation variable W, in Model 3. The last Column reports the ratio of the coeffi cients estimates in model 2 divided by 1122.02. Model 1 linear (OLS) 3.45 (2.54) 28.76 (12.95) 65.67 9.96) -0.7 (0.325) 30.51 (4.36) 442.09 (58.85) -32.78 (23.18) 330.48 (270.78) Model 2 Tobit (MLE) 8.81 (4.46) 80.65 (21.58) 131.56 (17.28) 1.86 (0.54) 54.41 (7.42) -894.02 Model 3 Probit (MLE) 0.012 (0.005) 0.131 (0.025) 0.123 (0.019) 0.0019 (0.0006) 0.053 (0.008) 0.868 (0.119) 0.036 (0.043) 0.270 (0.509) -401.30 0.221 Model 2 betas, 0.0078 0.0718 0.1172 0.0016 0.04849 0.7968 0.0144 0.8603 nwifeinc educ exper age kidslt6 kidsge6 -16.22 (38.64) 965.31 (446.44) 3819.09 0.274 1122.02 constant log-likelihood value R-squared 0.266 750.18 Heteroskedasticity robust standard errors in parenthesis. Xi-nwifenc is equal to (familly income-wage of the woman × hours)/1000 X2 educ is woman's years of schooling Xy-exper is the actual labor market experience in years X4 exer is the square of exper Xs =age is the woman's age in years. X6 kids!t6 is the number of kids

Explanation / Answer

A. assumption of liner model on constant and linear model- There must be a linear relationship between the outcome variable and the independent variable.  

Assumption of normal distribution-for a normally distributed population, the sampling distribution is also normal when there are sufficient test items in the samples. the assumption of normality is valid in most cases but when it is not, it could lead to serious trouble.

Assumption of probit model - logistic regression does not make many of the key assumptions of linear regression and general linear models that are based on ordinary least squares algrithm-particularly regarding linearity, normality, homoscedasticity and measurement level.

F. TOBIT MODEL ALSO CALLED A CENSORED REGRESSION MODEL, IS DESIGNED TO ESTIMATE LINEAR RELATIONSHIPS BETWEEN VARIABLES WHEN THERE IS EITHER LEFT OR RIGHT CENSORING IN THE DEPENDENT VARIABLE.

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