### Table 6. Nonparametric estimates using pooled data.

in Nonparametric Estimation Of Labor Supply Functions Generated By Piece Wise Linear Budget Constraints

"... In PAGE 30: ...ncome. Both the elasticity and coefficient estimates show this pattern. The nonparametric elasticity estimate is smaller than the parametric one for the wage rate and larger for nonlabor income. Also, for the nonparametric estimates in the first column of Table6 , the coefficient of w3 is smaller than is the wage coefficient for the parametric estimate in equation (14). As previously noted, the coefficient of w3 gives the wage effect for a linear budget set, because dw is identically zero in that case.... In PAGE 33: ...assuming homoskedasticity leads to a simple Hausman test of the distributional assumption. Comparing the coefficient of w3 in the first column of Table6 with the coefficient of w in the first column of Table 7 gives a Hausman statistic 6.53, that should be a realization of a standard normal distribution.... ..."

### Table 5. Nonparametric estimation on all years. Cross-validation values

in Nonparametric Estimation Of Labor Supply Functions Generated By Piece Wise Linear Budget Constraints

### Table 4: Non-parametric estimation without a kernel. a: Without Heteroskedasticity. T serial

1997

"... In PAGE 43: ...o zero, then ut is homoskedastic (condition (2.31) is satisfied). When the BD filter is used, the product Vt displays a fairly simple pattern of serial correlation pattern, whereas the BHP filter generates relatively complicated serial correlation. Table4 reports the confidence intervals obtained for a test of the null hypothesis of no covariance between ut and xt . The R95 estimator is compared with the QS estimator of Andrews (1991) without prewhitening.... ..."

Cited by 4

### Table 4 Non-Parametric Estimates of the Variance of the Innovation in the R.W. Component as a Ratio to the Total Variance of the Series (V)

"... In PAGE 13: ....[ ] [. [ / ]]/ = + 075 1 12 (13) Estimates of V k for alternative values of k are reported in Table4 for the series under consideration. The results are broadly consistent with those derived using the Yule-Walker equations.... ..."

### Table 3: Discrete Time Hazard Estimation of Age at First Birth Males with Primary

"... In PAGE 18: ...17 5. Empirical Results In this section we discuss separately the effects of each of the covariates on the age at marriage (Table 2), age at first birth ( Table3 ) and the duration of subsequent birth intervals (Tables 4 and 5). Although we have also estimated the intervals from marriage to a first birth, we do not discuss the results due to some problems.... ..."

### Table 1: Quadratic or Nonparametric?

2001

"... In PAGE 8: ... The squared L2 risks of the estimators are computed based on 100 replications. The numbers in the parentheses in Table1 are the corresponding standard errors. Quadratic regression works much better than the nonparametric alternatives for the rst two cases, but becomes much worse for the latter two due to lack of exibility.... ..."

Cited by 13

### Table 4: Discrete Time Hazard Estimation of Second Birth Interval Males with Primary

### Table 5: Discrete Time Hazard Estimation of Third Birth Interval Males with Primary

### Table 2.14: Characteristics of Density Estimation Approach Non-Parametric Approach Gaussian GMM

2004

### Table 2. Standard deviation estimates (ms) for the three di erent L-curves as a function of discretization for 100 re- alizations. The true standard deviation is = 2 ms, whereas the nonparametric estimate b = 2:02 ms.

"... In PAGE 7: ... We adopted a much simpler strategy by comparing the standard deviation estimates for up to 300 layers. Table2 shows that the error esti- mation is reasonably robust to discretization. By com- paring model-independent and model-based variance es- timates we see that 10 layers are enough to explain the variations in the data.... ..."