### 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 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 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 Consistent nonparametric smooth test of the benchmark normal copula function This table reports Chen et al. (2003) consistent nonparametric smooth test of the null hypothesis ()1 1 , , Pr :

2005

### 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 1: Nonparametric Lag Selection for Lynx Data

2000

"... In PAGE 14: ... We follow the suggested procedure of the last section and use only the CAF P E1 and the CAF P E2a criteria and for reasons of comparison, the linear Schwarz criterion ARSC. Table1 summarizes the results for the lynx data. Except for the CAF P E1 criterion all criteria include lag 1 and 2 in their selection.... In PAGE 14: ... Recalling the results of the previous section, these lags for the CAF P E2a may be due to over tting. To decide whether the more parsimonious model is su cient, we investigated the residuals of all suggested models using the bandwidths of Table1 and conclude that lags 1 and 2 are su cient. A plot of the estimated regression function on a relevant grid is shown in Figure 5.... In PAGE 15: ...and 3 using AF P E1 while Yao and Tong (1994) found lags 1, 3 and 6 using cross-validation. Insert Table1 about here Applying our methods to daily exchange rate data poses a di erent challenge. While there are plenty of data (3212 observations), this bene t is compromised as the data is known to be highly dependent (although only weakly correlated) and therefore asymptotics kick in very slowly.... ..."

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### Table 2: Parametric Crime Equation Estimates

2003

"... In PAGE 16: ... The results are also similar to those obtained by Gyimah-Brempong (2001) who used the same data set to investigate the efiects of alcohol availability on crime rates.12 The estimates in Table2 indicate that there is a positive relationship between alcohol avail- ability and crime, results that are consistent with previous research results. How robust are the estimated results? Is the crime equation correctly specifled? We investigate these issues by us- ing a consistent nonparametric conditional moment test to test for the correct speciflcation of the estimated equation.... In PAGE 19: ... This pattern of changing response of crime to alcohol availability at difierent levels of alcohol availability is consistent with the results obtained by Alaniz et al (1998) who argue that the concentration of alcohol outlets in a neighborhood often leads to the breakdown of all social controls and hence leads to increased crime rates in that neighborhood. How do the nonparametric estimates compare to the linear parametric estimates presented in Table2 above? In order to facilitate a direct comparison of results, we present a summary of the goodness-of-flt14 of the parametric and nonparametric estimates (R2 and root mean square error (RMSE)), along with the calculated elasticities of crime with respect to alcohol availability computed at the mean number of licenses and crime rates, Ec;a in Table 4. An examination of Table 4 reveals that the nonparametric model provides a better flt to the un- derlying relationship between crime rates and alcohol availability than the linear parametric model.... ..."

### Table 4 Alternative nonparametric smooth test of the benchmark normal copula function This table reports Chen et al. (2003) alternative (non-consistent) nonparametric test when the

2005

### Table 4: Summary Comparison of Parametric versus Nonparametric Models

2003

"... In PAGE 19: ... How do the nonparametric estimates compare to the linear parametric estimates presented in Table 2 above? In order to facilitate a direct comparison of results, we present a summary of the goodness-of-flt14 of the parametric and nonparametric estimates (R2 and root mean square error (RMSE)), along with the calculated elasticities of crime with respect to alcohol availability computed at the mean number of licenses and crime rates, Ec;a in Table 4. An examination of Table4 reveals that the nonparametric model provides a better flt to the un- derlying relationship between crime rates and alcohol availability than the linear parametric model. Speciflcally, the R2s shown in Table 4 indicate that the nonparametric model explains at least twice as much of the variation in crime rates as the linear parametric model does.... In PAGE 19: ... An examination of Table 4 reveals that the nonparametric model provides a better flt to the un- derlying relationship between crime rates and alcohol availability than the linear parametric model. Speciflcally, the R2s shown in Table4 indicate that the nonparametric model explains at least twice as much of the variation in crime rates as the linear parametric model does. We also note that the regression standard errors are much lower for nonparametric estimates than their parametric counterparts.... ..."

### Table 5: Pooled Sample: Non-Parametric and Parametric Tests

1999

"... In PAGE 4: ...Tables Table 1: Model 12TE: Estimation Outcomes Table 2: Pooled Sample (37 MUNIs, 39 ENEL): SFM3 Estimation Outcomes Table 3: MUNIs and their ENEL Peers (SFM3): Winners/Losers Table 4: MUNI/ENEL Efficiency Comparison (SFM3): Statistics of Interest Table5 : Pooled Sample: Non-Parametric and Parametric Tests... ..."

Cited by 1