### Table 8: Groups of Goods of the Nonparametric Model

2006

### Table 8: Groups of Goods of the Nonparametric Model

2006

### 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 7: Nonparametric Model Tests Percent of population not rejecting each hypothesis at the 95% confidence level

2006

"... In PAGE 18: ... Gasoline demand itself has almost the same own price elasticity as in the aggregate data, and shows many similar cross price effects (again including a small positive cross effect on other transportation), though these nonparametric cross price effects are not estimated with enough precision to be statistically significant. Table7 lists results of various tests of rationality restrictions. We evaluate the estimated nonparametric demand functions and their derivatives at each data point, and for each ob- servation we test whether the demand functions at that point satisfy homogeneity, negative semidefiniteness, and Slutsky symmetry.... In PAGE 18: ... We evaluate the estimated nonparametric demand functions and their derivatives at each data point, and for each ob- servation we test whether the demand functions at that point satisfy homogeneity, negative semidefiniteness, and Slutsky symmetry. For each hypothesis, Table7 lists the percent of observations at which the hypothesis is not rejected at the 0.95 confidence level.... In PAGE 18: ...95 confidence level. Table7 shows that homogeneity (the absence of money illusion) is generally accepted, with a rejection rate of 11% of the data when we allow for endogenous regressors. We test symmetry and negative semidefiniteness of the composite commodity Slutsky matrix tildewide S(r, y, z) both with and without imposing homogeneity.... ..."

### Table 7: Nonparametric Model Tests Percent of population not rejecting each hypothesis at the 95% confldence level

2006

"... In PAGE 19: ... Gasoline demand itself has almost the same own price elasticity as in the aggregate data, and shows many similar cross price efiects (again including a small positive cross efiect on other transportation), though these nonparametric cross price efiects are not estimated with enough precision to be statistically signiflcant. Table7 lists results of various tests of rationality restrictions. We evaluate the estimated nonparametric demand functions and their derivatives at each data point, and for each ob- servation we test whether the demand functions at that point satisfy homogeneity, negative semideflniteness, and Slutsky symmetry.... In PAGE 19: ... We evaluate the estimated nonparametric demand functions and their derivatives at each data point, and for each ob- servation we test whether the demand functions at that point satisfy homogeneity, negative semideflniteness, and Slutsky symmetry. For each hypothesis, Table7 lists the percent of observations at which the hypothesis is not rejected at the 0.95 confldence level.... In PAGE 19: ...95 confldence level. Table7 shows that homogeneity (the absence of money illusion) is generally accepted, with a rejection rate of 11% of the data when we allow for endogenous regressors. We test symmetry and negative semideflniteness of the composite commodity Slutsky matrix e S(r; y; z) both with and without imposing homogeneity.... ..."

### Table 4. Gini coefficients of income of heads, static and dynamic effects predicted by nonparametric model

### Table 5. Gini coefficients of equivalent income, static and dynamic effects predicted by nonparametric model

### Table 3: Comparison of parametric growth models and nonparametric curve fitting.

2007

"... In PAGE 24: ...complexity and run time (especially when considering large auction datasets). Table3 summarizes the comparison of the parametric and nonparametric curve fitting on all these dimensions. In terms of fitted curves, growth models have the advantages of fitting monotone curves, fitting directly to the live bids (in some cases with the addition of the price at the start and end of the auction), and fitting any number of bids, including single-bid auctions (using the additional start and end prices).... ..."

### Table 2. Discrete Time Stochastic Volatility Models Estimated from Stock Prices: Optimized Value of the Criterion for the Non- linear Nonparametric Score. Model

1995

"... In PAGE 15: ... Their work is summarized in two tables reproduced here as Tables 1 and 2. ||||||||||||{Table 1 about here ||||||||||||{ ||||||||||||{ Table2 about here ||||||||||||{ Table 1 shows the optimized values of the EMM objective function scaled to follow the chi- squared distribution, as described in Section 2. From the top panel of the table it is seen that the standard stochastic volatility model with Gaussian errors... In PAGE 18: ...Table2 displays the objective function surface for versions of the stochastic volatility model against the Nonlinear Nonparametric Score. The standard model is overwhelmingly rejected.... ..."

### Table 1. Maximized log-likelihood and BIC for the three nonparametric Markov chain models.

"... In PAGE 9: ... The number of observations is a17 a4 a3 a19 a21 a17a9a19a9a19a41a4a7a6a14a9a12a8a86a17a15a12 a3 . Table1 displays the maximized log- likelihood a18a21a20a23a22 a38 a9 a75 , a78 a4 a21a27a8a15a6 a8a11a10 and the corresponding BIC for the four geographic regions. Table 2 shows the resulting values of the likelihood ratio test statistic and the corresponding p-values.... In PAGE 20: ... Evidently, when performance is gauged in terms of the area under the ROC curve, the models perform better in the North (both Northeast and Northern tor- nado alley) than in the South. This is the same pattern displayed according to the BIC criterion ( Table1 ). In terms of the reliability MSE, the model performs well in SE and NT but its performance in NE is only marginal.... In PAGE 27: ... Table1 . Maximized log-likelihood and BIC for the three nonparametric Markov chain models.... ..."

Cited by 1