An empirical comparison of nonparametric and parametric Engel functions

Nonparametric techniques are used to estimate income densities, Engel functions and their derivatives, and income elasticities. Nonparametric kernel methods give less ambiguous estimates of the densities than discrete, maximum penalised likelihood estimation. The nonparametric estimates of the Engel functions, their derivatives, and the income elasticities are compared with some corresponding parametric estimates. The nonparametrically estimated Engel functions provide a better fit to the data. Also, the nonparametric elasticity estimates are qualitatively similar to the parametric estimates. Some statistically significant differences are observed between the parametric versus nonparametric estimates, but not to the extent of having any major policy implications.     

A nonparametric method for term structure fitting with automatic smoothing

We present a nonparametric method for fitting the term structure of interest rates from bond prices. Our method is a variant of the smoothing spline approach, but within our framework we are able to determine the smoothing coefficient automatically from data using the generalized cross-validation or maximum likelihood estimates. We present an effective numerical algorithm to simultaneously find the term structure and the optimal smoothing coefficient. Finally, we compare the proposed nonparametric fitting method with other parametric and nonparametric methods to find its superior performance. We find that existing term structure fitting methods perform well in liquid markets while illiquid markets present new challenges, which we address in this article.     

Parametric versus nonparametric methods in risk scoring: an application to microcredit

The importance of credit access to improve economic opportunities in developing markets is well established in the literature. However, there exists a strong need to mitigate adverse selection problems in microlending. A risk scoring model that more accurately predicts the likelihood of repayment of potential borrowers can help address this market imperfection and to benefit both lenders and borrowers. This paper compares the performance of nonparametric versus semiparametric and traditional parametric risk scoring models based on default probabilities. We show the advantages of relying on less structured, data-driven methods for risk scoring using both simulated data and data from credit loans granted to small and microenterprises in rural Peru. The estimation results indicate that nonparametric methods lead to a better evaluation of credit worthiness and can help prevent including potential “bad” borrowers and excluding “good” borrowers from sensitive microcredit markets.     

Alcohol availability and crime: a robust approach

The relationship between alcohol availability and crime is investigated in this study. It first considers common parametric specifications that have been used in the literature. After applying a powerful consistent conditional moment test for correct specification, it is found that these common parametric specifications are rejected by the data. The study then proceeds with a robust nonparametric method that can have a rate of convergence close to that for a correctly specified parametric model when the underlying relationship is somewhat linear. The application of nonparametric methods reveals structure present in the data that would remain undetected when applying common parametric specifications, but more importantly reveals that the impact of alcohol availability is considerably higher than one might believe on the basis of the misspecified parametric model. It is also found that the marginal effect of alcohol availability on crime changes with the level of alcohol availability.     

Real-time data revisions and the PCE measure of inflation

This paper tracks data revisions in the Personal Consumption Expenditure using the exclusions-from-core inflation persistence model. Keeping the number of observations the same, the regression parameters of earlier vintages of real-time data, beginning with vintage 1996:Q1, are tested for coincidence against the regression parameters of the last vintage of real-time data, used in this paper, which is vintage 2008:Q2 in a parametric and two nonparametric frameworks. The effects of data revisions are not detectable in the vast majority of cases in the parametric model, but the flexibility of the two nonparametric models is able to utilize the data revisions.     

Nonparametric quantile regression analysis of R&D-sales relationship for Korean firms

This paper applies the nonparametric quantile regression estimation procedure to the analysis of the innovation-firm size relationship using Korean manufacturing firms data. Due to the high asymmetric distribution of R&D expenditure, the mean regression does not capture properly the stylized facts of R&D behavior; hence it underestimates the sales elasticity. Comparing the parametric estimates and nonparametric estimates allows us to see that there exists a nonlinear relationship in innovative activity and sales. Dividing the data into three groups according to the sales volume, the elasticity in the medium-sized firms is the biggest for scientific firms. This result conforms that the findings of Scherer (1965) coincide with findings from Korean manufacturing firms data in the sense that R&D expenditure tends to increase faster than firm size with size up to a point and then more slowly among larger firms. For the non-scientific firms, it steadily increases showing increasing returns to scale in innovative activity in large firms.     

Nonparametric regression estimation with general parametric error covariance: a more efficient two-step estimator

Recently Martins-Filho and Yao (J Multivar Anal 100:309–333, 2009) have proposed a two-step estimator of nonparametric regression function with parametric error covariance and demonstrate that it is more efficient than the usual LLE. In the present paper we demonstrate that MY’s estimator can be further improved. First, we extend MY’s estimator to the multivariate case, and also establish the asymptotic theorem for the slope estimators; second, we propose a more efficient two-step estimator for nonparametric regression function with general parametric error covariance, and develop the corresponding asymptotic theorems. Monte Carlo study shows the relative efficiency loss of MY’s estimator in comparison with our estimator in nonparametric regression with either AR(2) errors or heteroskedastic errors. Finally, in an empirical study we apply the proposed estimator to estimate the public capital productivity to illustrate its performance in a real data setting.     

Structure of Financial Savings During Indian Economic Reforms

This paper conducts nonparametric tests to examine whether data on financial savings in India can be rationalized in terms of a utility function of a representative economic agent. The nonparametric test has an advantage over its parametric counterpart in that it does not assume the existence of a utility function per se and checks whether the representative consumer’s demand structure can at all be rationalized by a utility function. Our test results of the necessary and sufficient conditions of the weak separability hypothesis suggest that data on financial savings in India are consistent with the existence of a utility function for a representative individual with a sub-preference where contractual savings (insurance and provident funds) can be separated out. This result could facilitate the construction of a suitable financial aggregate using these assets.     

Recent approaches to estimating Engel curves

Classical approaches of estimating cross-section Engel curves are based on parametric models. However, misspecification of a parametric model implies that information of structural nature might be masked. An alternative avoiding problems related to predetermined functional relations is the nonparametric approach. This paper surveys recent advances of nonparametric statistics in their relevance to estimating cross-section Engel curves.     

Nonparametric kernel estimation applied to forecasting: An evaluation based on the bootstrap

The results reported in this paper lend support to the nonparametric approach to estimating regression functions. This conclusion is based on a comparison of two sets of eight quarterly forecasts of U.S. hog supply generated by a well specified parametric dynamic model and by nonparametric kernel estimation. Despite the relatively small sample size, the nonparametric point forecasts are found to be as accurate as the parametric forecasts according to the mean square error and mean absolute error criteria. Bootstrap resampling is used to estimate the distributions of the forecast errors. The results of this exercise favour the nonparametric forecasts, which are found to have a tighter distribution.     

Nonparametric Generalized Least Squares in Applied Regression Analysis

This paper compares a nonparametric generalized least squares (NPGLS) estimator to parametric feasible GLS (FGLS) and variants of heteroscedasticity robust standard error estimators (HRSE) in an applied setting. NPGLS consistently estimates the unknown scedastic function and produces more efficient parameter estimates than HRSE. We apply these various approaches for handling heteroscedasticity to data on professor rankings obtained from RateMyProfessors.com. We find that the statistical significance of key variables differs across seven versions of HRSE, leading to different conclusions, and a standard parametric approach to FGLS suffers from misspecification. NPGLS combines the virtues of both of these parametric approaches.     

The relationship between childhood obesity and food insecurity: a nonparametric analysis

Childhood obesity and food insecurity are major public health concerns in the United States and other developed countries. Research on the relationship between the two has provided mixed results across a variety of data sets and empirical methods. Common throughout this research, however, is the use of parametric frameworks for empirical analyses. This study moves beyond parametric methods by examining the relationship between childhood obesity and food insecurity among low-income children with nonparametric regression techniques. We examine data from the Child Development Supplement (CDS) of the Panel Study of Income Dynamics (PSID), a nationally representative data set from the US. Consistent with recent work, our parametric analyses indicate that there is no statistically significant relationship between childhood obesity and food insecurity. In contrast, our nonparametric results indicate that the probability of being obese varies markedly with the level of food insecurity being experienced by the child. Moreover, this relationship differs across relevant subgroups including those defined by gender, race/ethnicity and income. Fully understanding the relationship between childhood obesity and food insecurity has significant policy implications.     

On nonparametric estimation of the Schumpeterian link between innovation and firm size: Evidence from Belgium,France, and Germany

This paper analyses the Schumpeterian link between innovative activity and firm size by means of the nonparametric Nadaraya-Watson estimator and of the partially linear approach by Speckman. Four data sets referring to the manufacturing industries of three European countries are available for the analysis. We demonstrate how nonparametric methods can produce more reliable conclusions than conventional methods. For this purpose, the roles of bandwidth choice, wild bootstrap, density estimation and trimming are studied. For the German data set of 1984 and for the French data set we find that small firms and large firms are more innovative than firms of intermediate size while the relation is rather hump-shaped for Germany 1989 and decreasing for Belgium. Including an additional parametric component into the estimations based on the French data contributes considerably to the explanation of innovative activity without affecting the U-shaped link between innovation and firm size.     

Inequality in democracies: Testing the classic democratic theory of redistribution

The classic democratic theory of redistribution claims that an increase in the mean-to-median (MM) income ratio causes a majority coalition in the electorate to collectively demand more redistribution. The functional dependence of redistribution on the MM income ratio is tested in parametric and nonparametric regression frameworks using an OECD panel dataset. While the parametric regression model is found to be misspecified rendering subsequent inference invalid, the robust nonparametric regression model fails to uncover evidence that the MM income ratio is relevant for predicting redistribution.     

An international study of persistence in output: Parametric estimates using the data dependent systems approach

This paper parametrically obtains estimates of persistence in output using Pandit's (1977, 1982) Data Dependent Systems approach for modelling autoregressive and moving average processes. The estimates are based on over a century of annual data for the rate of change of output in nine industrialized countries. The sensitivity of estimates to various model selection criteria is examined. While persistence in output is found to be sensitive to model selection criteria, the output of all countries including the United States is found to have a substantial degree of persistence if the ARMA models are chosen according to the Schwarz Bayesian Criterion, but excluding the ARMA models whose moving average roots are near the unit root (which involves pile-up phenomenon). Moreover, the parametric estimates of persistence are shown not to have the known upward bias problem commonly associated with parametric estimates of persistence relative to nonparametric estimates.     

Bank Competition,Economic Growth,and Nonlinearity: A Nonparametric Approach

This article asks empirically the question about potential nonlinearities in the link between bank competition and aggregate economic growth in a cross‐section of countries. I employ nonparametric methods, primarily kernel estimation, and several measures of banking competition. The results suggest that while the data show the presence of relevant nonlinearities, the available evidence provides only limited support for nonmonotonicities such as inverted u‐shapes, and so contrasts with previous findings obtained using parametric methods. This study corroborates the traditional view, according to which more bank competition fosters growth, with presumed implications for policy.     

Fractional Integration of Nominal Exchange Rates: Evidence from CEECs in the Light of EMU Enlargement

This paper uses fractional integration models to describe the long‐run dependence of nominal exchange rates in Central and Eastern European countries (CEECs). The analysis is validated using nonparametric, semiparametric and parametric techniques. From comparing the results across the three approaches, it was clear that mean reversion takes places only for the euro exchange rates in Bulgaria, Estonia, and Slovenia. Other exchange rates based on the euro also display mean reversion with the parametric methods. For the US dollar rates, the unit‐root null hypothesis cannot be rejected in any single country, indicating that shocks affecting the exchange rates against the US dollar are of a permanent nature, while those directed against the euro are less persistent, and tend sometimes to disappear in the long run. Policy implications are derived.     

Why Can’t I keep my Surname? The Fairness and Welfare of the Japanese Legal System

This study examines the welfare and fairness implications of Japan’s current policy on marriage surnames versus the proposed revised family law, which would enable husbands and wives to retain their premarital surnames. The study compares welfare in these two legal states, with a married couple’s welfare dependent on marriage-surname choice. It reviews the external preferences of anti-revisionists by the fairness criteria of impersonality or extended sympathy. Utilizing web-based survey data, the study conducts nonparametric rank analysis and parametric analysis of willingness to pay (WTP) for surname retention and legal support. Moreover, it conducts a structural equation analysis via a multiple indicators multiple causes (MIMIC) model, incorporating surname attachment and fairness as latent variables. The study shows that the revised law can increase welfare and that external disutility of the legal revision is invalid on fairness grounds.     

Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts

This article considers alternative methods to calculate the fair premium rate of crop insurance contracts based on county yields. The premium rate was calculated using parametric and nonparametric approaches to estimate the conditional agricultural yield density. These methods were applied to a data set of county yield provided by the Statistical and Geography Brazilian Institute (IBGE), for the period of 1990 through 2002, for soybean, corn and wheat, in the State of Paraná. In this article, we propose methodological alternatives to pricing crop insurance contracts resulting in more accurate premium rates in a situation of limited data.     

Measuring input substitution and output expansion effects: A nonparametric approach with application

A simple framework is developed for measuring input substitution and output expansion effects. These measures are nonparametric in the sense that specification and/or estimation of any parametric functions are not resquired. Monte Carlo experiments performed in the paper demonstrate the superiority of the proposed approach. An empirical application to Japanese manufacturing data yields results which satisfy a priori expectations.