Partial parametric estimation for nonstationary nonlinear regressions

This paper proposes an estimation method for a partial parametric model with multiple integrated time series. Our estimation procedure is based on the decomposition of the nonparametric part of the regression function into homogeneous and integrable components. It consists of two steps: In the first step we parameterize and fit the homogeneous component of the nonparametric part by the nonlinear least squares with other parametric terms in the model, and use in the second step the standard kernel method to nonparametrically estimate the integrable component of the nonparametric part from the residuals in the first step. We establish consistency and obtain the asymptotic distribution of our estimator. A simulation shows that our estimator performs well in finite samples. For the empirical illustration, we estimate the money demand functions for the US and Japan using our model and methodology.     

Testing and imposing Slutsky symmetry in nonparametric demand systems

Maximization of utility implies that consumer demand systems have a Slutsky matrix which is everywhere symmetric. However, previous non- and semi-parametric approaches to the estimation of consumer demand systems do not give estimators that are restricted to satisfy this condition, nor do they offer powerful tests of this restriction. We use nonparametric modeling to test and impose Slutsky symmetry in a system of expenditure share equations over prices and expenditure. In this context, Slutsky symmetry is a set of nonlinear cross-equation restrictions on levels and derivatives of consumer demand equations. The key insight is that due to the differing convergence rates of levels and derivatives and due to the fact that the symmetry restrictions are linear in derivatives, both the test and the symmetry restricted estimator behave asymptotically as if these restrictions were (locally) linear. We establish large and finite sample properties of our methods, and show that our test has advantages over the only other comparable test. All methods we propose are implemented with Canadian micro-data. We find that our nonparametric analysis yields statistically significantly and qualitatively different results from traditional parametric estimators and tests.     

Education,income, and ability revisited

The paper examines two approaches to the omitted variable problem. Both of them try to correct for the omitted variable bias by specifying several equations in which the unobservable appears. The first approach assumes that the common left out variable is the only thing connecting the residuals from these equations, making it possible to extract this common factor and control for it. The second approach relies on building a model of the unobservable, by specifying observable variables which are causally related to it. A combination of these two methods is applied to the 1964 CPS-NORC veterans sample in order to evaluate the bias in income- schooling regressions caused by the omission of an unobservable initial ‘ability’ variable.     

Maximum likelihood estimation of monotone and concave production frontiers

In this paper we bring together the previously separate parametric and nonparametric approaches to production frontier estimation by developing composed error models for maximum likelihood estimation from nonparametrically specified classes of frontiers. This approach avoids the untestable restrictions of parametric functional forms and also provides a statistical foundation for nonparametric frontier estimation. We first examine the single output setting and then extend our formulation to the multiple output setting. The key step in developing the estimation problems is to identify operational constraint sets to ensure estimation from the desired class of frontiers. We also suggest algorithms for solving the resulting constrained likelihood function optimization problems.The refereeing process of this paper was handled through R. Robert Russell. Helpful comments from Bob Russell and two anonymous referees are gratefully acknowedged. We are, of course, solely responsible for any remaining errors or omissions.     

Identification and estimation in sequential,asymmetric, English auctions

Within the independent private-values paradigm, we derive the data-generating process of the winning bid for the last unit sold at multi-unit, sequential, asymmetric, English auctions. When the identity of the winner and the number of units won by each bidder in previous stages of the auction are observed, we demonstrate nonparametric identification and propose a semi-nonparametric estimation strategy based on orthogonal polynomials. We apply our estimator to daily data from fish auctions in Denmark. For single-unit supply, we use our estimates to compare the revenues a seller could expect to earn were a Dutch auction employed instead.     

The effects of geographic expansion on the productivity of Spanish savings banks

This article analyzes the effects of geographic expansion on the productivity of Spanish savings banks. The study uses data from 1992 to 2004, the period when most savings banks expanded geographically. We consider an alternative approach to most multi-stage studies, which uses nonparametric methods both to measure productivity growth and to analyze its relationship with branch office expansion. Specifically, we use nonparametric regression techniques and their natural complement, conditional density estimation. Results indicate that savings banks that expand geographically outside their natural markets achieve greater productivity gains. However, there are some firms for which this result is more moderate. In contrast, lower increases in productivity are found in savings banks that expand on a nationwide basis, or that confine their territorial expansions to their traditional markets.     

Effect of FDI and Time on Catching Up: New Insights from a Conditional Nonparametric Frontier Analysis

We use an appropriate nonparametric two‐step approach on conditional efficiencies to investigate how foreign direct investment (FDI) and time affect the process of catching up. By using a dataset of 44 countries over 1970–2007, we explore the channels under which FDI fosters productivity by disentangling the impact of this factor on the production process and its components: impact on the attainable production set (input–output space) and the impact on the distribution of efficiencies. We extend existing methodological tools—conditional nonparametric efficiency measures—to examine these interrelationships. We emphasize the usefulness of smoothing over time to better analyze the potential dynamic influence of FDI on efficiency. We find that both FDI and time play an important role as influencing efficiency distribution and affecting, to a smaller extend, the production set. This effect of FDI does not seem to vary much over time. By the second‐stage nonparametric regression of the conditional efficiencies over FDI and time we identify clearly the effect of time and FDI on conditional efficiency and we determine idiosyncratic efficiency, which represents the‘Solow residual’, measured by looking to the unexplained part of the conditional efficiencies. Copyright © 2014 John Wiley & Sons, Ltd.     

Single-market disequilibrium models : Estimating and testing

The present paper investigates several issues of estimation and hypothesis testing in the context of a single-market disequilibrium model. The paper attempts to shed light on the following four questions: (1) What are the small-sample properties of the maximum likelihood estimator in various disequilibrium models? (2) How can one test the hypothesis of equilibrium vs. disequilibrium? (3) Can one reasonably estimate the unobservable demand and supply quantities from observable data? and (4) What are the consequences of using an equilibrium model instead of a disequilibrium one, or of using a misspecified disequilibrium model? Each of these questions is examined with the aid of sampling experiments.     

Semiparametric three‐step estimation methods for simultaneous equation systems

This paper proposes a new method for estimating a structural model of labour supply in which hours of work depend on (log) wages and the wage rate is considered endogenous. The main innovation with respect to other related estimation procedures is that a nonparametric additive structure in the hours of work equation is permitted. Though the focus of the paper is on this particular application, a three‐step methodology for estimating models in the presence of the above econometric problems is described. In the first step the reduced form parameters of the participation equation are estimated by a maximum likelihood procedure adapted for estimation of an additive nonparametric function. In the second step the structural parameters of the wage equation are estimated after obtaining the selection‐corrected conditional mean function. Finally, in the third step the structural parameters of the labour supply equation are estimated using local maximum likelihood estimation techniques. The paper concludes with an application to illustrate the feasibility, performance and possible gain of using this method. Copyright © 2005 John Wiley & Sons, Ltd.     

我国普通商品住房供求的影响因素及其稳定性研究

本文利用1997年到2009年全国以及各地区的房地产面板数据,借鉴蛛网模型的相关理论,构建供给与需求的联立方程,选择固定效应IV估计法拟合面板联立方程模型,对普通商品住房供求的影响因素及其稳定性进行了实证研究。结果表明,我国的普通商品住房市场处于不稳定状态,普通商品住房当期及滞后期的价格、城镇人均可支配收入、城镇就业人口、土地购置面积、经济适用房、别墅的供求状况等因素都对普通商品住房的供求变化产生较为显著的影响。     

Residential choice,hedonic prices,and the demand for urban air quality

This paper uses a two-step estimation procedure suggested by Sherwin Rosen to estimate structural demand and supply equations for urban air quality. In the first step, a hedonic price equation is estimated for residential property values for the Washington, D. C., SMSA for 1970. In the second step, a set of marginal hedonic prices is generated. These prices and the quantity of clean air (reciprocal of air pollution) are used as endogenous variables in a simultaneous equation model. Empirical results indicate a price elasticity of demand between ?1.2 and ?1.4 and a unitary income elasticity.     

Nonparametric estimation of the impact of taxes on female labor supply

This paper proposes a simple extension of nonparametric estimation methods for nonlinear budget‐set models derived in Blomquist and Newey ( 2002 ) to censored dependent variables. The nonparametric method is applied to estimate female labor supply elasticities using data on married women from the 1985 and 1989 waves of the Panel Study of Income Dynamics, exploiting the substantial variation in budget sets caused by the Tax Reform Act of 1986 as a source of identification. The estimated wage elasticities from this new method are 0.56 overall and 0.27 on the intensive margin. The income elasticity estimates are close to ? 0.67 overall and ? 0.13 on the intensive margin. Compared with the linear labor supply model, the estimated elasticities are usually larger for the nonparametric specifications that account for nonlinear budget sets. Copyright © 2010 John Wiley & Sons, Ltd.     

Does land use planning shape regional economies? A simultaneous analysis of housing supply,internal migration and local employment growth in the Netherlands

Why has job growth over the past decades been weaker in the Dutch Randstad area than in surrounding regions? In a simultaneous equations analysis, we find that employment has adjusted to the regional supply of labour. Net internal migration was predominantly determined by regional housing supply, and not by employment growth. Growth of the regional housing stock appeared insensitive to changes in the number of people and jobs. This lack of responsiveness to demand conditions is consistent with the presence of strong restrictions on residential development near the main Dutch cities, suggesting that the current regional distribution of economic activity in the Netherlands reflects land use planning decisions.     

Nonparametric Employment Subcenter Identification

A two-stage procedure is proposed for identifying urban employment subcenters. The first stage identifies candidate subcenters as significant positive residuals in a smoothed employment density function. Subcenters are those sites that provide significant explanatory power in the second-stage, semiparametric employment density function estimation. The procedure can be applied to either aggregated or disaggregated data, does not require detailed knowledge of the study area, and is easily reproducible by other researchers. Results are presented for five previously studied cities—Chicago, Dallas, Houston, Los Angeles, and San Francisco—and a new one, New Orleans.     

Instrumental variable estimation of nonseparable models

There are many environments where knowledge of a structural relationship is required to answer questions of interest. Also, nonseparability of a structural disturbance is a key feature of many models. Here, we consider nonparametric identification and estimation of a model that is monotonic in a nonseparable scalar disturbance, which disturbance is independent of instruments. This model leads to conditional quantile restrictions. We give local identification conditions for the structural equations from those quantile restrictions. We find that a modified completeness condition is sufficient for local identification. We also consider estimation via a nonparametric minimum distance estimator. The estimator minimizes the sum of squares of predicted values from a nonparametric regression of the quantile residual on the instruments. We show consistency of this estimator.     

Slowdown of credit flows in Jordan in the wake of the global financial crisis: Supply or demand driven?

This paper estimates a disequilibrium model of credit supply and demand to evaluate the relative role of these factors in the slowdown of credit flows in the Jordanian economy in the wake of the global financial crisis. The empirical analysis suggests that the credit stagnation is mainly driven by the restricted credit supply amid tighter monetary policy conditions in Jordan relative to the United States, as evidenced by the widened interest differential between the Central Bank of Jordan (CBJ) re-discount and the U.S. Federal Reserve funds rates. Although it appears that demand side factors related to the slowdown of economic activity have also had an impact, their role has been relatively modest. The estimation results imply that economic policies targeted towards stimulating the supply of credit are likely to be a more effective tool for expanding credit flows relative to demand stimulating policies.     

A primal-dual approach to nonparametric productivity analysis: The case of U.S. agriculture

Nonparametric methods for measuring productivity indexes based on bounds for the underlying production technology are presented. Following Banker and Maindiratta, the lower bound is obtained from a primal approach while the upper bound corresponds to a dual approach to nonparametric production analysis. These nonparametric bounds are then used to estimate input-based and output-based distance functions. These radial measures provide the basis for measuring productivity indexes. Application to times series data on U.S. agriculture indicates a large gap between the primal lower bound and the dual upper bound. This generates striking differences between the primal and dual nonparametric productivity indexes.Respectively, professor and associate professor of Agricultural Economics, University of Wisconsin-Madison. Seniority of authorship is equally shared. We would like to thank Rolf Färe and an anonymous reviewer for useful comments on an earlier draft of the paper. This research was supported in part by a Hatch grant from the College of Agriculture and Life Sciences, University of Wisconsin-Madison.     

The measurement of pay discrimination between job assignments

The traditional Becker/Arrow model of taste discrimination in pay depicts majority and minority labour as perfectly substitutable, implying that all workers perform precisely the same job assignment and have the same qualifications. The model is thus only appropriate for determining whether ceteris paribus pay differences between white workers and non-white workers, for example, performing job assignment A are attributable to prejudice (‘within-assignment discrimination’). The model is inappropriate for determining whether ceteris paribus pay differences between white workers in assignment A and non-white workers in assignment B reflect prejudice (‘cross-assignment discrimination’). We extend the traditional model to allow for cross-assignment discrimination and we propose an empirical methodology for its estimation. In so doing we address two broad questions: (1) Do predictions about cross-assignment discrimination vary with the form of the production function?; and (2) How can one estimate such discrimination when there is no common measure of productivity? We address the first question by deriving a measure of cross-assignment discrimination for four different production functions—Generalized Leontief, Quadratic, CES, and Cobb-Douglas. The Generalized Leontief provides the most general results, although closed form solutions are not possible. Closed form solutions are obtainable from the other three functions, but only under restrictive assumptions. There are two main findings. First, most predictions are generally robust across functional forms. Second, cross-assignment discrimination depends upon productivity and labour supply differences between the two worker groups, labour market structure, and the interaction between relative group productivity and prejudice. We address the second question by outlining, for future exploration, a two-stage regression methodology in which a standardised (i.e. common) measure of productivity is estimated separately for each occupation. This measure is then incorporated as a right-hand-side explanatory variable in a second-stage, all-occupation regression designed to estimate cross-assignment discrimination. We discuss the proposed methodology with reference to a valuable and interesting test case: The market for professional sports players.     

Local polynomial estimation of nonparametric simultaneous equations models

We define a new procedure for consistent estimation of nonparametric simultaneous equations models under the conditional mean independence restriction of Newey et al. [1999. Nonparametric estimation of triangular simultaneous equation models. Econometrica 67, 565–603]. It is based upon local polynomial regression and marginal integration techniques. We establish the asymptotic distribution of our estimator under weak data dependence conditions. Simulation evidence suggests that our estimator may significantly outperform the estimators of Pinkse [2000. Nonparametric two-step regression estimation when regressors and errors are dependent. Canadian Journal of Statistics 28, 289–300] and Newey and Powell [2003. Instrumental variable estimation of nonparametric models. Econometrica 71, 1565–1578].     

Nonparametric methods in sample surveys. Application to the estimation of cancer prevalence

We address the problem of the estimation of the population mean and the distribution function using nonparametric regression. These methods are being used in a wide range of settings and areas of research. In particular, they are a good alternative to other classical methods in the survey sampling context, since they work under the assumption that the underlying regression function is smooth. Some relevant nonparametric regression methods in survey sampling are presented. Data on breast cancer prevalence derived from 40 European countries are used to study the application of the nonparametric estimators to the estimation of cancer prevalence. Result derived from an empirical study show that nonparametric estimators have a good empirical performance in this study on cancer prevalence.