Semiparametric estimation of the fractional differencing parameter in the UK industrial production index

The order of integration of the industrial production index in the UK is investigated by means of semiparametric techniques in the time and in the frequency domain. Several methods like the RS statistic, along with others proposed by Robinson in a number of articles are applied to various differenced transformations of the log of the series. These methods perform poorly when using the time domain approaches, however, when using the frequency domain, the results are fairly conclusive. Evidence is found of a unit root at the zero frequency in the logged series whether or not the series is monthly seasonally differenced first.     

Semiparametric Bayesian estimation of mixed count regression models

This paper develops semiparametric Bayesian estimation approach for Poisson regression models with unobserved heterogeneity of unknown density. This approach is computationally efficient and allows automatic adaptation of the approximating density to data during estimation. Simulations show the estimator performs well.     

Semiparametric estimation of conditional mean functions with missing data

A new semiparametric estimator for estimating conditional expectation functions from incomplete data is proposed, which integrates parametric regression with nonparametric matching estimators. Besides its applicability to missing data situations due to non-response or attrition, the estimator can also be used for analyzing treatment effect heterogeneity and statistical treatment rules, where data on potential outcomes is missing by definition. By combining moments from a parametric specification with nonparametric estimates of mean outcomes in the non-responding population within a GMM framework, the estimator seeks to balance a good fit in the responding population with low bias in the non-responding population. The estimator is applied to analyzing treatment effect heterogeneity among Swedish rehabilitation programmes.

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A smooth coefficient model of carbon emissions

I use a semiparametric smooth coefficient model to estimate a generalization of the emissions convergence models derived from the green Solow model proposed by Brock and Taylor (J Econ Growth 15:127–153, 2010). Parametric estimates of simple homogeneous coefficient convergence models suggest that there may be heterogeneity in emissions convergence across different subsamples of observations. The semiparametric models confirm that there is heterogeneity across countries in coefficient estimates; however, such heterogeneity does not appear to be substantial enough to qualitatively influence the estimates derived from the parametric models. Hence, I find that (i) the green Solow model is a robust framework for analyzing carbon emissions convergence and (ii) carbon emissions are converging across a large sample of countries. My results suggest that international agreements that assign pollution rights based on population levels may be agreeable to many nations.     

Nonlinearity in dynamic adjustment: Semiparametric estimation of panel labor supply

We estimate a semiparametric dynamic panel data model by the local linear kernel method and we interpret the slope of the nonparametric component function as a varying slope coefficient. Thus, the slope coefficient is a smooth, but otherwise unknown, function of some of the regressors. A Monte Carlo experiment is reported to examine the finite sample performance of the local linear estimator. We apply the estimation method to a labor supply equation for men from the triannual Survey of Income and Program Participation (SIPP). Specification tests based on the estimated labor supply elasticities, partial adjustment coefficients, and residuals demonstrate the improvements from a semiparametric partially linear model. Our empirical results point to a need by economists to revisit the issue of the speed of labor market adjustment to policy induced shifts in labor demand and to take more formal econometric account of heterogeneity in wage effects when studying the distributional consequences of tax reforms for labor supply earnings. First version received: July 2000/Final version received: January 2001     

Efficient estimation of partially linear varying coefficient models

In this paper, we consider the problem of estimating a semiparametric partially linear varying coefficient model. We derive the semiparametric efficiency bound for the asymptotic variance of the finite-dimensional parameter estimator. We also propose an efficient estimator for estimating the finite-dimensional parameter of the model. Simulation results show substantial efficiency gain of our proposed estimator over a conventional estimator as considered in Ahmad et al. (2005).     

Semiparametric estimation and testing in a model of environmental regulation with adverse selection

We consider a principal–agent model of environmental regulation with adverse selection, where firms are regulated through contracts. We show how the model allows to recover information on structural cost parameters. We use a semiparametric method to estimate consistently such parameters without specifying the distribution of the agents private information. We also show how to check for the specification of the econometric model, as well as auxiliary parametric assumptions, by means of specification tests based on nonparametric estimation. Results are used to discuss a selection of economic issues related to environmental regulation.First version received: August 2002/Final version received: November 2003Previous versions of this work were presented at Seminaire INRA-IDEI, Toulouse, the conference on Applications of semiparametric methods for micro-data, Tilburg, and seminars at UC Berkeley, University of Wisconsin–Madison, CREST, and INRA-LEA. We are indebted to participants, and especially Michael Visser, for their comments. We thank two referees for helpful comments. Financial support from the Conseil Régional Midi-Pyrénées and from INRA is gratefully acknowledged.     

Estimation of TFP growth: a semiparametric smooth coefficient approach

This article uses a semiparametric smooth coefficient model (SPSCM) to estimate TFP growth and its components (scale and technical change). The SPSCM is derived from a nonparametric specification of the production technology represented by an input distance function (IDF), using a growth formulation. The functional coefficients of the SPSCM come naturally from the model and are fully flexible in the sense that no functional form of the underlying production technology is used to derive them. Another advantage of the SPSCM is that it can estimate bias (input and scale) in technical change in a fully flexible manner. We also used a translog IDF framework to estimate TFP growth components. A panel of U.S. electricity generating plants for the period 1986?C1998 is used for this purpose. Comparing estimated TFP growth results from both parametric and semiparametric models against the Divisia TFP growth, we conclude that the SPSCM performs the best in tracking the temporal behavior of TFP growth.     

Quantile estimation of frontier production function

The purpose of the paper is to provide new information on the performance of frontier estimation methods, using data from Italian hotel industry. Quantile regression is also suggested as solution to frontier production function estimation. It is shown that, while the choice of estimation methods among conventional techniques significantly affects the economic analysis, quantile regression provides valuable new information by estimating the whole spectrum of production functions corresponding to different efficiency levels. In addition, the method makes available a coherent framework to analyze the performance of the conventional techiniques. Jel classification: C14, C16, D24We would like to thank the Co-Editor, the Associate Editor and an anonymous referee for comments and suggestions. The research was supported by the University Research Council and the National Research Council. The usual disclaimer applies.The estimates were computed using the Roger Koenker and StatLibS-Plus routine of quantile regression and the Tim Coelli and CEPA Web site FRONTIER 4.1 Program. The data set is provided by the Ho.Re.Ca. survey conducted by ISTAT in 1992.First version received: June 2001/Final version received: December 2002     

On estimation of the CES production function—Revisited

Estimation of the non-linear Constant Elasticity of Scale (CES) function is generally considered problematic due to convergence problems and unstable and/or meaningless results. These problems often arise from a non-smooth objective function with large flat areas, the discontinuity of the CES function where the elasticity of substitution is one, and possibly significant rounding errors where the elasticity of substitution is close to one. We suggest three (combinable) solutions that alleviate these problems and improve the reliability and stability of the results.     

Semiparametric Bayesian inference of the Kuznets hypothesis

In contrast to the parametric (quadratic) specification used for examining the Kuznets hypothesis, this study relies on the semiparametric Bayesian inference of the partially linear regression to re-assess the validity of an inverted-U shape of the Kuznets curve. The simple framework permits us to perform estimation and model comparison in a unified way. Empirical results using cross-sectional data on 75 countries indicate that there exists an (approximately) inverted-√, hence, asymmetric relation between inequality and per capita GDP. Moreover, judged by the Bayes factor, overwhelming evidence is found in favor of our semiparametric specification. Finally, robustness check using alternative measure of inequality and pool data confirms our findings.     

A robust algorithm for parameter estimation in smooth transition autoregressive models

This paper considers whether earned wealth affects bidding behavior in an induced-value second-price auction. We find people bid more sincerely in the auction with earned wealth given monetary incentives; earned wealth did not induce sincere bidding in hypothetical auctions.     

Modelling the heterogeneous effects of stocking rate on dairy production: an application of unconditional quantile regression with fixed effects

This article analyses the effects of stocking rate on dairy production, using New Zealand dairy farm business data for the period 2005–2014. Unlike previous studies that assume a homogenous relationship between stocking rate and dairy production, we contribute to the literature by investigating the heterogeneous effects of stocking rate on milksolids production and applying an unconditional quantile regression model with fixed effects to control for unobserved farm-specific traits that are time invariant. The empirical results show that stocking rate exerts significant heterogeneous effects on milksolids production at different quantiles. In particular, we find that an additional increase in stocking rate (i.e. one cow/ha increase) increases milksolids production per hectare by between 17% and 25% but decreases milksolids production per cow by between 5% and 12%. In addition, we find that milking interval, dairy breed, farm labour, access to irrigation and farm location are important factors that increase milksolids production.     

Semiparametric Estimation of Rational Expectation Models

In this paper we consider the problem of estimating a semiparametric rational expectation model. We derive the root-n asymptotic normality result of the finite dimensional parameters of the model.

     

A Semiparametric Analysis of the Life Cycle-Permanent Income Hypothesis

This paper considers a semiparametric regression model to test the various implications of the Life Cycle-Permanent Income (LCP) hypothesis proposed by Hall (1978). The semiparametric regression model does not require any parametric assumption on the functional form of the unknown utility function in our analysis. In contrast, the linear regression models frequently used in the literature are justified under specific parametric forms of the utility function and may lead to a misleading conclusion on the LCP hypothesis if the parameterization is incorrect. Using both linear and semiparametric regression models, tests of the martingale property of consumption along with several specification tests are performed on the U. S quarterly data from 1947 to 1990. The results from the semiparametric model do not differ significantly from those from the linear model and suggest some evidences against the implications of the LCP hypothesis. [C14]     

Semiparametric Estimation of the Intercept of a Sample Selection Model

This paper provides a consistent and asymptotically normal estimator for the intercept of a semiparametrically estimated sample selection model. The estimator uses a decreasingly small fraction of all observations as the sample size goes to infinity, as in Heckman (1990). In the semiparametrics literature, estimation of the intercept has typically been subsumed in the nonparametric sample selection bias correction term. The estimation of the intercept, however, is important from an economic perspective. For instance, it permits one to determine the "wage gap" between unionized and nonunionized workers, decompose the wage differential between different socio-economic groups (e.g. male–female and black–white), and evaluate the net benefits of a social programme.     

Specification and estimation of stochastic multiple-output production and technical inefficiency

This paper presents a primal-based approach for specification and estimation of multiple-output production frontiers that allows simultaneous identification and estimation of determinants of technical inefficiency. The proposed model extends the technical efficiency effects model by Battese and Coelli (1995) to general multiple-output technologies. An empirical application using Swedish health care panel data from the years 1989–1994 is included. The key issue is to test for the effects on technical efficiency of an organizational reform implemented with the primary purpose to increase efficiency. The results reveal no significant effect on technical efficiency of the reform, but evidence of overall decreased technical efficiency and technical progress in the provision of health care over the studied time period are found.     

Asymmetric quantile analysis of the Swedish mortgage price discovery process

Based on Swedish banking data we discover robust and significantly positive Asymmetric Price Transmission (APT) effects over all analysed regression quantiles of our mortgage interest rates, with even larger positive APT for the higher percentiles. The analysis was enabled through unique access to a Swedish bank's (SEB) own records of their true borrowing costs. Our central contribution is that there is a higher propensity for the bank to rapidly increase its mortgage interest rates for customers following an increase in its borrowing costs, compared with the propensity for the bank to decrease its customers’ mortgage rates subsequent to a corresponding borrowing cost decrease.     

Semiparametric EGARCH model with the case study of China stock market

In this paper, we propose a new semiparametric method for GARCH model by combining the EGARCH (1,1) model and local polynomial regression. Based on the idea of two-stage estimate, a link function is estimated by the local polynomial and then the parameters are obtained via the weighted least square method. Finally we apply this method to the Shanghai Composite Index in the China stock market and compared the results with these of EGARCH.