On minimum distance estimators for unimodal densities

It is shown that minimum distance estimators for families of unimodal densities are always consistent; the rate of convergence is indicated. An algorithm is proposed for computing the minimum distance estimator for the family of all unimodal densities. References are given to the maximum likelihood method and the kernel method.     

A quasi-maximum likelihood method for estimating the parameters of multivariate diffusions

A quasi-maximum likelihood procedure for estimating the parameters of multi-dimensional diffusions is developed in which the transitional density is a multivariate Gaussian density with first and second moments approximating the true moments of the unknown density. For affine drift and diffusion functions, the moments are exactly those of the true transitional density and for nonlinear drift and diffusion functions the approximation is extremely good and is as effective as alternative methods based on likelihood approximations. The estimation procedure generalises to models with latent factors. A conditioning procedure is developed that allows parameter estimation in the absence of proxies.     

Smooth minimum distance estimation and testing with conditional estimating equations: Uniform in bandwidth theory

To study the influence of a bandwidth parameter in inference with conditional moments, we propose a new class of estimators and establish an asymptotic representation of our estimator as a process indexed by a bandwidth, which can vary within a wide range including bandwidths independent of the sample size. We study its behavior under misspecification. We also propose an efficient version of our estimator. We develop a procedure based on a distance metric statistic for testing restrictions on parameters as well as a bootstrap technique to account for the bandwidth’s influence. Our new methods are simple to implement, apply to non-smooth problems, and perform well in our simulations.     

The method of principal component analysis based on chi-square distance with regional application

This paper gives an outline of the framework of principal component analysis using chi-square distance and its application to a static and dynamical structure analysis of agricultural gross output in Western Switzerland.     

A simple and efficient method for estimating the magnitude and precision of welfare changes

In this article we propose a simple and efficient method of estimating welfare measures in an n-equation system. We also estimate the variance of these measures. The accuracy and convergence properties of our approach are studied: the algorithm converges quadratically. This is a distinct improvement over the Vartia algorithm which converges linearly. We demonstrate the method by evaluating the welfare consequences of price changes in the provision of health care. The GAUSS code, which is very short, is included.     

Approximations for densities of sufficient estimators

A simple method of obtaining asymptotic expansions for the densities of sufficient estimators is described. It is an extension of the one developed by O. Barndorff-Nielsen and D.R. Cox (1979) for exponential families. A series expansion in powers of n^?1 is derived of which the first term has an error of order n^?1 which can effectively be reduced to $\text{n}{}^{\mathrm{<mtext>-</mtext><mtext>?3</mtext><mtext>2</mtext>}}$ by renormalization. The results obtained are similar to those given by H.E. Daniels's (1954) saddlepoint method but the derivations are simpler. A brief treatment of approximations to conditional densities is given. Theorems are proved which extend the validity of the multivariate Edgeworth expansion to parametric families of densities of statistics which need not be standardized sums of independent and identically distributed vectors. These extensions permit the treatment of problems arising in time series analysis. The technique is used by J. Durbin (1980) to obtain approximations to the densities of partial serial correlation coefficients.     

Hermite series estimators for probability densities

Summary For Hermite series density estimators assertions about rates of convergence of MSE, MISE and about asymptotic normality are given. Moreover, we study the behaviour of these estimators if the density is not continuous. Hermite series estimators with random length are also considered. Convergence in probability and a.s. of these estimators is proved.     

Conceptual estimating tool for technology-driven projects: exploring parametric estimating technique

This paper examines a parametric estimating technique applied to technology-driven projects. Parametric cost estimating is a widely used approach for bidding on a contract, input into a cost benefit analysis, or as the pre-planning tool for project implementation. Extensive literature reviews suggest that effective parametric estimating methodology is becoming an essential tool for technology-driven organizations. The use of parametric estimating in budgeting, scheduling, and control of projects will enhance the ability of project management organizations to effectively and efficiently utilize valuable resources. The benefit of parametric estimating is its use as an estimating model for better determining potential resource requirements during the project pre-planning and conceptual phase.     

Calibration methods for estimating quantiles

We propose a calibrated estimator of the quantiles of sample survey data and discuss the asymptotic theory behind it. This estimator is defined for any sampling design and uses the information available on J auxiliary variables. A simulation study based on a real population is used to compare the estimator with various methods proposed previously.     

On estimating the reliability after sequentially estimating the mean: The exponential case

Sequential estimation problems for the mean parameter of an exponential distribution has received much attention over the years. Purely sequential and accelerated sequential estimators and their asymptotic second-order characteristics have been laid out in the existing literature, both for minimum risk point as well as bounded length confidence interval estimation of the mean parameter. Having obtained a data set from such sequentially designed experiments, the paper investigates estimation problems for the associatedreliability function. Second-order approximations are provided for the bias and mean squared error of the proposed estimator of the reliability function, first under a general setup. An ad hoc bias-corrected version is also introduced. Then, the proposed estimator is investigated further under some specific sequential sampling strategies, already available in the literature. In the end, simulation results are presented for comparing the proposed estimators of the reliability function for moderate sample sizes and various sequential sampling strategies.     

Moments expansion densities for quantifying financial risk

We propose a novel semi-nonparametric distribution that is feasibly parameterized to represent the non-Gaussianities of the asset return distributions. Our Moments Expansion (ME) density presents gains in simplicity attributable to its innovative polynomials, which are defined by the difference between the nth power of the random variable and the nth moment of the density used as the basis. We show that the Gram–Charlier distribution is a particular case of the ME-type of densities. The latter being more tractable and easier to implement when quadratic transformations are used to ensure positiveness. In an empirical application to asset returns, the ME model outperforms both standard and non-Gaussian GARCH models along several risk forecasting dimensions.     

A simple and efficient method for estimating the magnitude and precision of welfare changes: comment

A recent article (Breslaw and Smith, 1995 ) published in this journal provided an iterative numerical technique for computing the welfare effects of price changes. In this note we take issue with the application used to demonstrate this technique. In particular, we show that the demands used are inappropriate and that the authors incorrectly interpret the precision of the estimates of welfare change. Copyright © 2002 John Wiley & Sons, Ltd.     

Robust estimators for estimating discontinuous functions

We study the asymptotic behavior of a wide class of kernel estimators for estimating an unknown regression function. In particular we derive the asymptotic behavior at discontinuity points of the regression function. It turns out that some kernel estimators based on outlier robust estimators are consistent at jumps.     

The distance between rival nonstationary fractional processes

Asymptotic inference on nonstationary fractional time series models, including cointegrated ones, is proceeding along two routes, determined by alternative definitions of nonstationary processes. We derive bounds for the mean squared error of the difference between (possibly tapered) discrete Fourier transforms under the two regimes. We apply the results to deduce limit theory for estimates of memory parameters, including ones for cointegrating errors, with mention also of implications for estimates of cointegrating coefficients.     

The urban housing market: A new approach for estimating demand for housing by dwelling type

A new approach for estimating urban housing demand by dwelling type is suggested and tested empirically. The approach is based on the assumption that households choose their residential location and dwelling type in a way to maximize the communal utility. The model used is the Dynamic General Linear Expenditure System which allows determination of (1) urban housing demand by dwelling type, and (2) expenditure and own- and cross-price elasticities (compensated and uncompensated).     

Another approach for estimating RTS in dynamic DEA

Recently, in some papers in the field of operations research the performance analysis of decision making units, in the presence of time factor, has been dealt with by an analytical framework of DEA: dynamic DEA. This paper provides a new approach, utilizing the envelopment models, for estimating returns to scale in dynamic DEA, leading to an algorithm with computational advantages.     

Towards estimating the demand for money in China

The demand for money in China is estimated separately for the periods before and after the economic reform. Besides the traditional transactions demand variable, the expected rate of inflation (as a measure of the opportunity cost of holding money) and the monetization process are also incorporated into the demand function. The preliminary results show that the demand for money in China has changed in response to the institutional changes during the economic reform. Adding the monetization and inflation expectation variables into the money demand function has enhanced significantly its explanatory power.