Generalized Method of Moments Estimator Based On Semiparametric Quantile Regression Imputation

In this article, we consider an imputation method to handle missing response values based on semiparametric quantile regression estimation. In the proposed method, the missing response values are generated using the estimated conditional quantile regression function at given values of covariates. We adopt the generalized method of moments for estimation of parameters defined through a general estimation equation. We demonstrate that the proposed estimator, which combines both semiparametric quantile regression imputation and generalized method of moments, has competitive edge against some of the most widely used parametric and non-parametric imputation estimators. The consistency and the asymptotic normality of our estimator are established and variance estimation is provided. Results from a limited simulation study and an empirical study are presented to show the adequacy of the proposed method

Parameter estimation and model testing for Markov processes via conditional characteristic functions

Markov processes are used in a wide range of disciplines, including finance. The transition densities of these processes are often unknown. However, the conditional characteristic functions are more likely to be available, especially for L\'{e}vy-driven processes. We propose an empirical likelihood approach, for both parameter estimation and model specification testing, based on the conditional characteristic function for processes with either continuous or discontinuous sample paths. Theoretical properties of the empirical likelihood estimator for parameters and a smoothed empirical likelihood ratio test for a parametric specification of the process are provided. Simulations and empirical case studies are carried out to confirm the effectiveness of the proposed estimator and test.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ400 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Are Crop Yield Distributions Negatively Skewed? A Bayesian Examination

Crop Production/Industries,

Speculation and Volatility Spillover in the Crude Oil and Agricultural Commodity Markets: A Bayesian Analysis

This paper assesses the roles of various factors influencing the volatility of crude oil prices and the possible linkage between this volatility and agricultural commodity markets. Stochastic volatility models are applied to weekly crude oil, corn and wheat futures prices from November 1998 to January 2009. Model parameters are estimated using Bayesian Markov chain Monte Carlo methods. The main results are as follows. Speculation, scalping, and petroleum inventories are found to be important in explaining oil price variation. Several properties of crude oil price dynamics are established including mean-reversion, a negative correlation between price and volatility, volatility clustering, and infrequent compound Poisson jumps. We find evidence of volatility spillover among crude oil, corn and wheat markets after the fall of 2006. This could be largely explained by tightened interdependence between these markets induced by ethanol production.Gibbs sampling, Merton jump, leverage effect, stochastic volatility, Demand and Price Analysis, Financial Economics, Resource /Energy Economics and Policy, G13, Q4,

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

A calibration experiment in a longitudinal survey with errors-in-variables

The National Resources Inventory (NRI) is a large-scale longitudinal survey conducted to assess trends and conditions of nonfederal land. A key NRI estimate is year-to-year change in acres of developed land, where developed land includes roads and urban areas. In 2003, a digital data collection procedure was implemented replacing a map overlay. Data from an NRI calibration experiment are used to estimate the relationship between data collected under the old and new protocols. A measurement error model is postulated for the relationship, where duplicate measurements are used to estimate one of the error variances. If any significant discrepancy is detected between new and old measures, some parameters that govern the algorithm under new protocol can be changed to alter the relationship. Parameters were calibrated so overall averages nearly match for the new and old protocols. Analyses on the data after initial parameter calibration suggest that the relationship is a line with an intercept of zero and a slope of one, therefore the parameters currently used are acceptable. The paper also provides models of the measurement error variances as functions of the proportion of developed land, which is essential for estimating the effect of measurement error for the whole NRI data

Scoring recalls for L2 readers of English in China: Pausal or idea units

Written recall may be a powerful tool used to address reading deficiencies in China. With 180 students enrolled in a third-year English class at a large university in northeastern China, the present investigation studies the relationship between pausal and idea units used to codify written recalls, and it investigates whether the strength of the relationship between pausal and idea units depends on other variables, such as length of time spent studying English, the amount of leisure reading done in English, or the version of passage. Findings indicate a strong correlation between idea units and pausal units for written recalls. This correlation underscores prior findings by Bernhardt (1991), and it reveals that the strength of the relationship between pausal and idea units does not depend on the moderating variables examined. Results are discussed in light of prior research and a detailed discussion of future directions for experiments of this type is offered