Neglected heterogeneity in moment condition models

The central concern of this paper is parameter heterogeneity in models specified by a number of unconditional or conditional moment conditions and thereby the provision of a framework for the development of apposite optimal -tests against its potential presence. We initially consider the unconditional moment restrictions framework. Optimal -tests against moment condition parameter heterogeneity are derived with the relevant Jacobian matrix obtained in terms of the second order own derivatives of the moment indicator in a leading case. GMM and GEL tests of specification based on generalized information matrix equalities appropriate for moment-based models are described and their relation to optimal -tests against moment condition parameter heterogeneity examined. A fundamental and important difference is noted between GMM and GEL constructions. The paper is concluded by a generalization of these tests to the conditional moment context and the provision of a limited set of simulation experiments to illustrate the efficacy of the proposed tests

Choice-Based Demand Management and Vehicle Routing in E-Fulfillment

Attended home delivery services face the challenge of providing narrow delivery time slots to ensure customer satisfaction, while keeping the significant delivery costs under control. To that end, a firm can try to influence customers when they are booking their delivery time slot so as to steer them toward choosing slots that are expected to result in cost-effective schedules. We estimate a multinomial logit customer choice model from historic booking data and demonstrate that this can be calibrated well on a genuine e-grocer data set. We propose dynamic pricing policies based on this choice model to determine which and how much incentive (discount or charge) to offer for each time slot at the time a customer intends to make a booking. A crucial role in these dynamic pricing problems is played by the delivery cost, which is also estimated dynamically. We show in a simulation study based on real data that anticipating the likely future delivery cost of an additional order in a given location can lead to significantly increased profit as compared with current industry practice

Collateral Quality and Loan Default Risk: The Case of Vietnam

In the transition economy of Vietnam, financial market is dominated by banking sector but commercial banks heavily rely on collateral-based lending. While the relationship between collateral and implied credit risk is still in debate, this paper provides additional empirical evidence regarding the heterogeneous effects and transmission channels of collateral characteristics on loan delinquency. Applying instrumental variable probit analysis on a unique dataset of 2295 internal loan accounts in Vietnam, we find the significantly negative impact of collateral quality on the probability of default of consumer loans, supporting the dominance of borrower selection and risk-shifting over lender selection effects. The finding implies that high-quality collateral not only signals more credible borrower but also fosters good behavior in using loan, enabling bank to mitigate adverse selection and moral hazard problems

Empirical likelihood estimation of the spatial quantile regression

The spatial quantile regression model is a useful and flexible model for analysis of empirical problems with spatial dimension. This paper introduces an alternative estimator for this model. The properties of the proposed estimator are discussed in a comparative perspective with regard to the other available estimators. Simulation evidence on the small sample properties of the proposed estimator is provided. The proposed estimator is feasible and preferable when the model contains multiple spatial weighting matrices. Furthermore, a version of the proposed estimator based on the exponentially tilted empirical likelihood could be beneficial if model misspecification is suspect

Investor heterogeneity and the cross-section of U.K. investment trust performance

We use the upper and lower bounds derived by Ferson and Lin (2010) to examine the impact of investor heterogeneity on the performance of U.K. investment trusts relative to alternative linear factor models. We find using the upper bounds that investor heterogeneity has an important impact for nearly all investment trusts. The upper bounds are large in economic terms and significantly different from zero. We find no evidence of any trusts where all investors agree on the sign of performance beyond what we expect by chance. Using the lower bound, we find that trusts with a larger disagreement about trust performance have a weaker relation between the trust premium and past Net Asset Value (NAV) performance

Comparing composite likelihood methods based on pairs for spatial Gaussian random fields

In the last years there has been a growing interest in proposing methods for estimating covariance functions for geostatistical data. Among these, maximum likelihood estimators have nice features when we deal with a Gaussian model. However maximum likelihood becomes impractical when the number of observations is very large. In this work we review some solutions and we contrast them in terms of loss of statistical efficiency and computational burden. Specifically we focus on three types of weighted composite likelihood functions based on pairs and we compare them with the method of covariance tapering. Asymptotic properties of the three estimation methods are derived. We illustrate the effectiveness of the methods through theoretical examples, simulation experiments and by analyzing a data set on yearly total precipitation anomalies at weather stations in the United States.In the last years there has been a growing interest in proposing methods for estimating covariance functions for geostatistical data. Among these, maximum likelihood estimators have nice features when we deal with a Gaussian model. However maximum likelihood becomes impractical when the number of observations is very large. In this work we review some solutions and we contrast them in terms of loss of statistical efficiency and computational burden. Specifically we focus on three types of weighted composite likelihood functions based on pairs and we compare them with the method of covariance tapering. Asymptotic properties of the three estimation methods are derived. We illustrate the effectiveness of the methods through theoretical examples, simulation experiments and by analyzing a data set on yearly total precipitation anomalies at weather stations in the United States

ExploRing Persistence in Financial Time Series

Edited by W. Härdle, Z. Hlávka, and S. Klinke</p

Suicide Seasonality: Complex Demodulation as a Novel Approach in Epidemiologic Analysis

Seasonality of suicides is well-known and nearly ubiquitous, but recent evidence showed inconsistent patterns of decreasing or increasing seasonality in different countries. Furthermore, strength of seasonality was hypothesized to be associated with suicide prevalence. This study aimed at pointing out methodological difficulties in examining changes in suicide seasonality. METHODODOLOGY/PRINCIPAL FINDINGS: The present study examines the hypothesis of decreasing seasonality with a superior method that allows continuous modeling of seasonality. Suicides in Austria (1970-2008, N = 67,741) were analyzed with complex demodulation, a local (point-in-time specific) version of harmonic analysis. This avoids the need to arbitrarily split the time series, as is common practice in the field of suicide seasonality research, and facilitates incorporating the association with suicide prevalence. Regression models were used to assess time trends and association of amplitude and absolute suicide numbers. Results showed that strength of seasonality was associated with absolute suicide numbers, and that strength of seasonality was stable during the study period when this association was taken into account.Continuous modeling of suicide seasonality with complex demodulation avoids spurious findings that can result when time series are segmented and analyzed piecewise or when the association with suicide prevalence is disregarded