Investigating the distribution of the value of travel time savings

The distribution of the value of travel time savings (VTTS) is investigated employing various nonparametric techniques on a large, high quality data set. When background variables are not included in the model it is found that the right 13% tail of the distribution is not observed and hence the mean VTTS cannot be evaluated. This conclusion changes when background variables are introduced into a semiparametric model. A partially constrained Johnson SB distribution allowing evaluation of the mean VTTS is accepted against the nonparametric alternative and is preferred among 16 candidates for parametric VTTS distributions. The resulting mean VTTS is plausible but three times larger than the mean VTTS evaluated from a simple logit model and half as big as that arising from a model assuming a lognormal distribution for the VTTS. Such findings indicate the importance of properly accounting for the distribution when estimating the mean VTTS. The present findings may be used to guide the choice of mixing distribution in a mixed logit model.value of travel time savings, time, transport, VTTS, distribution, nonparametric, semiparametric, Klein-Spady, Zheng, Johnson SB, lognormal

On the Design of LQR Kernels for Efficient Controller Learning

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.Comment: 8 pages, 5 figures, to appear in 56th IEEE Conference on Decision and Control (CDC 2017

Exploring the Nexus between Banking Sector Reform and Performance: Evidence from Newly Acceded EU Countries

The aim of this study is to examine the relationship between banking sector reform and bank performance – measured in terms of efficiency, total factor productivity growth and net interest margin – accounting for the effects through competition and bank risk-taking. To this end, we develop an empirical model of bank performance and draw on recent econometric advances to consistently estimate it. The model is applied to bank panel data from ten newly acceded EU countries. The results indicate that both banking sector reform and competition exert a positive impact on bank efficiency, while the effect of reform on total factor productivity growth is significant only toward the end of the reform process. Finally, the effect of capital and credit risk on bank performance is in most cases negative, while it seems that higher liquid assets reduce the efficiency and productivity of banks.Bank performance; Banking sector reform; Competition; Risk-taking

SOME GUIDING PRINCIPLES FOR EMPIRICAL PRODUCTION RESEARCH IN AGRICULTURE

Constraints on production economic research are examined in three dimensions: problem focus, methodology, and data availability. Data availability has played a large role in the choice of problem focus and explains some misdirected focus. A proposal is made to address the data availability constraint. The greatest self-imposed constraints are methodological. Production economics has focused on flexible representations of technology at the expense of specificity in preferences. Yet some of the major problems faced by decision makers relate to long-term problems, e.g., the commodity boom and ensuring debt crisis of the 1970s and 1980s where standard short-term profit maximization models are unlikely to capture the essence of decision maker concerns.Production Economics,

COOPER-framework: A Unified Standard Process for Non-parametric Projects

Practitioners assess performance of entities in increasingly large and complicated datasets. If non-parametric models, such as Data Envelopment Analysis, were ever considered as simple push-button technologies, this is impossible when many variables are available or when data have to be compiled from several sources. This paper introduces by the ‘COOPER-framework’ a comprehensive model for carrying out non-parametric projects. The framework consists of six interrelated phases: Concepts and objectives, On structuring data, Operational models, Performance comparison model, Evaluation, and Result and deployment. Each of the phases describes some necessary steps a researcher should examine for a well defined and repeatable analysis. The COOPER-framework provides for the novice analyst guidance, structure and advice for a sound non-parametric analysis. The more experienced analyst benefits from a check list such that important issues are not forgotten. In addition, by the use of a standardized framework non-parametric assessments will be more reliable, more repeatable, more manageable, faster and less costly.DEA, non-parametric efficiency, unified standard process, COOPER-framework.

Managing uncertainty:financial, actuarial and statistical modelling.

present value; Value; Actuarial;

A Local Examination for Persistence in Exclusions-from-Core Measures of Inflation Using Real-Time Data

Using parametric and nonparametric methods, inflation persistence is examined through the relationship between exclusions-from-core inflation and total inflation for two sample periods and in five in-sample forecast horizons ranging from one quarter to three years over fifty vintages of real-time data in two measures of inflation: personal consumption expenditure and the consumer price index. Unbiasedness is examined at the aggregate and local levels. A local nonparametric hypothesis test for unbiasedness is developed and proposed for testing the local conditional nonparametric regression estimates, which can be vastly different from the aggregated nonparametric model. This paper finds that the nonparametric model outperforms the parametric model for both data samples and for all five in-sample forecast horizons.Real-Time Data, Local Estimation, Nonparametrics, Inflation Persistence, Monetary Policy