In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

From Marketing Mix to Relationship Marketing: Towards a Paradigm Shift in Marketing

Enclosed is a copy of the Board of Governors’ Semiannual Agenda of Regulations for the period August 1, 1980 through February 1, 1981. The Semiannual Agenda provides you with information on those regulatory matters that the Board now has under consideration or anticipates considering over the next six months, and is divided into three parts: (1) regulatory matters that the Board had considered during the previous six months on which final action has been taken; (2) regulatory matters that have been proposed for public comment and that require further Board consideration; and (3) regulatory matters that the Board may consider over the next six months.

Federal Reserve Bank of New Yorkの制定せる財務諸表様式について

A survey of data envelopment analysis in energy and environmental studies

The efficiency of Australian universities: a data envelopment analysis

Pitfalls and protocols in DEA

In this paper, we examine the comparative of higher education institutions in the UK. The governmental inititatives of the decade within this sector have given emphasis to issues of accountability, value for money and cost control. The reporting of various statistics regarding the universities' activities only fully achieves its potential value if it is used to define comprehensive concepts of performance and goal achievements informed by the institutions' missions. In that spirit, we propose concepts of cost and outcome efficiency in order to gain further insights into the universities' operations. Data envelopment analysis and its recent advances were used to asses the two types of efficiency. The application of the methodology to 45 universities in the UK revealed a subset of six institutions that showed satisfactory performance across alternative efficiency tests.

Assessing the Comparative Efficiency of Higher Education Institutions in the UK by the Means of Data Envelopment Analysis

This paper discusses a number of applications of data envelopment analysis and the nature of uncertainty in those applications. It then reviews the key approaches to handling uncertainty in data envelopment analysis (DEA) (imprecise DEA, bootstrapping, Monte Carlo simulation and chance constrained DEA) and considers their suitability for modelling the applications. The paper concludes with suggestions about the challenges facing an operational research analyst in applying DEA in real-world situations.

https://link.springer.com/content/pdf/10.1057%2Fjors.2009.145.pdf

Data envelopment analysis, operational research and uncertainty

Simple (equally weighted) moving averages are frequently used to estimate the current level of a time series, with this value being projected as a forecast for future observations. A key measure of the effectiveness of the method is the sampling error of the estimator, which this paper defines in terms of characteristics of the data. This enables the optimal length of the average for any steady state model to be established and the lead time forecast error derived. A comparison of the performance of a simple moving average (SMA) with an exponentially weighted moving average (EWMA) is made. It is shown that, for a steady state model, the variance of the forecast error is typically less than 3% higher than the appropriate EWMA. This relatively small difference may explain the inconclusive results from the empirical studies about the relative predictive performance of the two methods.

Some properties of a simple moving average when applied to forecasting a time series

This paper examines half a million observations of the size of orders from customers at an electrical wholesaler. It notes: the distribution of the size of customer orders for a single item (stock keeping unit or SKU) is very skewed and resembles a geometric distribution; while the average size of an order is different for different items, for one SKU the mean order size is effectively the same at different branches even when the branches have very different demand rates; across a range of SKUs there is a strong relationship linking the mean and the variance of order size. The general results above are shown to apply to even the slowest movers. This extension is important because for items with intermittent demand the size of customer orders is required to produce an unbiased estimate of demand. Also a knowledge of the distribution of demand is important for setting maximum and minimum stock levels and the scheme employed is described.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2601586.pdf

Estelle A. Shale

Papers

Pitfalls and protocols in DEA

Assessing the Comparative Efficiency of Higher Education Institutions in the UK by the Means of Data Envelopment Analysis

Data envelopment analysis, operational research and uncertainty

Some properties of a simple moving average when applied to forecasting a time series

An examination of the size of orders from customers, their characterisation and the implications for inventory control of slow moving items