Monetary Policy and Data Uncertainty: A Case Study of Distribution, Hotels and Catering Growth

This paper is a case study of the real world monetary policy data uncertainty problem. The initial and the latest release for growth rates of the distribution, hotels and catering sector are combined with official data on household income and two surveys in a state-space model. Though important to the UK economy, the distribution, hotels and catering sector is apparently difficult to measure. One finding is that the initial release data is not important in predicting the latest release. It could be that the statistical office develop the initial release as a building block towards the final release rather than an estimate of it. Indeed, there is multicollinearity between the initial release and the retail sales survey, which would then contain the same early available information. A second finding is that the estimate of the later release is sensitive to the estimate of the average historical growth rate. This means that establishing priors for this parameter and testing for shift structural breaks should be very important.Data Uncertainty; Distribution Sector; Kalman Filter; Monetary Policy

Multivariate control charts based on Bayesian state space models

This paper develops a new multivariate control charting method for vector autocorrelated and serially correlated processes. The main idea is to propose a Bayesian multivariate local level model, which is a generalization of the Shewhart-Deming model for autocorrelated processes, in order to provide the predictive error distribution of the process and then to apply a univariate modified EWMA control chart to the logarithm of the Bayes' factors of the predictive error density versus the target error density. The resulting chart is proposed as capable to deal with both the non-normality and the autocorrelation structure of the log Bayes' factors. The new control charting scheme is general in application and it has the advantage to control simultaneously not only the process mean vector and the dispersion covariance matrix, but also the entire target distribution of the process. Two examples of London metal exchange data and of production time series data illustrate the capabilities of the new control chart.Comment: 19 pages, 6 figure

Should we be surprised by the unreliability of real-time output gap estimates? Density estimates for the Euro area

Recent work has found that, without the benefit of hindsight, it can prove difficult for policy-makers to pin down accurately the current position of the output gap; real-time estimates are unreliable. However, attention primarily has focused on output gap point estimates alone. But point forecasts are better seen as the central points of ranges of uncertainty; therefore some revision to real-time estimates may not be surprising. To capture uncertainty fully density forecasts should be used. This paper introduces, motivates and discusses the idea of evaluating the quality of real-time density estimates of the output gap. It also introduces density forecast combination as a practical means to overcome problems associated with uncertainty over the appropriate output gap estimator. An application to the Euro area illustrates the use of the techniques. Simulated out-of-sample experiments reveal that not only can real-time point estimates of the Euro area output gap be unreliable, but so can measures of uncertainty associated with them. The implications for policy-makers use of Taylor-type rules are discussed and illustrated. We find that Taylor-rules that exploit real-time output gap density estimates can provide reliable forecasts of the ECB's monetary policy stance only when alternative density forecasts are combinedOutput gap; Real-Time; Density Forecasts; Density Forecast Combination; Taylor Rules

Statistical quality control applied to a telemetry system acceptance prodedure Technical report no. 13

Statistical quality control applied to telemetry system acceptance procedur

SPC Methods for Detecting Simple Sawing Defects Using Real-Time Laser Range Sensor Data

Effective statistical process control (SPC) procedures can greatly enhance product value and yield in the lumber industry, ensuring accuracy and minimum waste. To this end, many mills are implementing automated real-time SPC with non-contact laser range sensors (LRS). These systems have, thus far, had only limited success because of frequent false alarms and have led to tolerances being set excessively wide and real problems being missed. Current SPC algorithms are based on manual sampling methods and, consequently, are not appropriate for the volume of data generated by real-time systems. The objective of this research was to establish a system for real-time LRS size control data for automated lumber manufacturing. An SPC system was developed that incorporated multi-sensor data, and new SPC charts were developed that went beyond traditional size control methods, simultaneously monitoring multiple surfaces and specifically targeting common sawing defects. In this paper, eleven candidate control charts were evaluated. Traditional X-bar and range charts are suggested, which were explicitly developed to take into account the components of variance in the model. Applying these methods will lead to process improvements for sawmills using automated quality control systems, so that machines producing defective material can be identified and prompt repairs made

Extra-euro area manufacturing import prices and exchange rate pass-through

This paper uses a model of import prices whereby exporters to the euro area set export prices partly as a mark-up on their production costs (i.e., the degree of exchange rate pass-through) and partly in line with euro area producer prices (i.e., pricing-to-market). Using both time series and panel estimation techniques, the econometric results suggest that the pass through of changes in the effective exchange rate of the euro to the price of extra-euro area imports of manufactures is around 50% - 70%, while pricing-to-market has an estimated weight of between 50% - 30%. We also find some evidence of differences across import suppliers, with EU member states who are not part of the euro area assigning a relatively larger weight to pricing-to-market, while euro area imports from the United States seem to be characterised by a relatively higher degree of exchange rate pass-through. JEL Classification: D40, E30, F10, F31

On the Information Content of Oil Future Prices

This paper deals with the efficiency of the Brent Crude oil future contracts and tests whether futures can be used to predict realized oil spot prices. Evidence suggests that future prices up to three-months contracts on Brent Crude are unbiased predictors of future spot prices but the explanation power is not high (around 20%). Furthermore, using cointegration techniques the unbiasedness hypothesis for future prices as predictors of realized spot prices could not be rejected. When the sample is divided into sub-periods, the absence of bias in futures prices is rejected.

Modelo de apoio à decisão para a manutenção condicionada de equipamentos produtivos

Doctoral Thesis for PhD degree in Industrial and Systems EngineeringIntroduction: This thesis describes a methodology to combine Bayesian control chart and CBM (Condition-Based Maintenance) for developing a new integrated model. In maintenance management, it is a challenging task for decision-maker to conduct an appropriate and accurate decision. Proper and well-performed CBM models are beneficial for maintenance decision making. The integration of Bayesian control chart and CBM is considered as an intelligent model and a suitable strategy for forecasting items failures as well as allow providing an effectiveness maintenance cost. CBM models provides lower inventory costs for spare parts, reduces unplanned outage, and minimize the risk of catastrophic failure, avoiding high penalties associated with losses of production or delays, increasing availability. However, CBM models need new aspects and the integration of new type of information in maintenance modeling that can improve the results. Objective: The thesis aims to develop a new methodology based on Bayesian control chart for predicting failures of item incorporating simultaneously two types of data: key quality control measurement and equipment condition parameters. In other words, the project research questions are directed to give the lower maintenance costs for real process control. Method: The mathematical approach carried out in this study for developing an optimal Condition Based Maintenance policy included the Weibull analysis for verifying the Markov property, Delay time concept used for deterioration modeling and PSO and Monte Carlo simulation. These models are used for finding the upper control limit and the interval monitoring that minimizes the (maintenance) cost function. Result: The main contribution of this thesis is that the proposed model performs better than previous models in which the hypothesis of using simultaneously data about condition equipment parameters and quality control measurements improve the effectiveness of integrated model Bayesian control chart for Condition Based Maintenance.Introdução: Esta tese descreve uma metodologia para combinar Bayesian control chart e CBM (Condition- Based Maintenance) para desenvolver um novo modelo integrado. Na gestão da manutenção, é importante que o decisor possa tomar decisões apropriadas e corretas. Modelos CBM bem concebidos serão muito benéficos nas tomadas de decisão sobre manutenção. A integração dos gráficos de controlo Bayesian e CBM é considerada um modelo inteligente e uma estratégica adequada para prever as falhas de componentes bem como produzir um controlo de custos de manutenção. Os modelos CBM conseguem definir custos de inventário mais baixos para as partes de substituição, reduzem interrupções não planeadas e minimizam o risco de falhas catastróficas, evitando elevadas penalizações associadas a perdas de produção ou atrasos, aumentando a disponibilidade. Contudo, os modelos CBM precisam de alterações e a integração de novos tipos de informação na modelação de manutenção que permitam melhorar os resultados.Objetivos: Esta tese pretende desenvolver uma nova metodologia baseada Bayesian control chart para prever as falhas de partes, incorporando dois tipos de dados: medições-chave de controlo de qualidade e parâmetros de condição do equipamento. Por outras palavras, as questões de investigação são direcionadas para diminuir custos de manutenção no processo de controlo.Métodos: Os modelos matemáticos implementados neste estudo para desenvolver uma política ótima de CBM incluíram a análise de Weibull para verificação da propriedade de Markov, conceito de atraso de tempo para a modelação da deterioração, PSO e simulação de Monte Carlo. Estes modelos são usados para encontrar o limite superior de controlo e o intervalo de monotorização para minimizar a função de custos de manutenção.Resultados: A principal contribuição desta tese é que o modelo proposto melhora os resultados dos modelos anteriores, baseando-se na hipótese de que, usando simultaneamente dados dos parâmetros dos equipamentos e medições de controlo de qualidade. Assim obtém-se uma melhoria a eficácia do modelo integrado de Bayesian control chart para a manutenção condicionada

Development of univariate control charts for non-normal data

Thesis (Master)--Izmir Institute of Technology, Materials Science and Engineering, Izmir, 2006Includes bibliographical references (leaves: 50-51)Text in English; Abstract: Turkish and Englishxii, 75 leavesIn this study, a new control chart methodology was developed to address statistical process monitoring issue associated with non-normally distributed process variables. The new method (NM) was compared aginst the classical Shewhart control chart (OM) using synthetic datasets from normal and non-normal distributions as well as over an industrial example. The NM involved taking the difference between the specified probability density estimate and non-parametric density estimate of the variable of interest to calculate an error value. Both OM and NM were found to work well for normally distributed data when process is in-control and out-of control situation. Both methods could be returned back to normal operation upon feeding in control data. In case of non-normally distributed data, the OM failed significantly to detect small shifts in mean and standard deviation, however the NM maintained its performance to detect such changes. In the application to an industrial case (data were obtained from a local cement manufacturer about a 90 micrometer sieve fraction of the final milled cement product), the NM methodology outperformed the OM by recognizing the change in the mean and variance of the measured parameter. The data were tested for its distribution and were found to be non-normally distributed. Violations beyond the control limits in the new developed technique were easily observed. The NM was found to successfully operate without the necessity to apply run rules