Forecasting using Bayesian and Information Theoretic Model Averaging: An Application to UK Inflation

In recent years there has been increasing interest in forecasting methods that utilise large datasets, driven partly by the recognition that policymaking institutions need to process large quantities of information. Factor analysis is one popular way of doing this. Forecast combination is another, and it is on this that we concentrate. Bayesian model averaging methods have been widely advocated in this area, but a neglected frequentist approach is to use information theoretic based weights. We consider the use of model averaging in forecasting UK inflation with a large dataset from this perspective. We find that an information theoretic model averaging scheme can be a powerful alternative both to the more widely used Bayesian model averaging scheme and to factor models.Forecasting, Inflation, Bayesian model averaging, Akaike criteria, Forecast combining

Economic Forecasting with an Agent-Based Model

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national and sector accounts, input-output tables, government statistics, census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts as heterogeneous agents. The detailed structure of the ABM allows for a breakdown into sector level forecasts. Potential applications of the model include stress-testing and predicting the effects of changes in monetary, fiscal, or other macroeconomic policies

Planning the petrochemical industry in Kuwait using economic and safety objectives

Kuwait, one of the major oil producing countries in the Middle East, is in the process of globalizing its operation in petroleum and petrochemical production. Kuwaiti officials have expressedin terest in acceleratingd evelopmento f the country's relatively small petrochemical industry. The development is to produce new valuable chemicals from the available basic feedstock chemicals. Two of the important planning objectives for a petrochemical industry are the economic gain and the industrial safety involved in the development. For the economic evaluation of the industry, and for the proposed final product chemicals in the development, a long-range plan is needed to identify trends in chemical prices. The chemical prices are related to the oil price, which is considered an important motivator for the whole petrochemical industry. Price trend modelling is performed to be able to forecast these prices for the planning horizon. Safety, as the second objective, is considered in this study as the risk of chemical plant accidents. Risk, when used as an objective fimction, has to have a simple quantitative form to be easily evaluated for a large number of possible plants in the petrochemical network. The simple quantitative form adopted is a risk index that enables the number of people affected by accidents resulting in chemical releases to be estimated. The two objectives, when combined with constraints describing the desired or the possible structure of the industry, will form an optimization model. For this study, the petrochemical planning model consists of a Mixed Integer Linear Programming (MILP) model to select the best routes from the basic feedstocks available in Kuwait to the desired final products with multiple objective functions. The economic and risk objectives usually have conflicting needs. The presence of several conflicting objectives is typical when planning. In many cases, where optimization techniques are utilized, the multiple objectives are simply aggregated into one single objective function. Optimization is then conducted to get one optimal result. However, many results are obtained for different aggregations of the objectives and eventually a set of solutions is obtained. Other tools, such as strategic tools, are used to select the best solution from the set. This study, which is concerned with economic and risk objectives, leads to the identification of important factors that affect the petrochemical industry. Moreover, the procedure, of modelling and model solution, can be used to simplify the decisionmaking for complex or large systems such as the petrochemical industry. It presents the use of simple multiple objective optimization tools within a petrochemical planning tool formulated as a mixed integer linear programming model. Such a tool is particularly useful when the decision-making task must be discussed and approved by officials who often have little experience with optimization theories

A Predictive Modeling Approach to Counter Failures in Heat Seal Process Verification Methods

Most products sold today are packaged in a protective shell that involves the design of a box or wrapper. A subset of such products also adds a second layer of protection via sterilization. For both sterilized and non-sterilized products, a procedure referred to as the heat seal process creates the protective barrier from outside influence. For sterilized products, the American Society for Testing and Materials provides standards to test or verify seal strength, and this verification is normally accomplished by using a process called a Design of Experiments (DOE). The DOE method makes systematic use of powerful data collection and analysis tools, however, it also takes considerable time, capital, and resources to implement and verify. Moreover, when changes to the system or materials are necessary, the needed re-verification of the process compounds the effort needed to complete a subsequent DOE analysis. The objective of this thesis is to demonstrate the use of control-theoretic modeling and prediction algorithms to reduce the burden of DOE methods for heat-seal processes. Specifically, assuming the DOE analysis can collect data with sufficient instrumentation, we illustrate a two-pronged approach that employs (i) model identification from data to discern between success/failure of a heat-seal process and (ii) model-based feedback control to determine process reconfigurations towards failure recovery. Simulation experiments are presented that mimic the advent of heat-seal failures due to a new foil material and employ our approach to recover successful seals through minimal adjustments to the heater’s temperature profile. The extent to which the approach can apply to other process failure scenarios, different configurable inputs (e.g., seal pressure) or under non-ideal instrumentation assumptions is cited as future work

Ocean forecasting for wave energy production

There are a variety of requirements for future forecasts in relation to optimizing the production of wave energy. Daily forecasts are required to plan maintenance activities and allow power producers to accurately bid on wholesale energy markets, hourly forecasts are needed to warn of impending inclement conditions, possibly placing devices in survival mode, while wave-by-wave forecasts are required to optimize the real-time loading of the device so that maximum power is extracted from the waves over all sea conditions. In addition, related hindcasts over a long time scale may be performed to assess the power production capability of a specific wave site. This paper addresses the full spectrum of the aforementioned wave modeling activities, covering the variety of time scales and detailing modeling methods appropriate to the various time scales, and the causal inputs, where appropriate, which drive these models. Some models are based on a physical description of the system, including bathymetry, for example (e.g., in assessing power production capability), while others simply use measured data to form time series models (e.g., in wave-to-wave forecasting). The paper describes each of the wave forecasting problem domains, details appropriate model structures and how those models are parameterized, and also offers a number of case studies to illustrate each modeling methodology

Forecasting and Risk Management Techniques for Electricity Markets

This book focuses on the recent development of forecasting and risk management techniques for electricity markets. In addition, we discuss research on new trading platforms and environments using blockchain-based peer-to-peer (P2P) markets and computer agents. The book consists of two parts. The first part is entitled “Forecasting and Risk Management Techniques” and contains five chapters related to weather and electricity derivatives, and load and price forecasting for supporting electricity trading. The second part is entitled “Peer-to-Peer (P2P) Electricity Trading System and Strategy” and contains the following five chapters related to the feasibility and enhancement of P2P energy trading from various aspects

Economic Forecasting with an Agent-Based Model

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national and sector accounts, input-output tables, government statistics, census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts as heterogeneous agents. The detailed structure of the ABM allows for a breakdown into sector level forecasts. Potential applications of the model include stress-testing and predicting the effects of changes in monetary, fiscal, or other macroeconomic policies

NASA Tech Briefs, July 1992

Topics include: New Product Ideas; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences