This book provides a framework for analyzing and forecasting a variety of mineral and energy markets and related industries. Such modeling activity has been at the forefront of the economic and engineering professions for some time, having received a major stimulus fC?llowing the first oil price shock in 1973. Since that time, other shocks have affected these markets and industries, causing disequilibrium economic adjustments which are difficult to analyze and to predict. Moreover, geopolitics remains an important factor which can destabilize crude oil markets and associated refining industries. Mineral and energy modeling, consequently, has become a major interest of energy-related corporations, mining and drilling companies, metal manufacturers, public utilities, investment banks, . national government agencies and international organizations. This book hopes to advance mineral and energy modeling as follows: (1) The modeling process is presented sequentially by leading the model builder from model specification, estimation, simulation, and validation to practical model applications, including explaining history, analyzing policy, and market and price forecasting; (2) New developments in modeling approaches are presented which encompass econometric market and industry models, spatial equilibrium and programming models, optimal resource depletion models, input-output models, economic sector models, and macro oriented energy interaction models (including computable general equilibrium); (3) The verification and application of the models is considered not only individually but also in relation to the performance of alternative modeling approaches; and (4) The modeling framework includes a perspective on new directions, so that the present model building advice will extend into the future."

Part of a series, this volume comprises a selection of methodology-oriented papers presented at the 25th International Conference of the Applied Econometrics Association on International Commodity Market Modelling which took place at the World Bank, Washington, 1988. Economic and statistical analyses are obviously of great importance in studying commodity markets. A deep knowledge of market-clearing processes, the institutional structures of the industries related to each commodity market whether on the supply or demand side and the statistical methods of data handling for inference purposes are all needed in order to make good sense of the wealth of information on commodity market data. In addition, a technological understanding of the economic processes underlying each market is necessary. The agronomy of crop production, the techniques of crop distribution from harvest to end-use, the contributions of meteorology, the engineering of metallurgy, the engineering of processing factories, the combating of oil spills, the control of pollution and many other technological aspects of the different markets are essential for a good understanding of the forces at work in each case. Also legal and political factors play roles in the markets and require some specialized knowledge of their effects. Almost every market is different and so a specialized technological background is required, but that adds much substance to the research. By fitting together appropriate cross-disciplinary bodies of information in commodity market studies, a high degree of interest and analytical challenge can be attained.

Recent economic growth in China and other Asian countries has led to increased commodity demand which has caused price rises and accompanying price fluctuations not only for crude oil but also for the many other raw materials. Such trends mean that world commodity markets are once again under intense scrutiny. This book provides new insights into the modeling and forecasting of primary commodity prices by featuring comprehensive applications of the most recent methods of statistical time series analysis. The latter utilize econometric methods concerned with structural breaks, unobserved components, chaotic discovery, long memory, heteroskedasticity, wavelet estimation and fractional integration. Relevant tests employed include neural networks, correlation dimensions, Lyapunov exponents, fractional integration and rescaled range. The price forecasting involves structural time series trend plus cycle and cyclical trend models. Practical applications focus on the price behaviour of more than twenty international commodity markets.

Recent economic growth in China and other Asian countries has led to increased commodity demand which has caused price rises and accompanying price fluctuations not only for crude oil but also for the many other raw materials. Such trends mean that world commodity markets are once again under intense scrutiny. This book provides new insights into the modeling and forecasting of primary commodity prices by featuring comprehensive applications of the most recent methods of statistical time series analysis. The latter utilize econometric methods concerned with structural breaks, unobserved components, chaotic discovery, long memory, heteroskedasticity, wavelet estimation and fractional integration. Relevant tests employed include neural networks, correlation dimensions, Lyapunov exponents, fractional integration and rescaled range. The price forecasting involves structural time series trend plus cycle and cyclical trend models. Practical applications focus on the price behaviour of more than twenty international commodity markets.

RONALD C. DUNCAN During the 1980s, substantial advances were made in the global modelling of commodity markets in several areas, advances which were reflected in many of the papers delivered to the Applied Econometrics Association meeting held at the World Bank in Washington, DC, in October 1988. The several areas where I see advances being made, some of which the International Commodity Markets Division of the World Bank has taken part in, are the following: (a) in the theoretical specification of commodity price behaviour; (b) in the increased emphasis on modelling imperfect markets; (c) in the incorporation of the interrelationships between macro economic and commodity market variables; (d) in the specification of supply response, particularly in respect of perennial crops; and (e) in the realization of complementarity between time series analysis and economet rically estimated structural models. Improvements in the specification of the commodity price formation process have probably been the most important of the above advances. Until the early 1980s, prices were modelled as a simple linear function of stocks. Gilbert has played an important role in introducing the rational expecta tions hypothesis into the specification of commodity prices. Recent work by Gilbert, Trivedi, and Deaton and Laroque offers the possibility of non-linear specification of the relationship between prices and stocks within an expectational framework and of thereby capturing the phenomenon of sharp run-ups in commodity prices. Gilbert has also played an important role in clarifying the interrelation ships between macroeconomic variables and primary commodity prices."

This book provides a framework for analyzing and forecasting a variety of mineral and energy markets and related industries. Such modeling activity has been at the forefront of the economic and engineering professions for some time, having received a major stimulus fC?llowing the first oil price shock in 1973. Since that time, other shocks have affected these markets and industries, causing disequilibrium economic adjustments which are difficult to analyze and to predict. Moreover, geopolitics remains an important factor which can destabilize crude oil markets and associated refining industries. Mineral and energy modeling, consequently, has become a major interest of energy-related corporations, mining and drilling companies, metal manufacturers, public utilities, investment banks,. national government agencies and international organizations. This book hopes to advance mineral and energy modeling as follows: (1) The modeling process is presented sequentially by leading the model builder from model specification, estimation, simulation, and validation to practical model applications, including explaining history, analyzing policy, and market and price forecasting; (2) New developments in modeling approaches are presented which encompass econometric market and industry models, spatial equilibrium and programming models, optimal resource depletion models, input-output models, economic sector models, and macro oriented energy interaction models (including computable general equilibrium); (3) The verification and application of the models is considered not only individually but also in relation to the performance of alternative modeling approaches; and (4) The modeling framework includes a perspective on new directions, so that the present model building advice will extend into the future.

Which time series test should a researcher chose to best describe the interactions among a set of time series variables? Aimed at providing social scientists with practical guidelines for identifying the appropriate multivariate time series model to use, this book explores the nature and application of these increasingly complex tests. Other topics it covers are joint stationarity, testing for cointegration, testing for Granger causality, and testing for model order, and forecast accuracy. Related models explained include transfer function, vector autoregression, error correction models, and others. Readers with a working knowledge of time series regression will find this helpful book accessible.

Taking a sequential approach to time-series model building, this book explores how to test for stationarity, normality, independence, linearity, model order, and properties of the residual process. The authors clearly define each testing procedure and offer examples to illustrate each concept. The authors also provide advice on how to perform the tests using different software packages. "This provides a nice roadmap for those doing time series analysis, and the authors should be applauded for this... Their approach is straightforward and logical and I believe will be useful many practicing statisticians." --Technometrics