Modeling and Forecasting Primary Commodity Prices
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.
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History of Commodity Price Analysis
Identifying Trends and Breaks
Convergence of Commodity Prices
Identifying Price Cycles
Business Cycle Impacts
Color of Commodity Prices
Wavelet Models Transforms of Commodity Prices
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Agricultural aluminum analyzing applied approach autoregressive break point business cycles chaotic chapter Cocoa coefficients Coffee cointegration commodity markets commodity price cycles commodity price series common factor confirmed convergence copper correlation correlation dimension cotton CPGM cycle plus trend cyclical trend model Daubechies wavelets demand dependence developed distribution dynamics econometric Economics employed endogenous equation estimated evaluated example exogenous fractal fractal dimension fractional integration frequency function futures prices Granger Impulse response Journal Kouassi Labys lags lead level of resolution linear London Metal Exchange Lyapunov exponents macroeconomic metal prices methods modeling and forecasting noise nonlinear null hypothesis parameters Perron price analysis price behavior price forecasting price movements random walk regression regressors Rubber shocks Soybeans spatial specification stationary stationary process statistical stochastic structural breaks structural time series studies subperiod suggest Table term transformation underlying unit root Variance Decomp vector wavelet wheat Wool WTAG Zinc