Applied Economics · 2007 · 15 citations · 17 references
Social SciencesBusiness EconomicsEconomicsEconometricsFood EconomicsTime Series AnalysisCommodity Price DynamicsFood InflationModel SpecificationMultivariate ForecastingTime Series EconometricsTime Series ForecastingEconometric Price ModelingEnergy Price Effects On Food PricesError Variance EstimationGeneralized Autoregressive Conditional Heteroskedasticity ModelsInput-output Price RelationshipsRetail Food Price AnalysisRetail Food PricesWholesale Prices
To date, an overwhelming majority of the literature has addressed mean relationships between producer and consumer price inflation. Granger et al . (1986 Granger, CW, Robins, RP and Engle, RF. 1986. “Wholesale and retail prices: bivariate time-series modelling with forecastable error variance”. In Modelling Reliability, Edited by: Belsley, DA and Edwin, K. 1–17. Cambridge, , Massachusetts: MIT Press. [Google Scholar]) represent the only attempt to investigate second moment relationships. We examine the consumer--producer price relationship employing a multivariate GARCH-M framework that allows simultaneous estimation of the bivariate system along with providing explicit times series estimates of the variances of consumer and producer price inflation. This research also breaks new ground in the use of core and over-all inflation variance measures as well as examining state dependent mean and variance relationships. We find that mean relationships are generally sensitive to the measure of inflation used. Food and energy prices play an important role in transmitting changes in aggregate input prices to aggregate output prices. When food and energy prices are eliminated from consumer and producer price inflation measures, mean relationships break down irrespective of whether the economy is experiencing a high or low inflation regime. Variance relationships appear to be more robust in general and input price relationships in particular appear to respond to inflation regime shifts.
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Generalized autoregressive conditional heteroskedasticity
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