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Canadian Hog Supply Respose: A Provincial Level Analysis

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  • Rude, James
  • Surry, Yves

Abstract

Canada’s hog sector has faced two decades of tumultuous growth, yet there are no recent estimates of supply response. A state-space model for hog supply response is developed that accounts for the time series properties of the data while accounting for a multiplicity of unspecified sources for structural change. A GARCH process is used to estimate expected price and price volatility to investigate the role of price risk. Surprisingly,the results indicate relatively inelastic own price elasticities that are consistent with prior studies conducted over more stable periods. This implies that price variations can have substantial effects on total profits and losses. The lack of sensitivity of quantities supplied can be explained in Quebec by the presence of the ASRA program. In other provinces, we conjecture that price expectations used by farmers are quite diffuse and that marginal changes in the mean (and variance) do not have much impact. The effects of risk for supply response appear quite muted and impacts of feed price risk are potentially bigger than hog price risk.

Suggested Citation

  • Rude, James & Surry, Yves, 2013. "Canadian Hog Supply Respose: A Provincial Level Analysis," Working Papers 148590, Structure and Performance of Agriculture and Agri-products Industry (SPAA).
  • Handle: RePEc:ags:spaawp:148590
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    Keywords

    supply response; Kalman filter; ARCH and GARCH models; Livestock Production/Industries; Production Economics; Risk and Uncertainty; Q11; Q13; and C32;

    JEL classification:

    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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