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Semi-parametric generalized additive vector autoregressive models of spatial basis dynamicsAuthor(s): Selin Guney; Barry Goodwin; Andrés Riquelme
Source: American Journal of Agricultural Economics
Publication Series: Scientific Journal (JRNL)
Station: Southern Research Station
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DescriptionAn extensive line of research has examined linkages among spatially-distinct markets. We apply semi-parametric, generalized additive vector autoregressive models to a consideration of basis linkages among North Carolina corn and soybean markets. An extensive suite of linearity test suggests that basis and price relationships are nonlinear. Marginal effects, transmission elasticities, and generalized impulse responses are utilized to describe patterns of adjustment among markets. The semi-parametric models are compared to standard threshold vector autoregressive models and are found to reveal more statistical significance and substantially more nonlinearity in basis adjustments. Marginal effects are nonlinear and impulse responses suggest greater adjustments to extreme shocks and asynmetric adjustment patterns. The results provide evidence in favor of efficiently linked markets.
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CitationGuney, Selin; Goodwin, Barry K; Riquelme, Andrés. 2019. Semi-parametric generalized additive vector autoregressive models of spatial basis dynamics. American Journal of Agricultural Economics. 101(2): 541-562. https://doi.org/10.1093/ajae/aay033.
KeywordsLaw of one price, generalized additive models, nonlinear time series models
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