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Semi‐Parametric Generalized Additive Vector Autoregressive Models of Spatial Basis Dynamics
Author(s) -
Guney Selin,
Goodwin Barry K.,
Riquelme Andrés
Publication year - 2019
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1093/ajae/aay033
Subject(s) - autoregressive model , econometrics , parametric statistics , impulse response , nonparametric statistics , semiparametric model , nonlinear system , basis (linear algebra) , parametric model , mathematics , additive model , vector autoregression , impulse (physics) , economics , statistics , mathematical analysis , physics , geometry , quantum mechanics
An 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 tests 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 asymmetric adjustment patterns. The results provide evidence in favor of efficiently linked markets.