An extensive empirical literature has addressed a wide array of issues pertaining to price linkages over space and across time. Empirical models of price linkages have been used to measure market power and to characterize the operation of markets that are separated by space, time, and product form. The long history of these empirical models extends from simple tests of price correlation, to conventional regression tests, to modern time series models that account for nonstationarity, nonlinearities, and threshold behavior in market linkages. This paper proposes an entirely different and potentially novel approach to analyzing these same types of time series data in a nonlinear fashion. Copula-based models that consider the joint distribution of prices separated by space are developed and applied to weekly prices for important lumber products at geographically distinct markets. In particular, we consider prices taken from weekly editions of the Random Lengths publication for homogeneous OSB products.


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