Comparing non-parametric to parametric interaction terms in generalised additive models: production technology in the Canadian cable television industry

Morteza Haghiri, Stephen M. Law, James Nolan and Alireza Simchi

International Journal of Data Analysis Techniques and Strategies, 2013, vol. 5, issue 3, 229-251

Abstract: We use the theory of generalised additive models to develop a non-parametric cost function for the Canadian cable television industry through the 1990s. We offer that statistical testing for substitutability/complementarity of inputs is important for regulators and policymakers since some forms of industry regulation (e.g., rate of return regulation) have less distortionary effects on input choice if regulated returns are earned on an input that is complementary as opposed to a substitute for other inputs. Using detailed financial and operating data, cost function parameters for Canadian cable television are estimated using general spline smoothing techniques. We then test the degree of separability among the inputs using interaction terms defined for both non-parametric and parametric estimates. The results show that in this case, similar conclusions about input separability can be drawn using either parametric or non-parametric cost estimates.

Keywords: generalised additive models; GAMs; general spline smoothing techniques; input separability; data analysis; non-parametric cost function; production technology; Canada; cable television; cost estimation; cable TV. (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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