New Goodness-of-fit Diagnostics for Conditional Discrete Response Models
This paper proposes new speciﬁcation tests for conditional models with discrete responses. In particular, we can test the static and dynamic ordered choice model speciﬁcations, which is key to apply eﬀicient maximum likelihood methods, to obtain consistent estimates of partial eﬀects and to get appropriate predictions of the probability of future events. The traditional approach is based on probability integral transforms of a jittered discrete data which leads to continuous uniform iid series under the true conditional distribution. We investigate in this paper an alternative transformation based only on original discrete data. We show analytically and in simulations that our approach dominates the traditional approach in terms of power. We apply the new tests to models of the monetary policy conducted by the Federal Reserve.
Kheifets, Igor and Velasco, Carlos, "New Goodness-of-fit Diagnostics for Conditional Discrete Response Models" (2013). Cowles Foundation Discussion Papers. 2316.