Statistical Properties of Model-Based Signal Extraction Diagnostic Tests
AbstractA model-based diagnostic test for signal extraction was first described in Maravall (2003), and this basic idea was modified and studied in Findley et al. (2004). This paper improves on the latter work in two ways: central limit theorems for the diagnostics are developed, and two hypothesis-testing paradigms for practical use are explicitly described. A further modified diagnostic provides an interpretation of one-sided rejection of the null hypothesis, yielding general notions of “over-modeling” and “under-modeling.” The new diagnostics are demonstrated on two U.S. Census Bureau time series exhibiting seasonality.