The subtle danger of symmetry restrictions in time series regressions, with application to fertility models.

ABSTRACT

PIP It is widely known that linear restrictions involve bias. What is not known is that some linear restrictions are especially dangerous for hypothesis testing. For some, the expected value of the restricted coefficient does not lie between (among) the true unconstrained coefficients, which implies that the estimate is not a simple average of these coefficients. In this paper, the danger is examined regarding the additive linear restriction almost universally imposed in statistical research--the restriction of symmetry. Symmetry implies that the response of the dependent variable to a unit decrease in an expanatory variable is identical, but of opposite sign, to the response to a unit increase. The 1st section of the paper demonstrates theoretically that a coefficient restricted by symmetry (unlike coefficients embodying other additive restrictions) is not a simple average of the unconstrained coefficients because the relevant interacted variables are inversly correlated by definition. The next section shows that, under the restriction of symmetry, fertility in Finland from 1885-1925 appears to respond in a prolonged manner to infant mortality (significant and positive with a lag of 4-6 years), suggesting a response to expected deaths. However, unscontrained estimates indicate that this finding is spurious. When the restriction is relaxed, the dominant response is rapid (significant and positive with a lag of 1-2 years) and stronger for declines in mortality, supporting an aymmetric response to actual deaths. For 2 reasons, the danger of the symmetry restriction may be especially pervasive. 1st, unlike most other linear constraints, symmetry is passively imposed merely by ignoring the possibility of asymmetry. 2nd, modles in a wide range of fields--including macroeconomics (e.g., demand for money, consumption, and investment models, and the Phillips curve), international economics (e.g., intervention models of central banks), and labor economics (e.g., sticky wage models)--predict asymmetry. The conclusion of the study is that, to avoid spurious hypothesis testing, empirical research should systematically test for asymmetry, especially when predicted by theory.^ieng