“…Implicit in the use of cross-sectional regression analysis is the assumption that the observations do in fact represent points of equilibrium. This, of course, is seldom if ever strictly true and, especially where an adjustment period of some length is likely, it is possible that the results may be distorted to some extent (Kuh 1959;Grunfeld 1961). It could be, for example, that the negative association we have observed between property taxes and home values is primarily a short-run phenomenon, which would disappear over a longer period of time.…”
Section: Taxes and The Tiebout Hypothesismentioning
“…Implicit in the use of cross-sectional regression analysis is the assumption that the observations do in fact represent points of equilibrium. This, of course, is seldom if ever strictly true and, especially where an adjustment period of some length is likely, it is possible that the results may be distorted to some extent (Kuh 1959;Grunfeld 1961). It could be, for example, that the negative association we have observed between property taxes and home values is primarily a short-run phenomenon, which would disappear over a longer period of time.…”
Section: Taxes and The Tiebout Hypothesismentioning
“…This suggests that time-series data illustrate adjustments along short-run equilibria, while cross-section data depict adjustments along long-run equilibria, a conjecture that is well established in the literature (Kuh 1959;Grunfeld 1961;Baltagi and Griffin 1984). Thus, models estimated with cross-section data should be interpreted appropriately.…”
Section: Caution In Using Farm-level Datamentioning
“…Y it and X 2it k are independent. This is in fact just the conditions derived in Grunfeld (1961) for a cross-section regression of the type described above to give an unbiased estimator of long-run parameters within the framework of a partial adjustment model with stationary variables. Repeating the arguments in the proof of Proposition 1 in Appendix A.1 the estimator obtained by regressing Y it on X 2it k is also consistent (as N !…”
This paper speci…es a regression model describing cointegrating relations between variables at the individual level. The models considered allow for homogeneous cointegration and heterogeneous cointegration.In both cases correlation between the regressors and the regression error can occur through aggregate shocks that are common to all cross-section units so the condition about the regressors being independent of the regression error is not imposed. It is shown that the estimator obtained by a cross-section regression performed at any point in time is a consistent estimator of the cointegrating parameters in the homogeneous case and of the cointegrating parameter means in the heterogeneous case. In both cases the limiting distribution of the cross-section estimator is normal.
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