Inference-Without-Smoothing in the Presence of Nonparametric Autocorrelation
The author gives conditions under which nonparametric autocorrelation-consistent variance estimation is possible without smoothing. The conditions are relevant to inference on slope parameters in models with an intercept and strictly exogenous regressors, and allow regressors and disturbances to collectively have considerable stationary long memory and to satisfy only mild, in some cases minimal, moment conditions. His estimate dominates smoothed ones in the sense that it can have mean squared error proportional to the reciprocal of sample size. Under standard additional regularity conditions, the author shows that the estimate can validly studentize asymptotically normal estimates of structural parameters in linear simultaneous equations systems.
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Volume (Year): 66 (1998)
Issue (Month): 5 (September)
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