Robinson, Peter M. (1997) Inference-without-smoothing in the presence of nonparametric autocorrelation. Econometrics; EM/1997/338, EM/1997/338. Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science, London, UK.Full text not available from this repository.
In a number of econometric models, rules of large-sample inference require a consistent estimate of f(0), where f (?) is the spectral density matrix of yt = ut?xt, for covariance stationary vectors ut, xt. Typically yt is allowed to have nonparametric autocorrelation, and smoothing is used in the estimation of f(0). We give conditions under which f(0) can be consistently estimated 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. The estimate of f(0) dominates smoothed ones in the sense that it can have mean squared error of order n-1, where n is sample size. Under standard additional regularity conditions, we extend the estimate of f(0) to studentize asymptotically normal estimates of structural parameters in linear simultaneous equations systems. A small Monte Carlo study of finite sample behaviour is included.
|Item Type:||Monograph (Discussion Paper)|
|Additional Information:||© 1997 the author|
|Uncontrolled Keywords:||autocorrelation-consistent variance estimation; long-range dependence; simultaneous equations systems|
|Library of Congress subject classification:||H Social Sciences > HB Economic Theory|
|Sets:||Collections > Economists Online
Departments > Economics
Research centres and groups > Suntory and Toyota International Centres for Economics and Related Disciplines (STICERD)
|Date Deposited:||27 Apr 2007|
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