Library Header Image
LSE Research Online LSE Library Services

Inference-without-smoothing in the presence of nonparametric autocorrelation

Robinson, Peter (1998) Inference-without-smoothing in the presence of nonparametric autocorrelation. Econometrica, 66 (5). pp. 1163-1182. ISSN 0012-9682

Full text not available from this repository.


In a number of econometric models, rules of large-sample inference require a consistent estimate of f(O), where f(A) is the spectral density matrix of Y, = U 0 xt, for covariance stationary vectors it, xt. Typically y, is allowed to have nonparametric autocorrelation, and smoothing is used in the estimation of f(O). We give conditions under which f(O) 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(O) 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(O) to studentize asymptotically normal estimates of structural parameters in linear simultaneous equations systems. A small Monte Carlo study of finite sample behavior is included.

Item Type: Article
Official URL:
Additional Information: © 1998 The Econometric Society
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation
Date Deposited: 27 Apr 2007
Last Modified: 20 Sep 2021 03:34

Actions (login required)

View Item View Item