MINIMUM DISTANCE ESTIMATION OF NONSTATIONARY TIME SERIES MODELS

Content Provider	Scilit
Author	Moon, Hyungsik Roger Schorfheide, Frank
Copyright Year	2002
Description	This paper analyzes the limit distribution of minimum distance (MD) estimators for nonstationary time series models that involve nonlinear parameter restrictions. A rotation for the restricted parameter space is constructed to separate the components of the MD estimator that converge at different rates. We derive regularity conditions for the restriction function that are easier to verify than the stochastic equicontinuity conditions that arise from direct estimation of the restricted parameters. The sequence of matrices that is used to weigh the discrepancy between the unrestricted estimates and the restriction function is allowed to have a stochastic limit. For MD estimators based on unrestricted estimators with a mixed normal asymptotic distribution the optimal weight matrix is derived and a goodness-of-fit test is proposed. Our estimation theory is illustrated in the context of a permanent-income model and a present-value model.
Related Links	https://www.cambridge.org/core/services/aop-cambridge-core/content/view/E0FBF0FDCBB5B58FA3410376AF9F9E8F/S0266466602186063a.pdf/div-class-title-minimum-distance-estimation-of-nonstationary-time-series-models-div.pdf
Ending Page	1407
Page Count	23
Starting Page	1385
ISSN	02664666
e-ISSN	14694360
DOI	10.1017/s0266466602186063
Journal	Econometric Theory
Issue Number	6
Volume Number	18
Language	English
Publisher	Cambridge University Press (CUP)
Publisher Date	2002-12-01
Access Restriction	Open
Subject Keyword	Econometric Theory Cybernetical Science
Content Type	Text
Resource Type	Article
Subject	Social Sciences Economics and Econometrics