EconPapers    
Economics at your fingertips  
 

Estimation of longrun variance of continuous time stochastic process using discrete sample

Ye Lu and Joon Y. Park

Journal of Econometrics, 2019, vol. 210, issue 2, 236-267

Abstract: This paper develops the methodology and asymptotic theory for the estimation of longrun variance of continuous time process. We analyze the asymptotic bias and variance of the longrun variance estimator in continuous time, and provide the optimal bandwidth balancing them off and minimizing the asymptotic mean squared error. In the paper, we present not only how to consistently estimate the longrun variance of continuous time process, but also how to choose bandwidth optimally with data dependent procedures, using discrete samples. Our framework is also useful to analyze the high frequency behaviors of usual longrun variance estimators for discrete time series. In particular, we show that they all diverge to infinity as the sampling frequency increases. The relevance and usefulness of our continuous time framework and asymptotic theory are demonstrated by illustration and simulation.

Keywords: Continuous time model; Longrun variance estimator; Kernel estimation; Bandwidth selection (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407618302203
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:210:y:2019:i:2:p:236-267

DOI: 10.1016/j.jeconom.2018.04.006

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).

 
Page updated 2024-12-28
Handle: RePEc:eee:econom:v:210:y:2019:i:2:p:236-267