 Estimating the Persistence and the Autocorrelation Function of a Time Series that is Measured with ErrorPeter Hansen and Asger Lunde () CREATES Research Papers from Department of Economics and Business Economics, Aarhus University Abstract: An economic time series can often be viewed as a noisy proxy for an underlying economic variable. Measurement errors will influence the dynamic properties of the observed process and may conceal the persistence of the underlying time series. In this paper we develop instrumental variable (IV) methods for extracting information about the latent process. Our framework can be used to estimate the autocorrelation function of the latent volatility process and a key persistence parameter. Our analysis is motivated by the recent literature on realized (volatility) measures, such as the realized variance, that are imperfect estimates of actual volatility. In an empirical analysis using realized measures for the DJIA stocks we find the underlying volatility to be near unit root in all cases. Although standard unit root tests are asymptotically justified, we find them to be misleading in our application despite the large sample. Unit root tests based on the IV estimator have better finite sample properties in this context. Keywords: Persistence; Autocorrelation Function; Measurement Error; Instrumental Variables; Realized Variance; Realized Kernel; Volatility (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2010-08 Access Statistics for this paper More papers in CREATES Research Papers from Department of Economics and Business Economics, Aarhus University |
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