ESTIMATING THE PERSISTENCE AND THE AUTOCORRELATION FUNCTION OF A TIME SERIES THAT IS MEASURED WITH ERROR

By P. R. Hansen, Asger Lunde. Econometric Theory, 2013

91 citations recorded by the source corpus.

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 that are imperfect estimates of actual volatility. In an empirical analysis using realized measures for the Dow Jones industrial average 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 that are based on the IV estimator have better finite sample properties in this context.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/6a08a6da69cbb5d104cde459d113519e251ef737

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, unknown): “We employ their preferred specification, a two-stage least squares estimator in which lagged realized variances of order 4–10 are used as instrumental variables (see Hansen & Lunde, 2014, p. 82).”
Decoupling the Short- and Long-Term Behavior of Stochastic Volatility (2016, unknown): “A similar estimation problem was considered in Hansen and Lunde (2013), albeit in a different setup.”
Exploiting the errors: A simple approach for improved volatility forecasting (2016, background): “8 As previously noted, Hansen and Lunde (2014) propose the use of an instrumental variable procedure for dealing with this attenuation bias and obtain a consistent estimator of the autoregressive parameters of the true latent IV process.”
Forecasting Volatility Using High-Frequency Data (2011, unknown): “Hansen and Lunde (2010) demonstrate that measurement errors of the magnitude found in realized measures of daily volatility cause the autocorrelation function of the observed time series to look distinctively different from that of latent volatility.”