Estimation and Testing for High-dimensional Near Unit Root Time Series

In this paper, new form of the parameters of AR(1) with constant term with missing observations has been derived by using Ordinary Least Squares (OLS) method, Also, the properties of OLS estimator are discussed, moreover, an extension of Youssef [18]has been suggested for AR(1) with constant with missing observations. A comparative study between (OLS), Yule-Walker (YW) and modification of the ordinary least squares (MOLS) is considered in the case of stationary and near unit root time series, using Monte Carlo simulation.

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NONPARAMETRIC TRANSFORMATION REGRESSION WITH NONSTATIONARY DATA

Econometric Theory ◽

10.1017/s026646661400070x ◽

2014 ◽

Vol 32 (1) ◽

pp. 1-29 ◽

Cited By ~ 5

Author(s):

Oliver Linton ◽

Qiying Wang

Keyword(s):

Time Series ◽

Unit Root ◽

Kernel Regression ◽

American Statistical Association ◽

Correlation Structure ◽

Regression Estimator ◽

Statistical Association ◽

Nonstationary Data ◽

Nonparametric Transformation ◽

Near Unit Root

We examine a kernel regression estimator for time series that takes account of the error correlation structure as proposed by Xiao et al. (2003, Journal of the American Statistical Association 98, 980–992). We show that this method continues to improve estimation in the case where the regressor is a unit root or a near unit root process.

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

Econometric Theory ◽

10.1017/s0266466613000121 ◽

2013 ◽

Vol 30 (1) ◽

pp. 60-93 ◽

Cited By ~ 31

Author(s):

Peter R. Hansen ◽

Asger Lunde

Keyword(s):

Time Series ◽

Autocorrelation Function ◽

Unit Root ◽

Measurement Errors ◽

Dynamic Properties ◽

Unit Root Tests ◽

Economic Time Series ◽

Finite Sample ◽

Latent Process ◽

Near Unit Root

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.

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