Download Advances in Time Series Methods and Applications : The A. by Wai Keung Li, David A. Stanford, Hao Yu PDF

By Wai Keung Li, David A. Stanford, Hao Yu

This quantity studies and summarizes a few of A. I. McLeod's major contributions to time sequence research. It additionally includes unique contributions to the sphere and to similar parts by way of contributors of the festschrift held in June 2014 and acquaintances of Dr. McLeod. protecting a various variety of state of the art issues, this quantity good balances utilized and theoretical learn throughout fourteen contributions by means of specialists within the box. it is going to be of curiosity to researchers and practitioners in time sequence, econometricians, and graduate scholars in time sequence or econometrics, in addition to environmental statisticians, info scientists, statisticians drawn to graphical types, and researchers in quantitative possibility management.

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2011). Large vector auto regressions. ML]. 30. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58(1), 267–288. 31. Valdés-Sosa, P. , Sánchez-Bornot, J. , et al. (2005). Estimating brain functional connectivity with sparse multivariate autoregression. Philosophical Transactions Royal Society B, 360(1457), 969–981. 32. , & Tsai, C. (2007). Regression coefficients and autoregressive order shrinkage and selection via the lasso.

1, 19, 48]) suggest that the frequency of transactions should carry information about the state of the market. They predict the existence of transaction clustering. , [21, 22]). Like an ARCH process, an ACD process always has nonnegative autocorrelation at any lag, resulting in a spectral mode at frequency zero under (and only under) the alternative hypothesis. BP/LB tests do not exploit such a one-sided nature. We first propose a one-sided consistent test for ACD effects, which exploits the onesided nature of the alternative.

17. Heyse, J. (1985). Partial lag autocorrelation and partial process autocorrelation for vector time series, with applications. D. dissertation, Temple University. 18. , & Chang, Y. (2008). Subset selection for vector autoregressive processes using LASSO. Computational Statistics and Data Analysis, 52, 3645–3657. 19. , & Fu, W. (2000). Asymptotics for LASSO-type estimators. The Annals of Statistics, 28, 1356–1378. 20. Kock, A. B. (2012). On the oracle property of the adaptive lasso in stationary and nonstationary autoregressions.

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