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Relevant bibliographies by topics / Time series regression / Journal articles

Journal articles on the topic 'Time series regression'

To see the other types of publications on this topic, follow the link: Time series regression.

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

Tan, Chang Wei, Christoph Bergmeir, François Petitjean, and Geoffrey I. Webb. "Time series extrinsic regression." Data Mining and Knowledge Discovery 35, no. 3 (March 11, 2021): 1032–60. http://dx.doi.org/10.1007/s10618-021-00745-9.

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2

Truong, Young K., and Charles J. Stone. "SEMIPARAMETRIC TIME SERIES REGRESSION." Journal of Time Series Analysis 15, no. 4 (July 1994): 405–28. http://dx.doi.org/10.1111/j.1467-9892.1994.tb00202.x.

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3

Truong, Young K. "Nonparametric time series regression." Annals of the Institute of Statistical Mathematics 46, no. 2 (June 1994): 279–93. http://dx.doi.org/10.1007/bf01720585.

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4

Feng, Yanming. "Regression and Hypothesis Tests for Multivariate GNSS State Time Series." Journal of Global Positioning Systems 11, no. 1 (June 30, 2012): 33–45. http://dx.doi.org/10.5081/jgps.11.1.33.

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5

Choudhury, Askar H., Robert Hubata, and Robert D. St Louis. "Understanding Time-Series Regression Estimators." American Statistician 53, no. 4 (November 1999): 342. http://dx.doi.org/10.2307/2686054.

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6

Brännäs, Kurt, and Per Johansson. "Time series count data regression." Communications in Statistics - Theory and Methods 23, no. 10 (January 1994): 2907–25. http://dx.doi.org/10.1080/03610929408831424.

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7

Choudhury, Askar H., Robert Hubata, and Robert D. St. Louis. "Understanding Time-Series Regression Estimators." American Statistician 53, no. 4 (November 1999): 342–48. http://dx.doi.org/10.1080/00031305.1999.10474487.

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8

Jaditz, Ted, and Leigh A. Riddick. "Time-Series Near-Neighbor Regression." Studies in Nonlinear Dynamics and Econometrics 4, no. 1 (April 1, 2000): 35–44. http://dx.doi.org/10.1162/108118200569171.

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9

Cai, Zongwu. "REGRESSION QUANTILES FOR TIME SERIES." Econometric Theory 18, no. 1 (February 2002): 169–92. http://dx.doi.org/10.1017/s0266466602181096.

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In this paper we study nonparametric estimation of regression quantiles for time series data by inverting a weighted Nadaraya–Watson (WNW) estimator of conditional distribution function, which was first used by Hall, Wolff, and Yao (1999, Journal of the American Statistical Association 94, 154–163). First, under some regularity conditions, we establish the asymptotic normality and weak consistency of the WNW conditional distribution estimator for α-mixing time series at both boundary and interior points, and we show that the WNW conditional distribution estimator not only preserves the bias, variance, and, more important, automatic good boundary behavior properties of local linear “double-kernel” estimators introduced by Yu and Jones (1998, Journal of the American Statistical Association 93, 228–237), but also has the additional advantage of always being a distribution itself. Second, it is shown that under some regularity conditions, the WNW conditional quantile estimator is weakly consistent and normally distributed and that it inherits all good properties from the WNW conditional distribution estimator. A small simulation study is carried out to illustrate the performance of the estimates, and a real example is also used to demonstrate the methodology.

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10

Mammen, E., J. P. Nielsen, and B. Fitzenberger. "Generalized linear time series regression." Biometrika 98, no. 4 (October 13, 2011): 1007–14. http://dx.doi.org/10.1093/biomet/asr044.

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11

Pewsey, Arthur, and C. W. Ostrom. "Time Series Analysis (Regression Techniques)." Statistician 40, no. 4 (1991): 453. http://dx.doi.org/10.2307/2348738.

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12

Hung, Chihli, Chih-Neng Hung, and Szu-Yin Lin. "Predicting Time Series Using Integration of Moving Average and Support Vector Regression." International Journal of Machine Learning and Computing 4, no. 6 (2014): 491–95. http://dx.doi.org/10.7763/ijmlc.2014.v6.460.

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13

Caby, Errol, Allan D. R. McQuarrie, and Chih-Ling Tsai. "Regression and Time Series Model Selection." Technometrics 42, no. 2 (May 2000): 214. http://dx.doi.org/10.2307/1271469.

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14

Härdle, Wolfgang, and Philippe Vieu. "KERNEL REGRESSION SMOOTHING OF TIME SERIES." Journal of Time Series Analysis 13, no. 3 (May 1992): 209–32. http://dx.doi.org/10.1111/j.1467-9892.1992.tb00103.x.

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15

Ronchetti, Elvezio, Allan D. R. McQuarrie, and Chih-Ling Tsai. "Regression and Time Series Model Selection." Journal of the American Statistical Association 95, no. 451 (September 2000): 1008. http://dx.doi.org/10.2307/2669491.

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16

Chen, Jia, Jiti Gao, and Degui Li. "Estimation in semiparametric time series regression." Statistics and Its Interface 4, no. 2 (2011): 243–51. http://dx.doi.org/10.4310/sii.2011.v4.n2.a18.

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17

Ray, Bonnie K. "Regression Models for Time Series Analysis." Technometrics 45, no. 4 (November 2003): 364. http://dx.doi.org/10.1198/tech.2003.s166.

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18

Cavanaugh, Joseph. "Regression Models for Time Series Analysis." Journal of the American Statistical Association 99, no. 465 (March 2004): 299. http://dx.doi.org/10.1198/jasa.2004.s324.

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19

Weba, Michael. "Optimized regression models for time series." Statistics 20, no. 1 (January 1989): 109–23. http://dx.doi.org/10.1080/02331888908802149.

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20

Ma, Yunyan, and Yihui Luan. "Covariate-Adjusted Regression for Time Series." Communications in Statistics - Theory and Methods 41, no. 3 (February 2012): 422–36. http://dx.doi.org/10.1080/03610926.2011.609319.

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21

Lin, T. C., M. Pourahmadi, and A. Schick. "Regression Models with Time Series Errors." Journal of Time Series Analysis 20, no. 4 (July 1999): 425–33. http://dx.doi.org/10.1111/1467-9892.00147.

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22

Truong, Young K. "Robust nonparametric regression in time series." Journal of Multivariate Analysis 41, no. 2 (May 1992): 163–77. http://dx.doi.org/10.1016/0047-259x(92)90064-m.

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23

Kedem, Benjamin, and Konstantinos Fokianos. "Regression Theory for Categorical Time Series." Statistical Science 18, no. 3 (August 2003): 357–76. http://dx.doi.org/10.1214/ss/1076102425.

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24

Junus, Noor Wahida Md. "Predicting Penang Road Accidents Influences: Time Series Regression Versus Structural Time Series." Indian Journal of Science and Technology 8, no. 1 (January 20, 2015): 1–10. http://dx.doi.org/10.17485/ijst/2015/v8i30/84147.

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25

Ledolter, Johannes. "Smoothing Time Series with Local Polynomial Regression on Time." Communications in Statistics - Theory and Methods 37, no. 6 (February 11, 2008): 959–71. http://dx.doi.org/10.1080/03610920701693843.

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26

Jones, Richard H. "Time series regression with unequally spaced data." Journal of Applied Probability 23, A (1986): 89–98. http://dx.doi.org/10.2307/3214345.

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Regression analysis with stationary errors is extended to the case when observations are not equally spaced. The errors are modelled as either a discrete-time ARMA process with missing observations, or as a continuous-time autoregression with observational error observed at arbitrary times. Using a state-space representation, a Kalman filter is used to calculate the exact likelihood. The linear regression coefficients are separated out of the likelihood so non-linear optimization is required only with respect to the parameters modelling the error structure.

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27

Fikry Mahmoud, Rania. "ESTIMATING CO-INTEGRATING REGRESSION IN TIME SERIES." Fayoum Journal of Agricultural Research and Development 33, no. 1 (January 1, 2019): 92–102. http://dx.doi.org/10.21608/fjard.2019.190383.

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28

Phillips, P. C. B. "Regression Theory for Near-Integrated Time Series." Econometrica 56, no. 5 (September 1988): 1021. http://dx.doi.org/10.2307/1911357.

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29

Phillips, P. C. B. "Time Series Regression with a Unit Root." Econometrica 55, no. 2 (March 1987): 277. http://dx.doi.org/10.2307/1913237.

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30

Attouch, Mohammed Kadi, Ali Laksaci, and Elias Ould Sa^|^iuml;d. "Robust Regression for Functional Time Series Data." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 42, no. 2 (2012): 125–43. http://dx.doi.org/10.14490/jjss.42.125.

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31

Figueroa-López, José E., and Michael Levine. "Nonparametric regression with rescaled time series errors." Journal of Time Series Analysis 34, no. 3 (April 25, 2013): 345–61. http://dx.doi.org/10.1111/jtsa.12017.

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32

Harvey, A. C., and P. M. Robinson. "EFFICIENT ESTIMATION OF NONSTATIONARY TIME SERIES REGRESSION." Journal of Time Series Analysis 9, no. 3 (May 1988): 201–14. http://dx.doi.org/10.1111/j.1467-9892.1988.tb00464.x.

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33

Phillips, P. C. B., and S. N. Durlauf. "Multiple Time Series Regression with Integrated Processes." Review of Economic Studies 53, no. 4 (August 1986): 473. http://dx.doi.org/10.2307/2297602.

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34

Safi, Samir. "Variance Estimation in Time Series Regression Models." Journal of Modern Applied Statistical Methods 7, no. 2 (November 1, 2008): 506–13. http://dx.doi.org/10.22237/jmasm/1225512900.

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35

McCullough, B. D. "Predictions in Time Series Using Regression Models." Technometrics 45, no. 1 (February 2003): 102. http://dx.doi.org/10.1198/tech.2003.s17.

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36

Fuentes, Montserrat. "Predictions in Time Series Using Regression Models." Journal of the American Statistical Association 98, no. 463 (September 2003): 768–69. http://dx.doi.org/10.1198/jasa.2003.s289.

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37

Wang, Qin, and Rongning Wu. "Semiparametric Regression for Time Series of Counts." Communications in Statistics - Theory and Methods 44, no. 5 (March 4, 2015): 983–95. http://dx.doi.org/10.1080/03610926.2012.750359.

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38

Bhaskaran, Krishnan, Antonio Gasparrini, Shakoor Hajat, Liam Smeeth, and Ben Armstrong. "Time series regression studies in environmental epidemiology." International Journal of Epidemiology 42, no. 4 (June 12, 2013): 1187–95. http://dx.doi.org/10.1093/ije/dyt092.

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39

Jones, Richard H. "Time series regression with unequally spaced data." Journal of Applied Probability 23, A (1986): 89–98. http://dx.doi.org/10.1017/s0021900200117000.

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Abstract:

Regression analysis with stationary errors is extended to the case when observations are not equally spaced. The errors are modelled as either a discrete-time ARMA process with missing observations, or as a continuous-time autoregression with observational error observed at arbitrary times. Using a state-space representation, a Kalman filter is used to calculate the exact likelihood. The linear regression coefficients are separated out of the likelihood so non-linear optimization is required only with respect to the parameters modelling the error structure.

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40

Shiohama, Takayuki, and Masanobu Taniguchi. "Sequential estimation for time series regression models." Journal of Statistical Planning and Inference 123, no. 2 (July 2004): 295–312. http://dx.doi.org/10.1016/s0378-3758(03)00153-8.

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41

Ward, B. D., H. Garavan, T. J. Ross, A. S. Bloom, R. W. Cox, and E. A. Stein. "Nonlinear Regression for FMRI Time Series Analysis." NeuroImage 7, no. 4 (May 1998): S767. http://dx.doi.org/10.1016/s1053-8119(18)31600-8.

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42

Jansson, Michael, and Niels Haldrup. "REGRESSION THEORY FOR NEARLY COINTEGRATED TIME SERIES." Econometric Theory 18, no. 6 (September 24, 2002): 1309–35. http://dx.doi.org/10.1017/s0266466602186026.

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This paper proposes a notion of near cointegration and generalizes several existing results from the cointegration literature to the case of near cointegration. In particular, the properties of conventional cointegration methods under near cointegration are characterized, thereby investigating the robustness of cointegration methods. In addition, we obtain local asymptotic power functions of five cointegration tests that take cointegration as the null hypothesis.

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43

Tran, Lanh, George Roussas, Sidney Yakowitz, and B. Truong Van. "Fixed-design regression for linear time series." Annals of Statistics 24, no. 3 (June 1996): 975–91. http://dx.doi.org/10.1214/aos/1032526952.

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44

Robinson, P. M., and F. J. Hidalgo. "Time series regression with long-range dependence." Annals of Statistics 25, no. 1 (February 1997): 77–104. http://dx.doi.org/10.1214/aos/1034276622.

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45

Shi, Peide. "M-type regression splines involving time series." Journal of Statistical Planning and Inference 61, no. 1 (May 1997): 17–37. http://dx.doi.org/10.1016/s0378-3758(97)89714-5.

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46

Woody, Jonathan. "Time series regression with persistent level shifts." Statistics & Probability Letters 102 (July 2015): 22–29. http://dx.doi.org/10.1016/j.spl.2015.03.011.

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47

Ioannides, D. A. "Fixed design regression quantiles for time series." Statistics & Probability Letters 68, no. 3 (July 2004): 235–45. http://dx.doi.org/10.1016/j.spl.2003.12.005.

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48

Steigerwald, Douglas G. "Adaptive estimation in time series regression models." Journal of Econometrics 54, no. 1-3 (October 1992): 251–75. http://dx.doi.org/10.1016/0304-4076(92)90108-4.

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49

Zhao, Zhibiao, and Wei Biao Wu. "Confidence bands in nonparametric time series regression." Annals of Statistics 36, no. 4 (August 2008): 1854–78. http://dx.doi.org/10.1214/07-aos533.

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50

Hu, Shuhe, Chunhua Zhu, Yebin Chen, and Lichun Wang. "Fixed-design regression for Linear time series." Acta Mathematica Scientia 22, no. 1 (January 2002): 9–18. http://dx.doi.org/10.1016/s0252-9602(17)30450-2.

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