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Testing covariates in high dimension linear regression with latent factors

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  • Lan, Wei
  • Ding, Yue
  • Fang, Zheng
  • Fang, Kuangnan

Abstract

We propose here both F-test and z-test (or t-test) for testing global significance and individual effect of each single predictor respectively in high dimension regression model when the explanatory variables follow a latent factor structure (Wang, 2012). Under the null hypothesis, together with fairly mild conditions on the explanatory variables and latent factors, we show that the proposed F-test and t-test are asymptotically distributed as weighted chi-square and standard normal distribution respectively. That leads to quite different test statistics and inference procedures, as compared with that of Zhong and Chen (2011) when the explanatory variables are weakly dependent. Moreover, based on the p-value of each predictor, the method of Storey et al. (2004) can be used to implement the multiple testing procedure, and we can achieve consistent model selection as long as we can select the threshold value appropriately. All the results are further supported by extensive Monte Carlo simulation studies. The practical utility of the two proposed tests are illustrated via a real data example for index funds tracking in China stock market.

Suggested Citation

  • Lan, Wei & Ding, Yue & Fang, Zheng & Fang, Kuangnan, 2016. "Testing covariates in high dimension linear regression with latent factors," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 25-37.
    • Handle: RePEc:eee:jmvana:v:144:y:2016:i:c:p:25-37
    DOI: 10.1016/j.jmva.2015.10.013
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    References listed on IDEAS

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    1. Meinshausen, Nicolai & Meier, Lukas & Bühlmann, Peter, 2009. "p-Values for High-Dimensional Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1671-1681.
    2. Zhong, Ping-Shou & Chen, Song Xi, 2011. "Tests for High-Dimensional Regression Coefficients With Factorial Designs," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 260-274.
    3. H. Wang, 2012. "Factor profiled sure independence screening," Biometrika, Biometrika Trust, vol. 99(1), pages 15-28.
    4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    5. John D. Storey & Jonathan E. Taylor & David Siegmund, 2004. "Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 187-205, February.
    6. Jelle J. Goeman & Hans C. van Houwelingen & Livio Finos, 2011. "Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control," Biometrika, Biometrika Trust, vol. 98(2), pages 381-390.
    7. Li, Qi, 2000. "Efficient Estimation of Additive Partially Linear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 1073-1092, November.
    8. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    9. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
    10. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    11. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    12. Wei Lan & Hansheng Wang & Chih-Ling Tsai, 2014. "Testing covariates in high-dimensional regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 279-301, April.
    13. Wang, Hansheng, 2009. "Forward Regression for Ultra-High Dimensional Variable Screening," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1512-1524.
    14. Golubev, Georgi & Härdle, Wolfgang, 2000. "On adaptive estimation in partial linear models," SFB 373 Discussion Papers 2000,21, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    15. Cun-Hui Zhang & Stephanie S. Zhang, 2014. "Confidence intervals for low dimensional parameters in high dimensional linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 217-242, January.
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