References
- D. Aaronson and E. French, The effects of progressive taxation on labor supply when hours and wages are jointly determined, J. Hum. Resour. 4 (2009), pp. 386–408.
- J.G. Altonji, Intertemporal substitution in labor supply: evidence from micro data, J. Political Econ. 94 (1986), pp. S176–S215.
- J.D. Angrist, Grouped-data estimation and testing in simple labor-supply models, J. Econom. 47 (1991), pp. 243–266.
- J.D. Angrist, G.W. Imbens, and D.B. Rubin, Identification of causal effects using instrumental variables, J. Am. Stat. Assoc. 91 (1996), pp. 444–455.
- J.D. Angrist and A.B. Krueger, Instrumental variables and the search for identification: from supply and demand to natural experiments, J. Econ. Perspectives 15 (2001), pp. 69–85.
- O. Ashenfelter and J. Heckman, The estimation of income and substitution effects in a model of family labor supply, Econometrica 42 (1974), pp. 73–85.
- A. Babii, Identification and estimation in the functional linear instrumental regression, Tech. Rep., UNC Working paper, 2017.
- D. Benatia, M. Carrasco, and J.P. Florens, Functional linear regression with functional response, J. Econom. 201 (2017), pp. 269–291.
- F.D. Blau and L.M. Kahn, Changes in the labor supply behavior of married women: 1980–2000, J. Labor. Econ. 25 (2007), pp. 393–438.
- G.J. Borjas, The relationship between wages and weekly hours of work: the role of division bias, J. Human Resour. 15 (1980), pp. 409–423.
- C. Camerer, L. Babcock, G. Loewenstein, and R. Thaler, Labor supply of new york city cabdrivers: one day at a time, Q. J. Econ. 112 (1997), pp. 407–441.
- J. Cederbaum, M. Pouplier, P. Hoole, and S. Greven, Functional linear mixed models for irregularly or sparsely sampled data, Stat. Modelling. 16 (2016), pp. 67–88.
- C. Chen, S. Guo, and X. Qiao, Functional linear regression: dependence and error contamination, J. Bus. Econ. Stat. 40 (2022), pp. 444–457.
- D. Dorn, Essays on inequality, spatial interaction, and the demand for skills, Ph.D. diss., University of St. Gallen no. 3613, 2009.
- Z. Eckstein, M. Keane, and O. Lifshitz, Career and family decisions: Cohorts born 1935–1975, Econometrica 87 (2019), pp. 217–253.
- P.H. Eilers and B.D. Marx, Flexible smoothing with b-splines and penalties, Stat. Sci. 11 (1996), pp. 89–121.
- N. Eissa and H.W. Hoynes, Taxes and the labor market participation of married couples: the earned income tax credit, J. Public. Econ. 88 (2004), pp. 1931–1958.
- N. Eissa, Taxation and labor supply of married women: the tax reform act of 1986 as a natural experiment, NBER Working Paper 5023, 1995.
- J.P. Florens and S. Van Bellegem, Instrumental variable estimation in functional linear models, J. Econom. 186 (2015), pp. 465–476.
- A. Gajardo, S. Bhattacharjee, C. Carroll, Y. Chen, X. Dai, J. Fan, P.Z. Hadjipantelis, K. Han, H. Ji, C. Zhu, H.G. Müller, and J.L. Wang, fdapace: Functional Data Analysis and Empirical Dynamics, 2021. Available at https://CRAN.R-project.org/package=fdapace, R package version 0.5.8.
- R. Ghosal and A. Maity, A score based test for functional linear concurrent regression, Econom. Statist. 21 (2022), pp. 114–130.
- J. Goldsmith and J.E. Schwartz, Variable selection in the functional linear concurrent model, Stat. Med. 36 (2017), pp. 2237–2250.
- W.H. Greene, Econometric Analysis, 8th ed., New York, NY, Pearson, 2017.
- J. Heckman, Shadow prices, market wages, and labor supply, Econometrica 42 (1974), pp. 679–694.
- B.T. Heim, The incredible shrinking elasticities married female labor supply, 1978–2002, J. Human Resour. 42 (2007), pp. 881–918.
- T. Hsing and R. Eubank, Theoretical Foundations of Functional Data Analysis, with An Introduction to Linear Operators, New York, NY, John Wiley & Sons, 2015.
- G. Imbens, Instrumental variables: an econometrician's perspective, Stat. Sci. 29 (2014), pp. 323–358.
- A.E. Ivanescu, A.M. Staicu, F. Scheipl, and S. Greven, Penalized function-on-function regression, Comput. Stat. 30 (2015), pp. 539–568.
- G.M. James, T.J. Hastie, and C.A. Sugar, Principal component models for sparse functional data, Biometrika 87 (2000), pp. 587–602.
- P. Kokoszka and M. Reimherr, Introduction to Functional Data Analysis, Boca Raton, FL, CRC Press, 2017.
- D.R. Kowal, D.S. Matteson, and D. Ruppert, Functional autoregression for sparsely sampled data, J. Bus. Econ. Stat. 37 (2019), pp. 97–109.
- A. Leroux, L. Xiao, C. Crainiceanu, and W. Checkley, Dynamic prediction in functional concurrent regression with an application to child growth, Stat. Med. 37 (2018), pp. 1376–1388.
- A. Leroux, L. Xiao, C. Crainiceanu, and W. Checkly, fcr: Functional Concurrent Regression for Sparse Data, 2018. Available at https://CRAN.R-project.org/package=fcr, R package version 1.0.
- A. Lewbel, Using instrumental variables to estimate models with mismeasured regressors, in Handbook of Measurement Error Models, G.Y. Yi, A. Delaigle, and P. Gustafson, eds., Chap. 5, New York, NY, Chapman and Hall/CRC, 2021, pp. 85–96.
- T.E. MaCurdy, An empirical model of labor supply in a life-cycle setting, J. Political Econ. 89 (1981), pp. 1059–1085.
- N. Malfait and J.O. Ramsay, The historical functional linear model, Can. J. Statist. 31 (2003), pp. 115–128.
- J.S. Morris, Functional regression, Annu. Rev. Stat. Appl. 2 (2015), pp. 321–359.
- J. Petrovich, M. Reimherr, and C. Daymont, Highly irregular functional generalized linear regression with electronic health records, J. Royal Statist. Soc.: Ser. C (Applied Statistics) 71 (2022), pp. 806–833.
- J.O. Ramsay and B.W. Silverman, Functional Data Analysis, New York, NY, Springer, 2nd edition, 2005.
- J.O. Ramsay and B.W. Silverman, Applied Functional Data Analysis: Methods and Case Studies, New York, NY, Springer, 2007.
- A.R. Rao and M. Reimherr, Modern multiple imputation with functional data, Stat 10 (2021), p. e331.
- D. Şentürk and H.G. Müller, Functional varying coefficient models for longitudinal data, J. Am. Stat. Assoc. 105 (2010), pp. 1256–1264.
- D. Şentürk and D.V. Nguyen, Varying coefficient models for sparse noise-contaminated longitudinal data, Stat. Sin. 21 (2011), pp. 1831–1856.
- D. Seong and W.K. Seo, Functional instrumental variable regression with an application to estimating the impact of immigration on native wages, preprint (2022), arXiv:2110.12722v2.
- C.D. Tekwe, R.S. Zoh, M. Yang, R.J. Carroll, G. Honvoh, D.B. Allison, M. Benden, and L. Xue, Instrumental variable approach to estimating the scalar-on-function regression model with measurement error with application to energy expenditure assessment in childhood obesity, Stat. Med. 38 (2019), pp. 3764–3781.
- L. Xiao, C. Li, W. Checkley, and C. Crainiceanu, Fast covariance estimation for sparse functional data, Stat. Comput. 28 (2018), pp. 511–522.
- F. Yao, H.G. Müller, and J.L. Wang, Functional data analysis for sparse longitudinal data, J. Am. Stat. Assoc. 100 (2005), pp. 577–590.
- X. Zhang, Q. Zhong, and J.L. Wang, A new approach to varying-coefficient additive models with longitudinal covariates, Comput. Stat. Data. Anal. 145 (2020), p. 106912.