Downloads
cwp351515.pdf
PDF | 354.4 KB
Models with high-dimensional covariates arise frequently in economics and other fields. Often, only a few covariates have important effects on the dependent variable. When this happens, the model is said to be sparse. In applications, however, it is not known which covariates are important and which are not. This paper reviews methods for discriminating between important and unimportant covariates with particular attention given to methods that discriminate correctly with probability approaching 1 as the sample size increases. Methods are available for a wide variety of linear, nonlinear, semiparametric, and nonparametric models. The performance of some of these methods in finite samples is illustrated through Monte Carlo simulations and an empirical example.
Authors
Northwestern University
Working Paper details
- DOI
- 10.1920/wp.cem.2015.3515
- Publisher
- Institute for Fiscal Studies
Suggested citation
Horowitz, J. (2015). Variable selection and estimation in high-dimensional models. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/variable-selection-and-estimation-high-dimensional-models (accessed: 14 May 2024).
More from IFS
Understand this issue
Where next for the state pension?
13 December 2023
Social mobility and wealth
12 December 2023
Autumn Statement 2023: IFS analysis
23 November 2023
Policy analysis
The past and future of UK health spending
14 May 2024
Recent trends in and the outlook for health-related benefits
19 April 2024
Progression of nurses within the NHS
12 April 2024
Academic research
The role of hospital networks in individual mortality
13 May 2024
Forced displacement, mental health, and child development: Evidence from Rohingya refugees
10 May 2024
Leveraging edutainment and social networks to foster interethnic harmony
10 May 2024