PDSLASSO & LASSOPACK : Stata module for post-selection and post-regularization OLS or IV estimation and inference
- Submitting institution
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Heriot-Watt University
- Unit of assessment
- 17 - Business and Management Studies
- Output identifier
- 20929095
- Type
- G - Software
- Name of software house
- Boston College Department of Economics
- Month
- January
- Year
- 2019
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Working with coauthors Ahrens (ETH Zurich) and Hansen (Chicago), Schaffer developed two packages encompassing eight separate programs which implement a set of advanced econometric estimation and testing procedures for the high dimensional environment for use by applied researchers working in economics and allied disciplines within the Stata statistical software environment. The lassopack package is a suite of programs for L1/L2 penalized regression methods suitable for the high-dimensional setting where the number of predictors p may be large and possibly greater than the number of observations; the pdslasso package employs these methods in routines for estimating structural parameters in linear models with many controls and/or instruments, in a setting where the researcher is interested in estimating the causal impact of one or more (possibly endogenous) causal variables of interest. The packages together were downloaded over 37,000 times between their introduction in February 2018 and December 2020 [source: see PDF upload of written description of the software].
- Author contribution statement
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- Non-English
- No
- English abstract
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