Revisiting the Missing R&D-Patent Relation: Challenges and Solutions for Firm Fixed Effects Models

The repository provides the detailed examples and codes following the paper Chuang, H.C., Hsu, P.H., Kuan, C.M., Yang, J.C., 2025. Revisiting the Missing R&D-Patent Relation: Challenges and Solutions for Firm Fixed Effects Models. The paper is available at SSRN. https://ssrn.com/abstract=4636846

In this set of code, we demonstrate how users can implement the following four ways to identify the potential bias from fixed effects regressions:

1. Linear regression and Poisson regression with and without firm FEs
2. Within, Between, and Overall R squared
3. Adjusted Hausman-Taylor approach
4. Post-regularization LASSO (PRL) and Double Machine Learning (DML) methods for both linear and Poisson regression

Data and Code Availability:

We have prepared the following documents:

1. Replicate_code_MissingRDPatent_ML.do contains the STATA code that produces our main results in the paper;
2. Replicate_data_MissingRDPatent_ML.dta contains the dataset used in the paper.
3. panelr2.ado calculates overall, between, and within R squared after estimation.

Sample Data Construction

• PERMCO here is a masked firm identifier.
• Variables definitions are as follows, and interested readers are referred to our paper and the original data sources to collect them.
  • patent: Number of patent (data from USPTO PatentsView)
  • lnnpatent: The logarithm of one plus the number of patents (data from USPTO PatentsView)
  • RDAT: Research and development (R&D) expenses scaled by the total asset (data from Compustat)
  • lnME: The logarithm of market equity (data from CRSP)
  • RD_missing: A binary variable indicating that R&D expenses are missing (data from Compustat)
  • lnAge: The logarithm of years where a firm exists in Compustat (data from Compustat)
  • lnK2L: The logarithm of net property, plant, and equipment divided by the number of employees(data from Compustat)
  • TobinQ: The market value of a firm divided by its replacement cost (data from Compustat and CRSP)
  • ROA: Return on assets (data from Compustat)
  • Leverage: Long-term debt plus current debt, divided by total assets (data from Compustat)
  • CASHAT: Cash to the total asset (data from Compustat)
  • KZidx: The financial constrain index (data from Compustat)
  • Instown: The total institutional ownership from 13f filings divided by the total number of shares outstanding (data from CRSP and Thomson/Refinitiv)
  • oms_HHidx: One minus the Herfindahl-Hirschman index (HHI) based on three-digit industry classification (data from Compustat)
  • oms_HHidx_square: Square of oms_HHidx (data from Compustat)

Results for Sample Data

• Depends on computer capacity; we also provide results and corresponding STATA log files.

  • Log files:

    • ReplicateLog_OLS_FE_adjHT
    • ReplicateLog_PRL_Linear
    • ReplicateLog_DML_Linear
    • ReplicateLog_Poisson_FE
    • ReplicateLog_PRL_Poisson
    • ReplicateLog_DML_Poisson

  • csv files:

    • FE_OLS_adjHT_estimates.csv
    • PRL_Linear_estimates.csv
    • DML_Linear_estimates.csv
    • Poisson_FE_estimates.csv
    • PRL_Poisson_estimates.csv
    • DML_Poisson_estimates.csv

Software

• The STATA version to fully implement the replicate code is: STATA 17.
• The LASSO inference approaces: poregress, xporegress, popoisson, and xpopoisson are available since STATA 16. The cluster standard error for those methods are only available since STATA 17. The robust standard error can still be used for those methods in STATA 16.
• The following commands may need to be installed and updated on STATA to run the replicate code: reghdfe, ppmlhdfe, and esttab.
• To export the R squared values correctly to a CSV file, copy panelr2.ado to C:\Users\yourPC\ado\personal\ folder, and then update esttab by running ssc install estout, replace command in STATA.

Contact

Please contact Po-Hsuan Hsu (pohsuanhsu@mx.nthu.edu.tw) or Hui-Ching Chuang (huichingc@gmail.com) for any questions regarding the data. Please see the paper for more information. If you use these data sets, please CITE this paper as the data source.

Citation

Chuang, Hui-Ching and Hsu, Po-Hsuan and Kuan, Chung‐Ming and Yang, Jui-Chung, Revisiting the Missing R&D-Patent Relation: Challenges and Solutions for Firm Fixed Effects Models (2025). Available at SSRN: https://ssrn.com/abstract=4636846 or http://dx.doi.org/10.2139/ssrn.4636846

@article{chuang2022reexam, 
         title={Revisiting the Missing R&D-Patent Relation: Challenges and Solutions for Firm Fixed Effects Models},
         author={Chuang, Hui-Ching and Hsu, Po-Hsuan and Kuan, Chung-Ming and Yang, Jui-Chung},
         journal={Available at SSRN. https://ssrn.com/abstract=4636846},
         year={2022}
}