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- Article
Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data
By: Dean Hyslop and Wilbur Townsend
This article analyzes earnings dynamics and measurement error using a matched longitudinal sample of individuals’ survey and administrative earnings. In line with previous literature, the reported differences are characterized by both persistent and transitory factors.... View Details
Keywords: Earnings Dynamics; Measurement Error; Panel Data; Validation Study; Business Earnings; Measurement and Metrics; Forecasting and Prediction
Hyslop, Dean, and Wilbur Townsend. "Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data." Journal of Business & Economic Statistics 38, no. 2 (2020).
- 2015
- Working Paper
Measurement Errors of Expected-Return Proxies and the Implied Cost of Capital
Despite their popularity as proxies of expected returns, the implied cost of capital's (ICC) measurement error properties are relatively unknown. Through an in-depth analysis of a popular implementation of ICCs by Gebhardt, Lee, and Swaminathan (2001) (GLS), I show... View Details
Wang, Charles C.Y. "Measurement Errors of Expected-Return Proxies and the Implied Cost of Capital." Harvard Business School Working Paper, No. 13-098, May 2013. (Revised February 2015.)
- 2023
- Working Paper
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... View Details
Keywords: Racial Disparity; Paycheck Protection Program; Measurement Error; AI and Machine Learning; Race; Measurement and Metrics; Equality and Inequality; Prejudice and Bias; Forecasting and Prediction; Outcome or Result
Scharfstein, David S., and Sergey Chernenko. "The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities." Working Paper, April 2023.
- 11 Jun 2013
- Working Paper Summaries
Measurement Errors of Expected Returns Proxies and the Implied Cost of Capital
Keywords: by Charles C.Y. Wang
- October 1994
- Article
Aggregation, Specification and Measurement Errors in Product Costing
By: S. Datar and M. Gupta
Datar, S., and M. Gupta. "Aggregation, Specification and Measurement Errors in Product Costing." Accounting Review 69, no. 4 (October 1994): 567–591.
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data,... View Details
- April 2023
- Article
The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences
By: Armin Falk, Anke Becker, Thomas Dohmen, David B. Huffman and Uwe Sunde
Incentivized choice experiments are a key approach to measuring preferences in economics but are also costly. Survey measures are a low-cost alternative but can suffer from additional forms of measurement error due to their hypothetical nature. This paper seeks to... View Details
Keywords: Survey Validation; Experiment; Preference Measurement; Surveys; Economics; Behavior; Measurement and Metrics
Falk, Armin, Anke Becker, Thomas Dohmen, David B. Huffman, and Uwe Sunde. "The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences." Management Science 69, no. 4 (April 2023): 1935–1950.
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details
Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science
Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).
- January 2025
- Technical Note
AI vs Human: Analyzing Acceptable Error Rates Using the Confusion Matrix
By: Tsedal Neeley and Tim Englehart
This technical note introduces the confusion matrix as a foundational tool in artificial intelligence (AI) and large language models (LLMs) for assessing the performance of classification models, focusing on their reliability for decision-making. A confusion matrix... View Details
Keywords: Reliability; Confusion Matrix; AI and Machine Learning; Decision Making; Measurement and Metrics; Performance
Neeley, Tsedal, and Tim Englehart. "AI vs Human: Analyzing Acceptable Error Rates Using the Confusion Matrix." Harvard Business School Technical Note 425-049, January 2025.
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement... View Details
- Article
Measuring the Scientific Effectiveness of Contact Tracing: Evidence from a Natural Experiment
By: Thiemo Fetzer and Thomas Graeber
Contact tracing has for decades been a cornerstone of the public health approach to epidemics, including Ebola, severe acute respiratory syndrome, and now COVID-19. It has not yet been possible, however, to causally assess the method’s effectiveness using a randomized... View Details
Fetzer, Thiemo, and Thomas Graeber. "Measuring the Scientific Effectiveness of Contact Tracing: Evidence from a Natural Experiment." Proceedings of the National Academy of Sciences 118, no. 33 (August 17, 2021): 1–4.
- Other Article
Sustainable Strategies and Net-Zero Goals
By: Mark L. Frigo, Robert S. Kaplan and Karthik Ramanna
In a recent Harvard Business Review article, Kaplan and Ramanna describe a rigorous approach, the E-liability method, for companies’ ESG reporting, especially as it pertains to GHG emissions measurements. They argue that the current standards for measuring... View Details
Keywords: Measurement; Sustainability; Net-zero Emissions; Environmental Sustainability; Integrated Corporate Reporting; Measurement and Metrics; Strategy
Frigo, Mark L., Robert S. Kaplan, and Karthik Ramanna. "Sustainable Strategies and Net-Zero Goals." Special Issue on Sustainability. Strategic Finance 103, no. 10 (April 2022): 42–49.
- Spring 2013
- Article
Does Mandatory IFRS Adoption Improve the Information Environment?
By: Joanne Horton, George Serafeim and Ioanna Serafeim
We examine the effect of mandatory International Financial Reporting Standards (IFRS) adoption on firms' information environment. We find that after mandatory IFRS adoption, consensus forecast errors decrease for firms that mandatorily adopt IFRS relative to forecast... View Details
Keywords: International Accounting; Financial Reporting; Standards; Information; Quality; Earnings Management
Horton, Joanne, George Serafeim, and Ioanna Serafeim. "Does Mandatory IFRS Adoption Improve the Information Environment?" Contemporary Accounting Research 30, no. 1 (Spring 2013): 388–423.
- Awards
Runner-Up for Best Paper Award, INFORMS Workshop on Data Science, 2018
Runner Up for the 2018 Best Paper Award at the INFORMS Workshop on Data Science for "Using Data-Mined Variables in Causal Inference Tasks: A Random Forest Approach to the Measurement Error Problem" with Mochen Yang, Gordon Burtch, and Gediminas Adomavicius. View Details
- 2022
- Working Paper
The Stock Market Value of Human Capital Creation
By: Matthias Regier and Ethan Rouen
We develop a measure of firm-year-specific human capital investment from publicly disclosed personnel expenses (PE) and examine the stock market valuation of this investment. Measuring the future value of PE (PEFV) based on the relation between lagged... View Details
Regier, Matthias, and Ethan Rouen. "The Stock Market Value of Human Capital Creation." Harvard Business School Working Paper, No. 21-047, October 2020. (Revised March 2022.)
- April 2023
- Article
The Stock Market Valuation of Human Capital Creation
By: Ethan Rouen and Matthias Regier
We develop a measure of firm-year-specific human capital investment from publicly disclosed personnel expenses (PE) and examine the stock market valuation of this investment. Measuring the future value of PE (PEFV) based on the relation between... View Details
Rouen, Ethan, and Matthias Regier. "The Stock Market Valuation of Human Capital Creation." Art. 102384. Journal of Corporate Finance 79 (April 2023).
- Research Summary
Equity Valuation
Professor Wang’s research utilizes valuation theory to explain how firm fundamentals are related to the expected rates of equity returns and their term structures. His research provides strong evidence that valuation-based proxies of expected returns outperform the... View Details
- 2024
- Working Paper
Finance Without Exotic Risk
By: Pedro Bordalo, Nicola Gennaioli, Rafael La Porta and Andrei Shleifer
We address the joint hypothesis problem in cross-sectional asset pricing by using measured analyst expectations of earnings growth. We construct a firm-level measure of Expectations Based Returns (EBRs) that uses analyst forecast errors and revisions and shuts down any... View Details
Bordalo, Pedro, Nicola Gennaioli, Rafael La Porta, and Andrei Shleifer. "Finance Without Exotic Risk." NBER Working Paper Series, No. 33004, September 2024.
- March 2019
- Article
Open Source Software and Firm Productivity
By: Frank Nagle
As open source software (OSS) is increasingly used as a key input by firms, understanding its impact on productivity becomes critical. This study measures the firm-level productivity impact of nonpecuniary (free) OSS and finds a positive and significant value-added... View Details
Keywords: Applications and Software; Open Source Distribution; Performance Productivity; Information Technology; Strategy
Nagle, Frank. "Open Source Software and Firm Productivity." Management Science 65, no. 3 (March 2019): 1191–1215.