Publications
Publications
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- All HBS Web
(1,116)
- Faculty Publications (221)
- August 2021
- Article
Multiple Imputation Using Gaussian Copulas
By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use... View Details
- July 19, 2021
- Article
Do Most Family Businesses Really Fail by the Third Generation?
By: Josh Baron and Rob Lachenauer
Perhaps the most commonly-cited statistic about family businesses is their failure rates. Most articles or speeches about family businesses start with some version of the “three-generation rule,” which suggests that most don’t survive beyond three generations. But that... View Details
- June 2021
- Technical Note
Introduction to Linear Regression
By: Michael Parzen and Paul Hamilton
This technical note introduces (from an applied point of view) the theory and application of simple and multiple linear regression. The motivation for the model is introduced, as well as how to interpret the summary output with regard to prediction and statistical... View Details
- May 2021
- Article
Fifty Shades of QE: Comparing Findings of Central Bankers and Academics
By: Brian Fabo, Marina Jančoková, Elisabeth Kempf and Ľuboš Pástor
We compare the findings of central bank researchers and academic economists regarding the macroeconomic effects of quantitative easing (QE). We find that central bank papers find QE to be more effective than academic papers do. Central bank papers report larger effects... View Details
- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the... View Details
- Mar 2021
- Conference Presentation
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both... View Details
- 2021
- Working Paper
First Law of Motion: Influencer Video Advertising on TikTok
By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging... View Details
- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and... View Details
- February 2021
- Tutorial
T-tests: Theory and Practice
This video provides an introduction to hypothesis testing, sampling, t-tests, and p-values. It provides examples of A/B testing and t-testing to assess whether difference between two groups are statistically significant. This video can be assigned in conjunction with... View Details
- February 2021
- Tutorial
What is AI?
By: Tsedal Neeley
This video explores the elements that constitute artificial intelligence (AI). From its mathematical basis to current advances in AI, this video introduces students to data, tools, and statistical models that make a computer 'intelligent.' Through an explanation of... View Details
- February 2021
- Article
Assessment of Electronic Health Record Use Between U.S. and Non-U.S. Health Systems
By: A Jay Holmgren, Lance Downing, David W. Bates, Tait D. Shanafelt, Arnold Milstein, Christopher Sharp, David Cutler, Robert S. Huckman and Kevin A. Schulman
Importance: Understanding how the electronic health record (EHR) system changes clinician work, productivity, and well-being is critical. Little is known regarding global variation in patterns of use.
Objective: To provide insights into which EHR... View Details
Objective: To provide insights into which EHR... View Details
- January 2021
- Case
The FIRE Savings Calculator
By: Michael Parzen and Paul Hamilton
This case follows Carol Muñoz, a member of the Financial Independence, Retire Early (FIRE) lifestyle movement. At the age of 45, Carol is considering retiring and living off the $1 million she has accumulated. Using Monte Carlo simulation, Carol forecasts the... View Details
- Article
Memory and Representativeness
By: Pedro Bordalo, Katherine Baldiga Coffman, Nicola Gennaioli, Frederik Schwerter and Andrei Shleifer
We explore the idea that judgment by representativeness reflects the workings of episodic memory, especially interference. In a new laboratory experiment on cued recall, participants are shown two groups of images with different distributions of colors. We find that i)... View Details
- Article
Resilience vs. Vulnerability: Psychological Safety and Reporting of Near Misses with Varying Proximity to Harm in Radiation Oncology
By: Palak Kundu, Olivia Jung, Amy C. Edmondson, Nzhde Agazaryan, John Hegde, Michael Steinberg and Ann Raldow
Background
Psychological safety, a shared belief that interpersonal risk taking is safe, is an important determinant of incident reporting. However, how psychological safety affects near-miss reporting is unclear, as near misses contain contrasting cues that... View Details
Psychological safety, a shared belief that interpersonal risk taking is safe, is an important determinant of incident reporting. However, how psychological safety affects near-miss reporting is unclear, as near misses contain contrasting cues that... View Details
- 2020
- Working Paper
An Executive Order Worth $100 Billion: The Impact of an Immigration Ban's Announcement on Fortune 500 Firms' Valuation
By: Dany Bahar, Prithwiraj Choudhury and Britta Glennon
On June 22, 2020, President Trump issued an Executive Order (EO) that suspended new work visas, barring nearly 200,000 foreign workers and their dependents from entering the United States and preventing American companies from hiring skilled immigrants using H-1B or L1... View Details
Keywords: Visa; Foreign Workers; Fortune 500; Immigration; Policy; System Shocks; Business Ventures; Valuation
- 2020
- Working Paper
Targeting for Long-Term Outcomes
By: Jeremy Yang, Dean Eckles, Paramveer Dhillon and Sinan Aral
Decision makers often want to target interventions so as to maximize an outcome that is observed only in the long term. This typically requires delaying decisions until the outcome is observed or relying on simple short-term proxies for the long-term outcome. Here we... View Details
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
- August 2020
- Technical Note
Comparing Two Groups: Sampling and t-Testing
This note describes sampling and t-tests, two fundamental statistical concepts. View Details
- 2021
- Working Paper
Digital Labor Market Inequality and the Decline of IT Exceptionalism
By: Ruiqing Cao and Shane Greenstein
Several decades of expansion in digital communications, web commerce, and online distribution have altered regional IT labor market returns in the United States. IT occupations experienced similar wage growth as STEM occupations involving IT-related work activities,... View Details
- 2021
- Conference Presentation
An Algorithmic Framework for Fairness Elicitation
By: Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton and Zhiwei Steven Wu
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders.... View Details