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- 2024
- Working Paper
Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference
By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov
Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to... View Details
- August 2023 (Revised March 2024)
- Case
Arla Foods: Data-Driven Decarbonization (A)
By: Michael Parzen, Michael W. Toffel, Susan Pinckney and Amram Migdal
The case describes Arla’s history, in particular its climate change mitigation efforts, and how it implemented a price incentive system to motivate individual farms to implement scope 1 greenhouse gas emissions mitigation measures and receive a higher milk price. The... View Details
Keywords: Dairy Industry; Business Earnings; Agribusiness; Animal-Based Agribusiness; Acquisition; Mergers and Acquisitions; Decision Making; Decisions; Voting; Environmental Management; Climate Change; Environmental Regulation; Environmental Sustainability; Green Technology; Pollution; Moral Sensibility; Values and Beliefs; Financial Strategy; Price; Profit; Revenue; Food; Geopolitical Units; Global Strategy; Ownership Type; Cooperative Ownership; Performance Efficiency; Performance Evaluation; Problems and Challenges; Natural Environment; Science-Based Business; Business Strategy; Commercialization; Cooperation; Corporate Strategy; Food and Beverage Industry; Agriculture and Agribusiness Industry; Europe; United Kingdom; European Union; Germany; Denmark; Sweden; Luxembourg; Belgium
- August 2023 (Revised January 2024)
- Supplement
Arla Foods: Data-Driven Decarbonization (B)
By: Michael Parzen, Michael W. Toffel, Susan Pinckney and Amram Migdal
The case describes Arla’s history, in particular its climate change mitigation efforts, and how it implemented a price incentive system to motivate individual farms to implement scope 1 greenhouse gas emissions mitigation measures and receive a higher milk price. The... View Details
Keywords: Dairy Industry; Earnings Management; Environmental Accounting; Animal-Based Agribusiness; Mergers and Acquisitions; Decisions; Voting; Climate Change; Environmental Regulation; Environmental Sustainability; Green Technology; Pollution; Moral Sensibility; Values and Beliefs; Financial Strategy; Price; Profit; Revenue; Food; Geopolitical Units; Cross-Cultural and Cross-Border Issues; Global Strategy; Cooperative Ownership; Performance Efficiency; Performance Evaluation; Problems and Challenges; Natural Environment; Science-Based Business; Business Strategy; Commercial Banking; Cooperation; Corporate Strategy; Motivation and Incentives; Food and Beverage Industry; Agriculture and Agribusiness Industry; Europe; United Kingdom; European Union; Denmark; Sweden; Luxembourg; Belgium
- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market... View Details
- 2022
- Working Paper
Slowly Varying Regression under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details
Keywords: Mathematical Methods
- 2022
- Article
Nonparametric Subset Scanning for Detection of Heteroscedasticity
By: Charles R. Doss and Edward McFowland III
We propose Heteroscedastic Subset Scan (HSS), a novel method for identifying covariates that are responsible for violations of the homoscedasticity assumption in regression settings. Viewing the problem as one of anomalous pattern detection, we use subset scanning... View Details
- 2022
- Working Paper
Machine Learning Models for Prediction of Scope 3 Carbon Emissions
By: George Serafeim and Gladys Vélez Caicedo
For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15... View Details
- March 2022 (Revised July 2022)
- Technical Note
Linear Regression
This note provides an overview of linear regression for an introductory data science course. It begins with a discussion of correlation, and explains why correlation does not necessarily imply causation. The note then describes the method of least squares, and how to... View Details
- March 2022 (Revised July 2022)
- Technical Note
Prediction & Machine Learning
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional... View Details
- March 2022
- Article
Assessing the Impact of the COVID-19 Pandemic on Clinician Ambulatory Electronic Health Record Use
By: A Jay Holmgren, Lance Downing, Mitchell Tang, Christopher Sharp, Christopher Longhurst and Robert S. Huckman
Objective: The COVID-19 pandemic changed clinician electronic health record (EHR) work in a multitude of ways. To evaluate how, we measure ambulatory clinician EHR use in the United States throughout the COVID-19 pandemic.
Materials and Methods: We use EHR... View Details
Materials and Methods: We use EHR... View Details
- February 2022
- Article
Sugar-sweetened Beverage Purchases and Intake at Event Arenas with and without a Portion Size Cap
By: Sheri Volger, James Scott Parrott, Brian Elbel, Leslie K. John, Jason P. Block, Pamela Rothpletz-Puglia and Christina A. Roberto
This is the first real-world study to examine the association between a voluntary 16-ounce (oz.) portion-size cap on sugar-sweetened beverages (SSB) at a sporting arena on volume of SSBs and food calories purchased and consumed during basketball games. Cross-sectional... View Details
- July 2021
- Article
Multinationality and Capital Structure Dynamics: A Corporate Governance Explanation
By: Daniel Gyimah, Nana Abena Kwansa, Anthony K. Kyiu and Anywhere Sikochi
This paper examines the impact of corporate governance on capital structure dynamics. Using ordinary least squares regressions on 17,496 firm-year observations for 2,294 U.S. multinational companies (MNCs) over the period 1990–2018, we find that MNCs with strong... 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
Choice Architecture in Physician–patient Communication: A Mixed-methods Assessment of Physicians' Competency
By: J. Hart, K. Yadav, S. Szymanski, A. Summer, A. Tannenbaum, J. Zlatev, D. Daniels and S.D. Halpern
Background: Clinicians’ use of choice architecture, or how they present options, systematically influences the choices made by patients and their surrogate decision makers. However, clinicians may incompletely understand this influence.... View Details
- January 2021
- Article
Machine Learning for Pattern Discovery in Management Research
By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect... View Details
- May 2020
- Article
Scalable Holistic Linear Regression
By: Dimitris Bertsimas and Michael Lingzhi Li
We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting... View Details
- 2020
- Working Paper
Cutting the Gordian Knot of Employee Health Care Benefits and Costs: A Corporate Model Built on Employee Choice
By: Regina E. Herzlinger and Barak D. Richman
The U.S. employer-based health insurance tax exclusion created a system of employer-sponsored insurance (ESI) with limited insurance choices and transparency that may lock employed households into health plans that are costlier or different from those they prefer to... View Details
- October 2018
- Article
A Theory of Intergenerational Mobility
By: Gary Becker, Scott Duke Kominers, Kevin Murphy and Jorg L. Spenkuch
We develop a model of intergenerational resource transmission that emphasizes the link between cross-sectional inequality and intergenerational mobility. By drawing on first principles of human capital theory, we derive several novel results. In particular, we show... View Details
- Article
Applying Random Coefficient Models to Strategy Research: Identifying and Exploring Firm Heterogeneous Effects
By: Juan Alcácer, Wilbur Chung, Ashton Hawk and Gonçalo Pacheco-de-Almeida
Strategy aims at understanding the differential effects of firms’ actions on performance. However, standard regression models estimate only the average effects of these actions across firms. Our paper discusses how random coefficient models (RCMs) may generate new... View Details
- 2018
- Working Paper
Opportunistic Returns and Dynamic Pricing: Empirical Evidence from Online Retailing in Emerging Markets
By: Chaithanya Bandi, Antonio Moreno, Donald Ngwe and Zhiji Xu
We investigate how dynamic pricing can lead to more product returns in the online retail industry. Using detailed sales data of more than two million transactions from the Indian online retail market, where price promotions are very common, we document two types of... View Details