Publications

Forthcoming
Article

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; Analytics and Data Science

Research Summary

Overview

Pushing decision authority downward and increasing employee autonomy have become watchwords for the modern organization. Leaders of contemporary organizations view efforts to replace “command and control” systems with less-hierarchical approaches to organizing as... View Details

Keywords: Formalization; Teams; Decentralization; Hierarchy; Organizational Design; Organizational Structure; Self-managing Organizations; Future Of Work; Flat Organization

2023
Working Paper

Estimating Productivity in the Presence of Spillovers: Firm-Level Evidence from the U.S. Production Network

By: Ebehi Iyoha

This paper examines the extent to which productivity gains are transmitted across U.S. firms through buyer-supplier relationships. Many empirical studies measure firm-to-firm spillovers using firm-level productivity estimates derived from control function approaches.... View Details

Keywords: Supply and Industry; Partners and Partnerships; Production

March–April 2014
Article

Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans

By: Doug J. Chung, Thomas Steenburgh and K. Sudhir

We estimate a dynamic structural model of sales force response to a bonus based compensation plan. Substantively, the paper sheds insights on how different elements of the compensation plan enhance productivity. We find evidence that: (1) bonuses enhance productivity... View Details

Keywords: Performance Productivity; Salesforce Management; Compensation and Benefits

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

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Teaching Interest

Marketing Models Doctoral Seminar

This course is a doctoral level course on Quantitative Marketing. We will cover methodological as well as substantive topics this semester. Methodological topics include: Choice models, Entry and Exit models, Dynamic structural models, Bayesian estimation methods... View Details

July 1999
Background Note

Note on Statistical Tests for a Randomized Matched Pair Experimental Design, A

By: Alvin J. Silk

Concerns understanding the conditions under which an experimental design that employs matching and randomization may result in gains in precision as compared to a design that utilizes randomization and independent samples--i.e., no matching. An empirical example is... View Details

Keywords: Marketing; Performance Efficiency; Mathematical Methods

Educators

2019
Working Paper

The Impact of Professionals' Contributions to Online Knowledge Communities on Their Workplace Knowledge Work

By: Hila Lifshitz - Assaf and Frank Nagle

Knowledge work is becoming increasingly challenging as pace of change in the knowledge frontier is increasing. Organizations have created multiple mechanisms to minimize knowledge gaps and increase learning such internal training, mentorship programs as well as... View Details

Keywords: Open Source; Future Of Work; Software Development; Knowledge Work; Online Community; Learning; Knowledge Sharing; Applications and Software; Open Source Distribution; Performance Productivity

Article

It's Not Easy Being Green: The Role of Self-Evaluations in Explaining Support of Environmental Issues

By: Scott Sonenshein, K. A. DeCelles and Jane E. Dutton

Using a mixed methods design, we examine the role of self-evaluations in influencing support for environmental issues. In Study 1—an inductive, qualitative study—we develop theory about how environmental issue supporters evaluate themselves in a mixed fashion,... View Details

Keywords: Social Issues; Environmental Sustainability; Performance Evaluation; Cognition and Thinking

12 Mar 2008
Working Paper Summaries

Allocating Marketing Resources

Keywords: by Sunil Gupta & Thomas J. Steenburgh

2024
Working Paper

Bootstrap Diagnostics for Irregular Estimators

By: Isaiah Andrews and Jesse M. Shapiro

Empirical researchers frequently rely on normal approximations in order to summarize and communicate uncertainty about their findings to their scientific audience. When such approximations are unreliable, they can lead the audience to make misguided decisions. We... View Details

Keywords: Mathematical Methods; Decision Choices and Conditions

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2017
Working Paper

Investment Timing with Costly Search for Financing

By: Samuel Antill

I develop a dynamic model of investment timing in which firms must first choose when to search for external financing. Search is costly and the arrival of investors is uncertain, leading to delay in financing and investment. Depending on parameters, my model can... View Details

Keywords: Real Options; Search And Bargaining; Time-varying Financial Conditions; Investment; Venture Capital; Mathematical Methods

SSRN

Article

Mitigating Bias in Adaptive Data Gathering via Differential Privacy

By: Seth Neel and Aaron Leon Roth

Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details

Keywords: Bandit Algorithms; Bias; Analytics and Data Science; Mathematical Methods; Theory

Research Summary

Incorporating Price and Inventory Endogeneity in Firm-Level Sales Forecasting.

Forecasting firm-level sales is a key activity in top-down planning in most organizations. In the retailing industry, firms can use inventory and price to stimulate demand. Hence, standard time series methods for sales forecasting can be improved by incorporating... View Details

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

Keywords: Missing Data; Bayesian Statistics; Imputation; Categorical Data; Estimation

November–December 2023
Article

Iterative Coordination and Innovation: Prioritizing Value over Novelty

By: Sourobh Ghosh and Andy Wu

An innovating organization faces the challenge of how to prioritize distinct goals of novelty and value, both of which underlie innovation. Popular practitioner frameworks like Agile management suggest that organizations can adopt an iterative approach of frequent... View Details

Keywords: Innovation; Novelty; Goals; Specialization; Coordination; Field Experiment; Software Development; Agile; Scrum; Iteration; Iterative; Organizations; Innovation and Invention; Value; Goals and Objectives; Integration; Applications and Software

30 Dec 2014
Working Paper Summaries

Return on Political Investment in the American Jobs Creation Act of 2004

Keywords: by Hui Chen, Katherine Gunny & Karthik Ramanna

Research Summary

Overview

Professor Huang examines the micro-foundations of entrepreneurship: the individual-level decision-making processes that influence entrepreneurs’ ability to acquire resources that they need, yet lack, especially financial capital. Deploying a variety of methods from... View Details

2023
Article

Post Hoc Explanations of Language Models Can Improve Language Models

By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju

Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance... View Details

Keywords: AI and Machine Learning; Performance Effectiveness