Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
  • Research
    • Research
    • Publications
    • Global Research Centers
    • Case Development
    • Initiatives & Projects
    • Research Services
    • Seminars & Conferences
    →
  • Publications→

Publications

Publications

Filter Results : (42) Arrow Down
Filter Results : (42) Arrow Down Arrow Up

Show Results For

  • All HBS Web  (156)
    • Faculty Publications  (42)

    Show Results For

    • All HBS Web  (156)
      • Faculty Publications  (42)

      Econometrics Remove Econometrics →

      Page 1 of 42 Results →

      Are you looking for?

      → Search All HBS Web
      • Article

      How Much Should We Trust Staggered Difference-In-Differences Estimates?

      By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
      Difference-in-differences analysis with staggered treatment timing is frequently used to assess the impact of policy changes on corporate outcomes in academic research. However, recent advances in econometric theory show that such designs are likely to be biased in the...  View Details
      Keywords: Difference In Differences; Staggered Difference-in-differences Designs; Generalized Difference-in-differences; Dynamic Treatment Effects; Mathematical Methods
      Citation
      SSRN
      Find at Harvard
      Related
      Baker, Andrew C., David F. Larcker, and Charles C.Y. Wang. "How Much Should We Trust Staggered Difference-In-Differences Estimates?" Journal of Financial Economics 144, no. 2 (May 2022): 370–395. (Editor's Choice, May 2022.)
      • 2021
      • Working Paper

      How Much Should We Trust Staggered Difference-In-Differences Estimates?

      By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
      Difference-in-differences analysis with staggered treatment timing is frequently used to assess the impact of policy changes on corporate outcomes in academic research. However, recent advances in econometric theory show that such designs are likely to be biased in the...  View Details
      Keywords: Difference In Differences; Staggered Difference-in-differences Designs; Generalized Difference-in-differences; Dynamic Treatment Effects; Mathematical Methods
      Citation
      SSRN
      Read Now
      Related
      Baker, Andrew C., David F. Larcker, and Charles C.Y. Wang. "How Much Should We Trust Staggered Difference-In-Differences Estimates?" European Corporate Governance Institute Finance Working Paper, No. 736/2021, February 2021. (Harvard Business School Working Paper, No. 21-112, April 2021.)
      • 2021
      • Working Paper

      Crisis Interventions in Corporate Insolvency

      By: Samuel Antill and Christopher Clayton
      We model the optimal resolution of insolvent firms in general equilibrium. Absent externalities, the optimal corporate-insolvency system encourages lending by letting banks assign liquidations ex-post. We show that a social planner optimally intervenes in such a system...  View Details
      Keywords: Insolvent Firms; Government Intervention; Liquidation; Econometric Models; Insolvency and Bankruptcy; Governance; Policy
      Citation
      SSRN
      Related
      Antill, Samuel, and Christopher Clayton. "Crisis Interventions in Corporate Insolvency." Working Paper, February 2021.
      • 2021
      • Working Paper

      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; Analysis; Theory; Measurement and Metrics; Performance Consistency
      Citation
      Read Now
      Related
      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." Working Paper, 2021. (3rd Round Revision.)
      • January 2021
      • Article

      A Model of Relative Thinking

      By: Benjamin Bushong, Matthew Rabin and Joshua Schwartzstein
      Fixed differences loom smaller when compared to large differences. We propose a model of relative thinking where a person weighs a given change along a consumption dimension by less when it is compared to bigger changes along that dimension. In deterministic settings,...  View Details
      Keywords: Relative Thinking; Econometric Models; Behavior; Cognition and Thinking
      Citation
      Find at Harvard
      Read Now
      Related
      Bushong, Benjamin, Matthew Rabin, and Joshua Schwartzstein. "A Model of Relative Thinking." Review of Economic Studies 88, no. 1 (January 2021): 162–191.
      • 2021
      • Working Paper

      Real Credit Cycles

      By: Pedro Bordalo, Nicola Gennaioli, Andrei Shleifer and Stephen J. Terry
      We incorporate diagnostic expectations, a psychologically founded model of overreaction to news, into a workhorse business cycle model with heterogeneous firms and risky debt. A realistic degree of diagnosticity, estimated from the forecast errors of managers of U.S....  View Details
      Keywords: Econometric Models; Business Cycles; Credit
      Citation
      Read Now
      Related
      Bordalo, Pedro, Nicola Gennaioli, Andrei Shleifer, and Stephen J. Terry. "Real Credit Cycles." NBER Working Paper Series, No. 28416, January 2021.
      • November 2020
      • Article

      Taxation in Matching Markets

      By: Arnaud Dupuy, Alfred Galichon, Sonia Jaffe and Scott Duke Kominers
      We analyze the effects of taxation in two-sided matching markets, i.e., markets in which all agents have heterogeneous preferences over potential partners. In matching markets, taxes can generate inefficiency on the allocative margin by changing who is matched to whom,...  View Details
      Keywords: Matching Markets; Labor Markets; Taxation; Labor; Markets
      Citation
      Find at Harvard
      Read Now
      Related
      Dupuy, Arnaud, Alfred Galichon, Sonia Jaffe, and Scott Duke Kominers. "Taxation in Matching Markets." International Economic Review 61, no. 4 (November 2020): 1591–1634.
      • Fall 2020
      • Article

      Business Credit Programs in the Pandemic Era

      By: Samuel G. Hanson, Jeremy C. Stein, Adi Sunderam and Eric Zwick
      We develop a pair of models that speak to the goals and design of the sort of business-lending and corporate-bond purchase programs that have been introduced by governments in response to the ongoing COVID-19 pandemic. An overarching theme is that, in contrast to the...  View Details
      Keywords: COVID-19; Business Lending; Government Intervention; Econometric Models; Health Pandemics; Credit; Governance; Policy
      Citation
      Read Now
      Related
      Hanson, Samuel G., Jeremy C. Stein, Adi Sunderam, and Eric Zwick. "Business Credit Programs in the Pandemic Era." Brookings Papers on Economic Activity (Fall 2020).
      • August 2020 (Revised September 2020)
      • Technical Note

      Assessing Prediction Accuracy of Machine Learning Models

      By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne
      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
      Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
      Citation
      Educators
      Purchase
      Related
      Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
      • August 2020
      • Technical Note

      Comparing Two Groups: Sampling and t-Testing

      By: Iavor I Bojinov, Chiara Farronato, Yael Grushka-Cockayne, Willy C. Shih and Michael W. Toffel
      This note describes sampling and t-tests, two fundamental statistical concepts.  View Details
      Keywords: Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Analytics and Data Science; Analysis; Surveys; Mathematical Methods
      Citation
      Educators
      Purchase
      Related
      Bojinov, Iavor I., Chiara Farronato, Yael Grushka-Cockayne, Willy C. Shih, and Michael W. Toffel. "Comparing Two Groups: Sampling and t-Testing." Harvard Business School Technical Note 621-044, August 2020.
      • 2020
      • Working Paper

      Optimal Illiquidity

      By: John Beshears, James J. Choi, Christopher Clayton, Christopher Harris, David Laibson and Brigitte C. Madrian
      We calculate the socially optimal level of illiquidity in an economy populated by households with taste shocks and present bias (Amador, Werning, and Angeletos 2006). The government chooses mandatory contributions to respective spending/savings accounts, each with a...  View Details
      Keywords: Illiquidity; Social Security; Econometric Models
      Citation
      Read Now
      Related
      Beshears, John, James J. Choi, Christopher Clayton, Christopher Harris, David Laibson, and Brigitte C. Madrian. "Optimal Illiquidity." NBER Working Paper Series, No. 27459, July 2020.
      • 2020
      • Article

      A Practical Approach to Sales Compensation: What Do We Know Now? What Should We Know in the Future?

      By: Doug J. Chung, Byungyeon Kim and Niladri B. Syam
      Personal selling represents one of the most important elements in the marketing mix, and appropriate management of the sales force is vital to achieving the organization’s objectives. Among the various instruments of sales management, compensation plays a pivotal role...  View Details
      Keywords: Sales Compensation; Sales Management; Sales Strategy; Principal-agent Theory; Structural Econometrics; Field Experiments; Machine Learning; Artificial Intelligence; Salesforce Management; Compensation and Benefits; Motivation and Incentives; AI and Machine Learning
      Citation
      Find at Harvard
      Read Now
      Related
      Chung, Doug J., Byungyeon Kim, and Niladri B. Syam. "A Practical Approach to Sales Compensation: What Do We Know Now? What Should We Know in the Future?" Foundations and Trends® in Marketing 14, no. 1 (2020): 1–52.
      • June 2020
      • Article

      How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections

      By: Maria Ibanez and Michael W. Toffel
      Accuracy and consistency are critical for inspections to be an effective, fair, and useful tool for assessing risks, quality, and suppliers—and for making decisions based on those assessments. We examine how inspector schedules could introduce bias that erodes...  View Details
      Keywords: Assessment; Bias; Inspection; Scheduling; Econometric Analysis; Empirical Research; Regulation; Health; Food; Safety; Quality; Performance Consistency; Governing Rules, Regulations, and Reforms
      Citation
      SSRN
      Find at Harvard
      Read Now
      Related
      Ibanez, Maria, and Michael W. Toffel. "How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections." Management Science 66, no. 6 (June 2020): 2396–2416. (Revised February 2019. Featured in Harvard Business Review, Forbes, Food Safety Magazine, Food Safety News, and KelloggInsight. (2020 MSOM Responsible Research Finalist.))
      • 2020
      • Working Paper

      A General Theory of Identification

      By: Iavor Bojinov and Guillaume Basse
      What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree on a definition in the context of parametric statistical models — roughly, a parameter θ in a model P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is...  View Details
      Keywords: Identification; Econometric Models; Data and Data Sets; Theory
      Citation
      Read Now
      Related
      Bojinov, Iavor, and Guillaume Basse. "A General Theory of Identification." Harvard Business School Working Paper, No. 20-086, February 2020.
      • 2018
      • Chapter

      Competing Interests

      By: Joel Goh
      Book Abstract: The editors, aided by a team of internationally acclaimed experts, have curated this timely volume to help newcomers and seasoned researchers alike to rapidly comprehend a diverse set of thrusts and tools in this rapidly growing cross-disciplinary field....  View Details
      Keywords: Healthcare; Analytics; Health Care and Treatment; Research; Competition
      Citation
      Find at Harvard
      Purchase
      Related
      Goh, Joel. "Competing Interests." Chap. 4 in Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations, edited by Tinglong Dai and Sridhar Tayur, 51–78. John Wiley & Sons, 2018.
      • 2018
      • Working Paper

      Detecting Anomalies: The Relevance and Power of Standard Asset Pricing Tests

      By: Malcolm Baker, Patrick Luo and Ryan Taliaferro
      The two standard approaches for identifying capital market anomalies are cross-sectional coefficient tests, in the spirit of Fama and MacBeth (1973), and time-series intercept tests, in the spirit of Jensen (1968). A new signal can pass the first test, which we label a...  View Details
      Keywords: Investment Management; Anomalies; Portfolio Construction; Transaction Costs; Investment; Management; Asset Pricing; Market Transactions; Cost
      Citation
      Read Now
      Related
      Baker, Malcolm, Patrick Luo, and Ryan Taliaferro. "Detecting Anomalies: The Relevance and Power of Standard Asset Pricing Tests." Working Paper, July 2018.
      • 2018
      • Working Paper

      How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections

      By: Maria Ibanez and Michael W. Toffel
      Many production processes are subject to inspection to ensure they meet quality, safety, and environmental standards imposed by companies and regulators. Inspection accuracy is critical to inspections being a useful input to assessing risks, allocating quality...  View Details
      Keywords: Assessment; Bias; Inspection; Scheduling; Econometric Analysis; Empirical Research; Regulation; Health; Food; Safety; Quality; Performance Consistency; Performance Evaluation; Food and Beverage Industry; Service Industry
      Citation
      SSRN
      Read Now
      Related
      Ibanez, Maria, and Michael W. Toffel. "How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections." Harvard Business School Working Paper, No. 17-090, April 2017. (Revised October 2018. Formerly titled "Assessing the Quality of Quality Assessment: The Role of Scheduling". Featured in Forbes, Food Safety Magazine, and Food Safety News.)
      • Article

      Pricing and Production Flexibility: An Empirical Analysis of the U.S. Automotive Industry

      By: Antonio Moreno and Christian Terwiesch
      We use a detailed data set from the U.S. auto industry spanning from 2002 to 2009 and a variety of econometric methods to characterize the relationship between the availability of production mix flexibility and firms’ use of responsive pricing. We find that production...  View Details
      Keywords: Empirical Operations Management; Flexibility; Pricing; Automotive Industry; Production; Price; Management; Analysis; Auto Industry; United States
      Citation
      Find at Harvard
      Purchase
      Related
      Moreno, Antonio, and Christian Terwiesch. "Pricing and Production Flexibility: An Empirical Analysis of the U.S. Automotive Industry." Manufacturing & Service Operations Management 17, no. 4 (Fall 2015): 428–444.
      • 2020
      • Working Paper

      Should Firms Move Talent from the Geographic Periphery to Hubs? A Strategic Human Capital Perspective

      By: Prithwiraj Choudhury, Victoria Sevcenko and Tarun Khanna
      A longstanding literature holds that firms should hire and move talent from the geographic periphery to hubs as a means to create value from human capital. They do so, however, at the risk of losing the worker to rivals located in the same geographic hub,...  View Details
      Keywords: Geographic Location; Selection and Staffing; Employment; Residency; Technology Industry; India
      Citation
      Read Now
      Related
      Choudhury, Prithwiraj, Victoria Sevcenko, and Tarun Khanna. "Should Firms Move Talent from the Geographic Periphery to Hubs? A Strategic Human Capital Perspective." Harvard Business School Working Paper, No. 14-080, February 2014. (Revised August 2020.)
      • 2014
      • Chapter

      Schumpeter's Plea: Historical Reasoning in Entrepreneurial Theory and Research

      By: G. Jones and R. Daniel Wadhwani
      This chapter draws on theories of entrepreneurship and history to explore the ways in which historical processes play an integral role in entrepreneurship. It builds off the plea by Joseph Schumpeter for an active exchange between historical approaches and theories of...  View Details
      Keywords: Entrepreneurs; Business History; Entrepreneurship; History; Organizations
      Citation
      Find at Harvard
      Related
      Jones, G., and R. Daniel Wadhwani. "Schumpeter's Plea: Historical Reasoning in Entrepreneurial Theory and Research." Chap. 8 in Organizations in Time: History, Theory, Methods, edited by Marcelo Bucheli and R. Daniel Wadhwani, 192–216. New York: Oxford University Press, 2014.
      • 1
      • 2
      • 3
      • →

      Are you looking for?

      → Search All HBS Web
      ǁ
      Campus Map
      Harvard Business School
      Soldiers Field
      Boston, MA 02163
      →Map & Directions
      →More Contact Information
      • Make a Gift
      • Site Map
      • Jobs
      • Harvard University
      • Trademarks
      • Policies
      • Digital Accessibility
      Copyright © President & Fellows of Harvard College