Publications

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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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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

SSRN

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

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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

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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

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

SSRN

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

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2008
Chapter

Allocating Marketing Resources

By: Sunil Gupta and Thomas J. Steenburgh

Companies spend billions of dollars on marketing every year because it is essential to organic growth. Given these large investments, marketing managers have the responsibility to optimally allocate resources and to demonstrate that their investments generate... View Details

Keywords: Investment Return; Resource Allocation; Marketing; Demand and Consumers; Mathematical Methods

2008
Working Paper

Allocating Marketing Resources

By: Sunil Gupta and Thomas J. Steenburgh

Marketing is essential for the organic growth of a company. Not surprisingly, firms spend billions of dollars on marketing. Given these large investments, marketing managers have the responsibility to optimally allocate these resources and demonstrate that these... View Details

Keywords: Investment Return; Resource Allocation; Marketing; Demand and Consumers; Mathematical Methods

February 2005
Article

An Econometric Analysis of Inventory Turnover Performance in Retail Services

By: Vishal Gaur, Marshall L. Fisher and Ananth Raman

Keywords: Mathematical Methods; Assets; Performance; Sales; Retail Industry

1980
Working Paper

Components of Manufacturing Inventories: A Structural Model of the Production Process

By: Alan J. Auerbach and Jerry R. Green

This paper presents a structural model of production and inventory accumulation based on the hypothesis of cost minimization. It differs from previous attempts in several respects. First, it integrates the analysis of input inventories with output inventories, treating... View Details