Estimation - Publications - Faculty & Research - Harvard Business School

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2019
Working Paper

Large-Scale Demand Estimation with Search Data

By: Tomomichi Amano, Andrew Rhodes and Stephan Seiler

In many online markets, traditional methods of demand estimation are difficult to implement because assortments are very large and individual products are sold infrequently. At the same time, data on consumer search (i.e., browsing) behavior are often available and are... View Details

Keywords: High-dimensional Data; Demand Estimation; Consideration Sets; Consumer Search

November 2012
Article

The Variance of Non-Parametric Treatment Effect Estimators in the Presence of Clustering

By: Samuel G. Hanson and Adi Sunderam

Non-parametric estimators of treatment effects are often applied in settings where clustering may be important. We provide a general methodology for consistently estimating the variance of a large class of non-parametric estimators, including the simple matching... View Details

Keywords: Treatment Effects; Matching Estimators; Clustering; Applications and Software; Mathematical Methods

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April 2020
Article

Designs for Estimating the Treatment Effect in Networks with Interference

By: Ravi Jagadeesan, Natesh S. Pillai and Alexander Volfovsky

In this paper, we introduce new, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies, we introduce the notion of considering a treatment... View Details

Keywords: Experimental Design; Network Inference; Neyman Estimator; Symmetric Interference Model; Homophily

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Research Summary

An Unlimited Moments GMM Estimator

A short time series relative to the number of moment conditions in a GMM framework yields an inconsistent estimator. To circumvent this problem, researchers generally restrict the number of moment conditions to some fraction of the length of the time... View Details

1999
Other Unpublished Work

Estimating Industry Multiples

By: Malcolm Baker and R. S. Ruback

We analyze industry multiples for the S&P 500 in 1995. We use Gibbs sampling to estimate simultaneously the error specification and small sample minimum variance multiples for 22 industries. In addition, we consider the performance of four common multiples: the simple... View Details

Keywords: Management Analysis, Tools, and Techniques; Performance; Mathematical Methods

September 2008 (Revised September 2010)
Exercise

Exercise on Estimation

By: Jason Riis and John T. Gourville

This exercise is meant to assess students' level of confidence around everyday business and general knowledge questions, for the purpose of identifying where they are overconfident and underconfident. View Details

Keywords: Decision Making; Forecasting and Prediction; Personal Characteristics

October 2023
Case

Prime Coalition: Estimating Climate Impact

By: Natalia Rigol, Benjamin N. Roth, Brian Trelstad and Amram Migdal

A case on CRANE, a tool to help investors and green technology companies estimate the future climate impact of new technologies and products, called emissions reduction potential (ERP). The case includes material on CRANE’s methodology for estimating future carbon... View Details

Keywords: Carbon Emissions; Environmental Accounting; Analysis; Climate Change; Green Technology; Innovation and Invention; Measurement and Metrics; Philanthropy and Charitable Giving; Risk and Uncertainty; Nonprofit Organizations; Social Enterprise

Article

Consistent Covariance Matrix Estimation with Cross-Sectional Dependence and Heteroskedasticity in Cross-Sectional Financial Data

By: K. A. Froot

Keywords: Econometrics; Panel Estimation; Autocorrelation; Heteroskedasticity; Mathematical Methods; Economics

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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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November 2007
Background Note

Bayesian Estimation & Black-Litterman

By: Joshua D. Coval and Erik Stafford

Describes a practical method for asset allocation that is more robust to estimation errors than the traditional implementation of mean-variance optimization with sample means and covariances. The Bayesian inspired Black-Litterman model is described after introducing... View Details

Keywords: Asset Management; Investment Portfolio; Mathematical Methods

January 2021 (Revised March 2021)
Supplement

E-Commerce Analytics for CPG Firms (A): Estimating Sales

By: Ayelet Israeli and Fedor (Ted) Lisitsyn

The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for... View Details

Fast Answer

Companies: earnings estimates

Where can I find earnings estimates for companies? First Call Real Time Earnings Estimates has current and historical data. For Current Overview – All Earnings: At top of screen, look up desired... View Details

February 2025
Article

Estimating Models of Supply and Demand: Instruments and Covariance Restrictions

By: Alexander MacKay and Nathan H. Miller

We consider the identification of empirical models of supply and demand with imperfect competition. We show that a restriction on the covariance between unobserved demand and cost shocks can resolve endogeneity and identify the price parameter. We demonstrate how to... View Details

Keywords: Demand Estimation; Identification; Endogeneity Bias; Covariance Restrictions; Ordinary Least Squares; Instrumental Variables; Price; Demand and Consumers; Competition

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2024
Working Paper

What Drives Variation in Investor Portfolios? Estimating the Roles of Beliefs and Risk Preferences

By: Mark Egan, Alexander MacKay and Hanbin Yang

We present an empirical model of portfolio choice that allows for the nonparametric estimation of investors' (subjective) expectations and risk preferences. Utilizing a comprehensive dataset of 401(k) plans from 2009 through 2019, we explore heterogeneity in asset... View Details

Keywords: Stock Market Expectations; Demand Estimation; Retirement Planning; Defined Contribution Retirement Plan; 401 (K); Finance; Investment Portfolio; Investment; Retirement; Behavioral Finance; Financial Services Industry; United States

May 2014
Article

Bias in Reduced-form Estimates of Pass-through

By: Alexander MacKay, Nathan H. Miller, Marc Remer and Gloria Sheu

We show that, in general, consistent estimates of cost pass-through are not obtained from reduced-form regressions of price on cost. We derive a formal approximation for the bias that arises even under standard orthogonality conditions. We provide guidance on the... View Details

Keywords: Pass-through; Reduced-form Aggression; Bias

April–June 2022
Other Article

Commentary on 'Causal Decision Making and Causal Eﬀect Estimation Are Not the Same... and Why It Matters'

By: Edward McFowland III

There has been a substantial discussion in various methodological and applied literatures around causal inference; especially in the use of machine learning and statistical models to understand heterogeneity in treatment effects and to make optimal decision... View Details

Keywords: Causal Inference; Treatment Effect Estimation; Treatment Assignment Policy; Human-in-the-loop; Decision Making; Fairness

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July 2022
Supplement

Solution for E-Commerce Analytics for CPG Firms (A): Estimating Sales

By: Ayelet Israeli

April 2016
Technical Note

Estimating the Equity Risk Premium

By: Samuel Hanson, Robin Greenwood and David Biery

September 2004
Article

Estimating the Market Risk Premium

By: E. Scott Mayfield

Keywords: Markets; Risk and Uncertainty

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July 1993
Article

Simultaneous Estimation of Cost Drivers

By: S. Datar, S. Kekre, T. Mukhopadhyay and K. Srinivasan

Keywords: Cost

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