About

Dr. Todd Kuethe is the Shrader Chair in Farmland Economics at Purdue University within the Department of Agricultural Economics. His research focuses on farmland markets, land values, and the economic dynamics of farmland, contributing to the understanding of farmland market trends and valuation. Dr. Kuethe is actively involved in farm policy analysis and farmland market updates, providing insights into land market conditions and their implications for agricultural economics.

Research topics

• Economics
• Finance
• Business
• Agricultural economics
• Econometrics
• Macroeconomics
• Monetary economics
• Statistics
• Mathematics
• Financial system
• Geography

Selected publications

• Overreactions and Underreactions in USDA Forecasts

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• The Impact of Supply and Demand Changes on Non‐Real‐Estate Agricultural Loans

  Applied Economic Perspectives and Policy · 2025-12-30

  articleOpen access

  ABSTRACT This study examines the degree to which changes in non‐real‐estate agricultural loans at commercial banks are driven by changes in supply or demand. Our identification strategy exploits information provided by agricultural lending surveys conducted by three Federal Reserve Banks: Chicago, Kansas City, and Minneapolis. Building on recent studies of loan officer opinion surveys, we estimate the changes in agricultural loan supply and demand using an unbalanced panel of 1028 banks across the 2002–2021 period. The survey responses provide instruments for supply and demand changes to examine fluctuations in bank‐level agricultural loan volumes obtained from Federal Financial Institutions Examinations Council quarterly “call reports.” We find that changes in the volume of non‐real‐estate farm loans at commercial banks are principally driven by changes in excess loan demand. These findings support a careful approach for policies aimed at boosting supply of agricultural credit. JEL Classification: Q14, G21, Q00

  PublisherOA PDFDOI

• The Loss Function of USDA Forecasters: Evidence from WASDE Animal Product Price Forecasts

  Journal of Agricultural and Applied Economics · 2025-12-23 · 1 citations

  articleOpen accessSenior author

  Abstract The loss function is a mathematical representation of the costs experienced by a forecaster when observed outcomes differ from what was predicted. Prior studies suggest that USDA forecasts are not optimal based on an assumed mean-zero quadratic loss function. This study proposes an alternative view of forecast evaluation, which assumes all USDA forecasts are produced to minimize the forecasters’ costs, and searches for the dimensions of the loss function under which optimality holds. We illustrate the degree to which USDA loss functions vary across a series of WASDE price forecasts. A better understanding of USDA forecasters’ costs will benefit forecasters and forecast users.

  PublisherOA PDFDOI

• A Time Series Analysis of Herd Investor Behavior Using Online and Social Media Data

  SAGE Open · 2025-07-01 · 1 citations

  articleOpen access

  We examine the relationship between market performance of leading cryptocurrencies (Bitcoin and Ethereum), meme-stocks (AMC, GameStop), and subjects of corporate boycotts (Bud Light) using weekly market price and volume data along with social media data of weekly mentions (which total 337 million in this dataset) and net sentiment. Using vector autoregression (VAR) time series analysis along with Granger causality testing and structural breaks, we successfully predict trade volume of these various assets using social media data and price data. We also find that closing price data and trade volume are reliable predictors of net sentiment about crypto in online and social media. However, we struggle to predict the closing price for the group of assets studied. We also employ impulse response functions, finding evidence of a dynamic relationship occurring between online and social media net sentiment and online media volume with closing price and trade volume. These functions show that investor sentiment operates with a short memory lasting around 3 weeks, additionally these functions show that price generates a shock on trade volume but that crypto and meme-stock markets experience this differently. Our findings reinforce the notion that meme-stock traders and herd investors do not trade on market fundamentals but are instead sensitive to herding (or sentiment) movements. Our findings also suggest that compared to these meme-stock investors, crypto markets have more traditional motivations of loss aversion.

  PublisherDOI

• Pricing derivatives in agricultural land markets

  Agricultural Finance Review · 2025-03-21

  article

  Purpose The purpose of this paper is to discuss the potential of land derivatives in light of an increased engagement of non-agricultural investors. These financial instruments allow participation in the market without physically acquiring land and further contribute to increased market liquidity. One prerequisite for their establishment is the availability of a transparent pricing framework. This paper aims to identify such a framework for derivatives on a farmland index. Design/methodology/approach The study reviews the current state of farmland derivatives and discusses different alternatives in constructing appropriate farmland indices. It provides an overview on pricing methods for real estate derivatives and applies a risk-neutral valuation approach to the NCREIF Farmland Index. Findings Derivative prices are sensitive to the market price of risk. The pricing of traded products is hampered by missing information to determine the market price of risk. This limits the practical implementation of land derivatives in the near future. Originality/value Despite few opportunities for trading land-based derivatives based on funds, this line of research has so far been limited by the lack of an adequate pricing approach. This paper adds to the sparse empirical literature on derivatives in land markets by identifying and applying a pricing framework.

  PublisherDOI

• Evaluating USDA’s farm balance sheet forecasts

  Agricultural Finance Review · 2024-06-21

  articleSenior author

  Purpose The United States Department of Agriculture Farm Balance Sheet forecasts provide important, timely information on the financial assets and debt in the U.S. farm sector. Despite their prominent role in policy and decision making, the forecasts have not been rigorously evaluated. This research examines the degree to which the USDA’s Farm Balance Forecasts are optimal predictors of subsequent official estimates. Design/methodology/approach Following prior studies of USDA’s farm income forecasts, archived asset and debt forecasts from 1986 through 2021 are used in regression-based tests of bias and efficiency. Findings Forecasts from 1986–2021 are found to be unbiased but inefficient. The forecasts have a tendency to over-react to new information early in the revision process. Originality/value These findings can be helpful for forecast users in adjusting their expectations and for forecasters in adjusting the current forecasting methods.

  PublisherDOI

• Experimental evidence of bargaining power in agricultural land markets

  European Review of Agricultural Economics · 2024-12-01 · 3 citations

  articleOpen access

  Abstract There is public concern about the degree to which rising farmland rental rates are driven by the perceived market influence of non-agricultural actors. We conduct a structural estimation to analyse the potential bargaining power of different types of actors in the farmland market. It allows us to infer their latent reservation utilities by exploiting equilibrium conditions, derived from a stochastic ultimatum game. Reservation utilities reflect outside options in negotiations, as they are determinants of bargaining power. We conduct economic experiments in the rental market. Our findings show that farmers and local actors have more bargaining power than non-farmers and absentee actors, respectively.

  PublisherOA PDFDOI

• Value of Farm Data in Farmland Rental Markets

  Land Economics · 2024-03-25 · 1 citations

  article

  Precision farming data enhance agricultural productivity by informing site-specific resource management. These benefits may be capitalized into the underlying value of the farmland, raising rental rates. This article uses a stated preference choice experiment to estimate farmers’ willingness to pay for farm data in farmland rental markets. Farmers are willing to pay a small premium to acquire data accrued by previous operators, depending on the field type and quality information provided by the landowner and farmers’ use of precision agriculture technology. We find evidence that farm data confer both a “management value” and a “signaling value” to prospective tenants.

  PublisherDOI

• An evaluation of Congressional Budget Office's baseline projections of <scp>USDA</scp> mandatory farm and nutrition programs

  Applied Economic Perspectives and Policy · 2024-06-25

  articleOpen accessSenior author

  Abstract The Congressional Budget Office (CBO) projections of USDA's mandatory farm and nutrition program outlays are important in shaping US agricultural policy. Using CBO projections and observed outcomes from 1985 through 2020, we examine the degree to which projections of farm, supplemental nutrition assistance program (SNAP), and child nutrition program outlays are unbiased, efficient, and informative. We find that projections for farm and child nutrition program outlays are unbiased, SNAP outlays are unbiased at short‐term but are downward biased beyond a 3‐year horizon. All three series of projections are inefficient. SNAP and child nutrition program outlay projections are informative up to a 5‐year horizon, but the farm program outlay projections are informative for only a 1‐year horizon. Disaggregated farm program data since 2008 suggests that the uninformativeness principally stems from conservation and commodity program projections. The findings may be valuable to CBO, as they continue to improve projections, and to projection users, in adjusting their expectations.

  PublisherOA PDFDOI

• Information Rigidities and Farmland Value Expectations

  Land Economics · 2024-11-30

  articleOpen access

  <h3>Abstract</h3> This study examines the degree to which information inefficiency influences farmland price expectations. Using expectations and observed values of Iowa farmland from 1964 to 2021 and the empirical test of Coibion and Gorodnichenko (2015), this study estimates the degree to which information rigidities hold explanatory power for information inefficiency. Our results suggest Iowa farmland professionals infrequently update their information set or underweight new information. This study provides a necessary step toward a better understanding of the role of information in farmland market efficiency, furthering the discussion of development of additional public information in farmland markets.

  PublisherOA PDFDOI

Frequent coauthors

• Jennifer Ifft

  Kansas State University

  41 shared

• Todd Hubbs

  Economic Research Service

  20 shared

• Mitch Morehart

  13 shared

• Jonathan Coppess

  University of Illinois Urbana-Champaign

  12 shared

• Ani L. Katchova

  The Ohio State University

  8 shared

• David B. Oppedahl

  Federal Reserve Bank of Chicago

  7 shared

• Mitchell J. Morehart

  7 shared

• Chad Fiechter

  Agricultural & Applied Economics Association

  7 shared

Education

• PhD, Agricultural Economics

  Purdue University

  2009

• MS, Agribusiness Economics

  Southern Illinois University

  2005

• BS, Economics

  Saint Louis University

  2003

Awards & honors

• James C. Snyder Memorial Lecture