Determining Returns to Storage: USDA Data versus Micro Level Data

USDA data are commonly used to determine producers' returns to storage. Aggregating data may result in a loss of information, leading to underestimated returns. This study compares USDA and elevator data from Oklahoma to determine how much USDA data underestimates returns. Results indicate USDA data only slightly underestimate returns to storage.Research Methods/ Statistical Methods,

The Preference for Round Number Prices

This study determines if a preference for round prices exists in the wheat market and how wheat sales react to price movements around whole dollar amounts. The results show round prices are slightly more prevalent than non-round prices and that transactions increase when price moves above a whole dollar amount.Demand and Price Analysis,

The Preference for Round Number Prices

This study determines if a preference for round prices exists in the wheat market and how wheat sales react to price movements around whole dollar amounts. The results show round prices are slightly more prevalent than non-round prices and that transactions increase when price moves above a whole dollar amount

Determining Returns to Storage: USDA Data versus Micro Level Data

USDA data are commonly used to determine producers' returns to storage. Aggregating data may result in a loss of information, leading to underestimated returns. This study compares USDA and elevator data from Oklahoma to determine how much USDA data underestimates returns. Results indicate USDA data only slightly underestimate returns to storage

Determining Returns to Storage: Does Data Aggregation Matter

Aggregate data are commonly used to determine returns to storage. However, recent studies have shown that aggregating data may lead to underestimated returns. This article compares aggregate and elevator data from Oklahoma to determine if aggregate data underestimate returns. We find no difference between the mean returns estimated with aggregate data and the mean returns estimated with transaction level data from grain elevators in Oklahoma

The Impact of Marketing Strategy Information on the Producer's Selling Decision

There is no shortage of studies regarding price forecasting and marketing strategies of producers. However, the majority of these studies take a normative approach, focusing on deriving an optimal strategy for producers to follow based on information received from producer surveys. Due to such things as psychological biases, producers may not actually use the marketing information that they say they do. This study uses actual producer transaction data to determine how producers marketing decisions correspond with those recommended by market advisory services and with those that use futures spreads to calculate expected returns. The results show that producers do respond to using futures spreads to represent expected returns to storage. Also, it appears that Oklahoma producers make marketing decisions opposite of those recommended by market advisory services

Market Advisory Service Recommendations and Wheat Producers' Selling Decisions

"This study uses actual producer transaction data to determine how Oklahoma wheat producers' selling decisions compare to recommendations from market advisory services and market incentives as reflected in futures spreads. Results show that producers responded to expected returns to storage as measured by futures spreads. Also, Oklahoma producers make marketing decisions that are either unrelated or the opposite of recommendations from market advisory services". Copyright (c) 2008 Canadian Agricultural Economics Society.

Determining Returns to Storage: Does Data Aggregation Matter

Aggregate data are commonly used to determine returns to storage. However, recent studies have shown that aggregating data may lead to underestimated returns. This article compares aggregate and elevator data from Oklahoma to determine if aggregate data underestimate returns. We find no difference between the mean returns estimated with aggregate data and the mean returns estimated with transaction level data from grain elevators in Oklahoma.aggregate data, data collection, information loss, returns to storage, Agribusiness, Q13,