This dataset example introduces researchers to estimating cross-correlations between two time series variables. A cross-correlation examines the correlation between two time series variables contemporaneously and at various lagged values. Cross-correlations help researchers understand if two variables are related to each other and, if so, whether movement in one variable tends to precede or follow movement in the other. This example uses a subset of data from the United States Department of Agriculture (USDA) Database. It examines the cross-correlation between the average annual prices per bushel for barley and oats in the United States from 1876 to 2015. Understanding whether prices for two grains are correlated and, if so, whether one price leads or follows the other could help policy makers, farmers, and economists make better forecasts of future agricultural prices. The sample dataset used for this example has been cleaned and organized to make this example easier to follow. Interested readers should read the full documentation for the dataset before using it for research (https://www.ers.usda.gov/data-products/feed-grains-database.aspx).Direct Prerequisites: Time Series ACFs and PACFs

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