Time series ACF and PACF and the USDA Feed Grains Database (1876-2015) : U.S. oats yield per acre /
This dataset example introduces researchers to plotting an autocorrelation function (ACF) and a partial autocorrelation function (PACF) for a single time series variable. An ACF plots the average correlation between data points in a time series with lagged values of the same series. A PACF computes...
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| Format: | eBook |
| Language: | English |
| Published: |
London :
SAGE Publications, Ltd.,
2017.
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| Online Access: | Connect to the full text of the electronic book |
| Summary: | This dataset example introduces researchers to plotting an autocorrelation function (ACF) and a partial autocorrelation function (PACF) for a single time series variable. An ACF plots the average correlation between data points in a time series with lagged values of the same series. A PACF computes the average partial correlation between data points in a time series with lagged values of the same series, but controlling for the values of shorter lags. ACFs and PACFs help researchers understand the temporal dynamics of an individual time series. This example uses a subset of data from the United States Department of Agriculture (USDA) Database. It examines trends in annual oats yield per acre in bushels from 1876 to 2015. Understanding temporal dynamics in grain yields could help policy makers, farmers, and economists make better forecasts of future yields. 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 (http://www.ers.usda.gov/data-products/feed-grains-database.aspx). |
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| Physical Description: | 1 online resource : illustrations. |
| ISBN: | 9781473995581 1473995582 |