Statistical inference is the process of drawing conclusions from samples of statistical data about things not fully described or recorded in those samples. During the 1920s, economists in the United States articulated a general approach to statistical inference that downplayed the value of the inferential measures derived from probability theory that later came to be central to the idea of statistical inference in economics. This approach is illustrated by the practices of economists of the Bureau of Economic Analysis of the US Department of Agriculture, who regularly analyzed statistical samples to forecast supplies of various agricultural products. Forecasting represents an interesting case for studying the development of inferential methods, as analysts receive regular feedback on the effectiveness of their inferences when forecasts are compared with actual events.
Statistical Inference in Economics in the 1920s and 1930s: The Crop and Livestock Forecasts of the US Department of Agriculture
Jeff Biddle; Statistical Inference in Economics in the 1920s and 1930s: The Crop and Livestock Forecasts of the US Department of Agriculture. History of Political Economy 1 December 2021; 53 (S1): 53–80. doi: https://doi.org/10.1215/00182702-9414775
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