{'fraction_non_alphanumeric': 0.0220162224797219, 'fraction_numerical': 0.012746234067207415, 'mean_word_length': 5.4, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Revisiting Aggregate U.S. Meat Demand with a Bayesian Averaging of Classical Estimates Approach: Do We Need a More General Theory?

Although meat demand is one of the most studied issues in agricultural economics, our understanding of this phenomenon has been hampered by valid concerns about model specification uncertainty. This article revisits the need for more general theories of aggregate U.S. meat demand. Using a Bayesian averaging of classical estimates approach, we draw comprehensive inferences over 1,048,576 demand systems. We find very little evidence supporting the need for more general theories that include demand determinants beyond prices and expenditures. We find strong evidence in support of symmetry and negativity, but strong evidence against homogeneity, which is consistent with other research. Copyright 2008, Oxford University Press.