Identifying Assumptions and Research Dynamics

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Tác giả: Andrew Ellis, Ran Spiegler

Ngôn ngữ: eng

Ký hiệu phân loại: 001.4 Research; statistical methods

Thông tin xuất bản: 2024

Mô tả vật lý:

Bộ sưu tập: Báo, Tạp chí

ID: 201823

A representative researcher has repeated opportunities for empirical research. To process findings, she must impose an "identifying assumption." She conducts research when the assumption is sufficiently plausible (taking into account both current beliefs and the quality of the opportunity), and updates beliefs as if the assumption were perfectly valid. We study the dynamics of this learning process. While the rate of research cannot always increase over time, research slowdown is possible. We characterize environments in which the rate is constant. Long-run beliefs can exhibit history-dependence and "false certitude." We apply the model to stylized examples of empirical methodologies: experiments, various causal-inference techniques, and "calibration."
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