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It is a pleasure to participate in the discussion on Automated Inference and the Future of Econometrics at the 20th anniversary mark of Econometric Theory . Many factors appear to be fueling the growth of computer-intensive inferential rules; some of these lie with theoretical advances and with the current and predictable increases in computing power and in availability of large data sets.
Among the many possible aspects, I wish to comment on three issues related to this theme, with a view toward possible developments. These comments are not intended to highlight some published work of mine, and they are mostly nontechnical; I hope none of these features will be seen as a liability by the reader.
The three issues, listed in increasing degree of importance, concern the distinction between automation and computer aided decisions (Section 1), model selection (Section 2), and learning (Section 3). Conclusions are reported in Section 4.
1. AUTOMATION AND COMPUTER AIDED DECISIONS
The first aspect is wording. I perceive automated inference (AI) as associated with artificial intelligence and expert systems. Many computing-intensive procedures are instead of a different nature, which I would classify as tools for computer aided decisions (CAD), to be defined subsequently. This section argues that the two concepts are different and that they may be fruitfully applied in different situations. In particular AI is best suited for industrial applications, CAD for scientific communication.
The phrase "automated inference" conveys the idea that control over inference is left to a computer program or, more precisely, that the econometrician does not have complete control over inference. This concept is associated with artificial intelligence and expert systems because whatever inferential rule is being applied, it is used without the direct control of the econometrician. In the following I will use the (common) acronym AI for both phrases.
Examples of AI are "black-box" procedures. Many black boxes have been entertained in econometrics, especially in the past. They include, e.g., automatic univariate autoregressive moving average (ARMA) modeling, i.e., completely human-unaided computer software that takes in data and that produces predictions using an ARMA model, selected via a set of inference rules. More recent references include some applications of Bayesian vector autoregressions with Minnesota priors, which are close to being black boxes.
Automated procedures of...