SHERLOCK HOLMES AND THE SEARCH FOR TRUTH: A DIAGNOSTIC TALE

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Abstract
In four novels and fifty‐six short stories, Sir Arthur Conan Doyle developed the characters of Mr. Sherlock Holmes and his trusted friend and chronicler, Dr. John Watson. The creation of the brilliant sleuth and his partner who, although not possessing genius, stimulated it in his friend, was a masterstroke. The adventures, memoirs, return, last bow, and case book of Sherlock Holmes, as well as the four famous novels, are stories of sheer delight. Econometrics may not have the everlasting charm of Holmesian characters and adventures, or even a famous resident of Baker Street, but there is much in his methodological approach to the solving of criminal cases that is of relevance to applied econometric modelling. Holmesian detection may be interpreted as accommodating the relationship between data and theory, modelling procedures, deductions and inferences, analysis of biases, testing of theories, specification and respecification of theories, re‐evaluation and reformulation of theories, and finally reaching a solution to the problem at hand. With this in mind, can applied econometricians learn anything from the master of detection? This paper provides an outline of Holmesian deduction through the various stages of accommodation, namely problem solving, theorizing before data, examining the quality of data, the meaning of truth, reconciliation with data, and testing of theories. Testing procedures, especially the use of diagnostics, are the most common research method used in econometrics for examining a number of specifications within a modelling cycle of specification, estimation and evaluation. A diagnostic approach to the evaluation of empirical models is outlined through testing the key assumptions which define the parameter space for purposes of inference.

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