By Lars Peter Hansen
The normal thought of determination making below uncertainty advises the choice maker to shape a statistical version linking results to judgements after which to settle on the optimum distribution of results. This assumes that the choice maker trusts the version thoroughly. yet what may still a call maker do if the version can't be relied on? Lars Hansen and Thomas Sargent, top macroeconomists, push the sector ahead as they set approximately answering this query. They adapt strong keep watch over options and practice them to economics. through the use of this idea to enable determination makers recognize misspecification in monetary modeling, the authors boost functions to various difficulties in dynamic macroeconomics. Technical, rigorous, and self-contained, this ebook can be invaluable for macroeconomists who search to enhance the robustness of decision-making approaches.
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The different filters that emerge from chapters 17 and 18 illustrate how robust decision rules are ‘context specific’ in the sense that they depend on the common objective function in the twoplayer zero-sum game that is used to induce a robust decision rule. This theme will run through this book. Chapter 19 concludes by confronting some of the confining aspects of our work, some criticisms that we have heard, and opportunities for further progress. 41 We originally found this problem by stating and solving a conjugate problem of a kind familiar to economists through duality theory.
See chapter 14). It is natural to doubt whether decision makers would fully trust their statistical models after such policy changes. 12. Why not learn the correct specification? For much of this book, but not all, we attribute an enduring fear of misspecification to our decision maker. Wouldn’t it be more realistic to assume that the decision maker learns to detect and discard bad specifications as data accrue? 9. 1 so that, even with nondogmatic priors, it would take long time series to distinguish among the alternative specifications about which the decision maker is concerned.
We agree that this “model averaging” approach is a good way to address some wellstructured forms of model uncertainty. Indeed, in chapter 18 we shall use model averaging and Bayesian updating when we study problems that call for combined estimation and control. But the set of alternative models can be so vast that it is beyond the capacity of a decision maker to conjure up a unique well behaved prior. And even when he can, a decision maker might also want decisions to be robust to whatever prior he could imagine over this set of models.
