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By Lars Peter Hansen

The normal conception of selection 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 should still a call maker do if the version can't be relied on? Lars Hansen and Thomas Sargent, major macroeconomists, push the sector ahead as they set approximately answering this query. They adapt powerful keep an eye on suggestions and observe them to economics. by utilizing this thought to permit selection makers recognize misspecification in fiscal modeling, the authors enhance purposes to a number of difficulties in dynamic macroeconomics. Technical, rigorous, and self-contained, this publication could be worthwhile for macroeconomists who search to enhance the robustness of decision-making strategies.

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38 Nevertheless, prices of risky assets vary substantially across these pairs. In chapter 14, we revisit some quantitative findings of Tallarini (2000) and reinterpret asset pricing patterns that he imputed to very high risk aversion in terms of a plausible fear of model misspecification. We measure a plausible fear of misspecification by using the detection error probabilities introduced in chapter 9. Chapters 15 and 16 describe two more settings with multiple decision makers and introduce an equilibrium concept that extends rational expectations in what we think is a natural way.

Chapter 7 represents things in the time domain, while chapter 8 works in the frequency domain. Incorporating discounting requires carefully restating the control problems used to induce robust decision rules. Chapters 7 and 8 describe two ways to alter the discounted linear quadratic optimal control problem in a way to induce robust decision rules: (1) to form one of several two-player zero-sum games in which nature chooses from a set of models in a way that makes the decision maker want robust decision rules; and (2) to adjust the continuation value function in the dynamic program in a way that encodes the decision maker’s preference for a robust rule.

1 ) using a time series {yt }Tt=1 of moderate size T , an idea that we develop in chapter 9. 4 ). Such decisions are said to be robust to misspecification of the approximating model. We compute robust decision rules by solving one of several distinct but related two-player zero-sum games: a maximizing decision maker chooses controls {ut } and a minimizing (also known as a “malevolent” or “evil”) agent chooses model distortions {wt+1 } . The games share common players, actions, and payoffs, but assume different timing protocols.

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