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Using the standard real business cycle model with lump-sum taxes, we analyze the impact of fiscal policy when agents form expectations using adaptive learning rather than rational expectations (RE). The output multipliers for government purchases are significantly higher under learning, and fall within empirical bounds reported in the literature, which is in sharp contrast to the implausibly low values under RE. Positive effects of fiscal policy are demonstrated during times of economic stress like the recent Great Recession. Finally, it is shown how learning can lead to consumption and investment dynamics empirically documented during some episodes of “fiscal consolidations.”

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the ~198 000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726 800 ± 34 000 km2. The uncertainty, about ±5%, is derived from careful single-glacier and basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliably is an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.

There were four 1.5-hour sessions of Division I business meetings during the XXVIIth IAU General Assembly. The first three were devoted to the reports of Commissions, Working Groups and services associated with the Division, discussion about plans for the next triennium and future structure of the Division. Scientific presentations on the future space astrometric mission Gaia were made at the fourth session.

We study how the use of judgment or “add-factors” in forecasting may disturb the set of equilibrium outcomes when agents learn by using recursive methods. We isolate conditions under which new phenomena, which we call exuberance equilibria, can exist in a standard self-referential environment. Local indeterminacy is not a requirement for existence. We construct a simple asset-pricing example and find that exuberance equilibria, when they exist, can be extremely volatile relative to fundamental equilibria.

By endowing his agents with simple forecasting models, or representations, M. Woodford (“Learning to Believe in Sunspots,” Econometrica 58, 277–307, 1990) found that finite state Markov sunspot equilibria may be stable under learning. We show that common factor representations generalize to all sunspot equilibria the representations used by Woodford. We find that if finite state Markov sunspots are stable under learning then all sunspots are stable under learning, provided common factor representations are used.

We investigate both the rational explosive inflation paths studied by McCallum (2001), and the classification of fiscal and monetary policies proposed by Leeper (1991), for stability under least squares (LS) learning of the rational expectations equilibria (REE). Our first result is that the explosive fiscalist REE is not locally stable under LS learning. In contrast, in Leeper's setting, there are policy regimes for which the fiscalist solution, in which fiscal variables affect the price level, can be a locally stable outcome under LS learning. However, for other policy regimes the monetarist solution is, instead, the locally stable REE.

The rational expectations hypothesis swept through macroeconomics during the 1970s and permanently altered the landscape. It remains the prevailing paradigm in macroeconomics, and rational expectations is routinely used as the standard solution concept in both theoretical and applied macroeconomic modelling. The rational expectations hypothesis was initially formulated by John F. Muth Jr. in the early 1960s. Together with Robert Lucas Jr., Thomas (Tom) Sargent pioneered the rational expectations revolution in macroeconomics in the 1970s.

The title speaks for itself: This special issue focuses attention on dynamical models in which expectations matter and in which expectational coordination is not taken for granted. This is a subject that has generated a lot of research in the past 20 years; the sample of papers presented here is witness to the present activity and the ongoing progress of our understanding. Here, as in previous research, the rational expectations construct remains the anchor of the analysis of expectational coordination, but it is subject to the additional critical scrutiny for which the present state of our knowledge calls more and more.

We investigate local strong rationality (LSR) in a one-step-forward-looking univariate model with memory one. Eductive arguments are used to determine when common knowledge (CK) that the solution is near some perfect-foresight path is sufficient to trigger complete coordination on that path (i.e., the path is LSR). Coordination of expectations is shown to depend on three factors: the nature of the CK initial beliefs, the degree of structural heterogeneity, and the information structure. Our sufficient conditions for LSR precisely reflect these features and provide basic consistent justifications for the choice of the saddle-path solution.