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This paper aims to analyze forecasting problems from the perspective of information extraction. Circumstances are studied under which the forecast of an economic variable from one domain (country, industry, market segment) should rely on information regarding the same type of variable from another domain even if the two variables are not causally linked. It is shown that Granger causality linking variables from different domains is the rule and should be exploited for forecasting.

This paper applies information economics, in particular the study of rational information extraction, to shed light on the debate on causality and forecasting.

It is shown that the rational generalization of information across domains can lead to effects that are hard to square with economic intuition but worth considering for forecasting. Information from one domain is shown to affect that from another domain if there is at least one common factor affecting both domains, which is not (or not yet) observed when a forecast has to be made. The analysis suggests the theoretical possibility that the direction of such effects across domains can be counter-intuitive. In time-series econometrics, such effects will show up in estimated coefficients with the “wrong” sign.

This study helps forecasters by indicating a wider set of variables relevant for prediction. The analysis offers a theoretical basis for using lagged values from the type of variable to be forecast but from another domain. For example, when forecasting the bond risk spread in one country, introducing in the time-series model the lagged value of the risk spread from another country is suggested. Two empirical examples illustrate this principle for specifying models for prediction. The application to risk spreads and inflation rates illustrates the principles of the approach suggested here which is widely applicable.

The present study builds on a probability theoretic analysis to inform the specification of time-series forecasting models.

This study aims to address the issue of prediction of inflation differences for an economy that considers either fixing its exchange rate or joining a currency union. In this setting, individual countries have limited control over their inflation, and anticipating the possible course of domestic inflation relative to inflation abroad becomes an important input in policy-making. In this context, the author compares simple forecast heuristics and econometric modeling.

The study compares two basically different approaches. The first approach of forecasting consists of simple heuristics. Various heuristics are considered that differ with respect to the economic reasoning that goes into quantifying the forecast rules. The simplest such forecasting heuristic suggests that the average over all available observations of inflation differentials should be taken as a predictor for the future. Bringing more economic insight to bear suggests a further heuristic according to which historical inflation differentials should be adjusted for changes in the nominal exchange rate. A further variant of this approach suggests that a forecast should exclusively rely on data from earlier times under a pegged exchange rate. A fundamentally different approach to prediction builds on dynamic econometric models estimated by using all available historical data independent of the currency regime.

The author studies three small member countries of the Eurozone, i.e. Finland, Luxembourg and Portugal. For the evaluation of the various forecasting strategies, he performs out-of-sample predictions over a horizon of five years. The comparison of the four different forecasting strategies documents that the variant of the forecast heuristic that draws on data from earlier experiences under fixed exchange rates performs better than the forecast based on the estimated econometric model.

The findings of this study provide helpful guidelines for countries considering either joining a currency union or fixing their exchange rate. The author shows that a simple forecasting heuristic gives sound advice for assessing the likely course of inflation.

This study describes how economic theory can guide the selection of historical data for assessing likely future developments. The analysis shows that using a simple heuristic based on historical analogy can lead to better forecasts than the analytically more sophisticated approach of econometric modeling.

This study aims to investigate the factors that make people want to hold cryptocurrency. Besides prior experience with holding crypto, this paper considers various expectations and conjectures about the future as key determinants.

Data for this study come from an online survey in the USA. Econometric analyses help to quantify the relative importance of drivers of demand for cryptocurrency.

Survey respondents will more likely hold cryptocurrency in the future the more they expect cryptocurrency to replace government money, to increase transparency in monetary affairs and to yield high profits. Importantly, demand is shown to be driven by the anticipation that nonmonetary uses of the Blockchain technology will have a spillover effect on the Bitcoin price. By contrast, subjective expectations of a crypto-induced financial crisis dampen demand. Econometric analyses show that differences in the future demand among people with and without prior holdings of cryptocurrency largely stem from differences in their expectations.

By relating individuals’ expectations to their plans, the present approach offers more insights than the mere attitude surveys already available. This paper’s insights on crypto demand drivers indicate that regulators should be wary about signaling safety of an asset whose fundamental value is still uncertain.

This paper aims to study the importance of initial resource endowments, the savings rate, and financing opportunities for growth and the distribution of income.

The analysis is based on a theoretical model of accumulation and applies simulations. The frame of presentation is the so‐called Kuznets curve.

The paper shows under what circumstances the inverted U‐shaped curve hypothesized by Kuznets emerges and clarifies the role of a functioning capital market for economic growth and the distribution of income. It turns out that when initial income disparities are high and the savings rate is low it cannot be taken for granted that economic growth leads to a more equal income distribution. The paper further shows that with an efficient capital market total income rises faster and further and inequality is always lower than without a functioning capital market.

Future research should help to identify the institutional and regulatory frameworks that promote a stable basic financial infrastructure.

By drawing on the concept of a hydraulic society the interplay of key factors that drive economic growth and income inequality can be clarified.