Economic agents often have to make decisions in environments affected by regime switches but expectation formation has hardly been explored in this context. We report about a laboratory experiment whose participants judgmentally forecast three time series subject to regime switches. The participants make forecasts without context knowledge and without support from statistical software. Their forecasts are only based on the previous realizations of the time series. Our interest is the explanation of the average forecasts with a simple model, the bounds & likelihood heuristic. In previous studies it was shown that this model can explain average forecasting behavior very well given stable and stationary time series. We find that the forecasts after a structural break are characterized by a higher variance and less accuracy over several periods. Considering this transition phase in the model, the heuristic performs even slightly better than the Rational Expectations Hypothesis.

We study with the help of a laboratory experiment the conditions under which an uninformed manipulator—a robot trader that unconditionally buys several shares of a common value asset in the beginning of a trading period and unwinds this position later on—is able to induce higher asset prices. We find that the average price is significantly higher in the presence of the manipulator if and only if the asset takes the lowest possible value and insiders receive perfect information about the true value of the asset. It is also evidenced that the robot trader makes trading gains. Finally, both uninformed and partially informed traders may suffer from the presence of the robot.

This paper presents an algorithm for simulating multiple equilibria in otherwise-linear dynamic models with occasionally-binding constraints. Our algorithm extends the guess-and-verify approach of Guerrieri and Iacoviello (2015) to detect and simulate multiple perfect foresight equilibria, and allows arbitrary “news shocks” up to a finite horizon. When there are multiple equilibria, we show how to compute expected paths using a “prior probabilities” approach and we provide an approach for running stochastic simulations with switching between equilibria on the simulated path. A policy application studies a New Keynesian model with a zero lower bound on nominal interest rates and multiple equilibria, including a “bad” solution based on self-fulfilling pessimistic expectations. A price-level targeting rule does not always eliminate the bad solution, but it shrinks the indeterminacy region substantially and improves stabilization and welfare relative to more conventional interest rate rules or forward guidance.

On average, childless women observed by the Panel Study of Income Dynamics report that they intend to have more children than they actually have. A collection of intentions that record only whether respondents intend to have another child can more accurately predict the number of children they have. Errors in the formation of intentions are not required to explain this finding. Rather, if intentions record a survey respondent's most likely predicted number of children, then the average of these intentions does not necessarily equal average actual fertility, even if intentions are formed using rational expectations.

While the last few decades of political economic history give the impression that the logic of neoliberalism is inexorable, this article argues that once we look further backwards and dig into recently declassified archives documenting the early days of neoliberal theory and practice, we find a messier picture. Economic policymakers in Thatcher and Reagan's administrations in the early 1980s did not set out to ‘fail forwards’ by generating a crisis that would enable a statist kind of neoliberalism. The key ideas that they drew on and the policies that they used to put them into practice sought to transform the economy indirectly, through a set of performative policy devices that they believed would generate a dramatic shift in people's inflationary expectations, lowering inflation without provoking a major recession. Archival records make it clear that these efforts were not only a failure, but also one that policymakers were acutely aware of at the time. By examining these quiet failures in economic policy, we can better understand how these governments simultaneously failed in their early efforts to introduce neoliberal economics and yet ultimately succeeded in transforming their economies in important respects – and in legitimising those transformations by narrating failure as a kind of inevitable success.

This paper charts the evolution of mainly empirical research at the NIESR over the 1970s and 80s. As was all too evident there were very large discrete technical improvements in data handling and manipulation over this period. Less well appreciated were the effects on the economy of major supply-side shocks coming from the World economy leading to ‘Stagflation’ and, interconnected but somewhat later, in the UK, marked changes in macro policy regime. This latter was strongly influenced by the seminal papers of Lucas (1976) and Sims (1980); both highly critical of the then current practices in macroeconomics, though each having very different intellectual stances. The response in the NIESR was to engage at an early stage in these innovations; applying the mantra that an informed criticism was more efficient than an uninformed one. During this period it became a leader in the econometrics of applied macro modelling under different expectations assumptions, including the rational expectations hypothesis (REH). Throughout, it remained critical of the anti-empirical drift encouraged in the Lucas Critique, criticism borne out more recently by the financial instability of the 1990s and the crisis that followed.

This paper examines the stability of sunspot equilibria in one-sector RBC models under infinite horizon learning. We present general conditions under which the reduced-form model can possess E-stable sunspot equilibria and apply these conditions to three prominent one-sector RBC models. We find that the rational expectations sunspot equilibria are generally unstable under learning.

This paper tests the ability of New Keynesian models to match the data regarding a key channel for monetary transmission: the dynamic interactions between macroeconomic variables and their corresponding expectations. We exploit survey expectations data and adopt a dynamic stochastic general equilibrium (DSGE)-VAR approach to assess the extent and sources of model misspecification. The results point to serious misspecification in the expectations-formation side of the DSGE model. The rational expectations hypothesis is primarily responsible for the model's failure to capture the co-movements between observed macroeconomic expectations and realizations. Alternative models of expectations formation help partially reconcile the New Keynesian model with the data.

The general method of moments procedure is used for estimating a soybean acreage response function assuming that producers hold rational expectations. Results indicate that soybean, corn, and wheat futures prices, lagged acreage, and government programs are significant factors for determining soybean plantings. Implications of the results are that crop acreage selection by Georgia producers is not very responsive to demand shocks. Thus, producers in other regions are more likely to absorb impacts from these shocks on crop acreage selection.

Risk has long been recognized as potentially important in determining agricultural supply. However, supply response models have either incorporated risk in an ad hoc manner or not at all. A rational expectations supply response model incorporating price risk is developed, an estimation procedure suggested, and an empirical example presented.

We explore real-time adaptive nonlinear learning dynamics in stochastic macroeconomic systems. Rather than linearizing nonlinear Euler equations where expectations play a role around a steady state, we instead approximate the nonlinear expected values using the method of parameterized expectations. Further, we assume that these approximated expectations are updated in real time as new data become available. We argue that this method of real-time parameterized expectations learning provides a plausible alternative to real-time adaptive learning dynamics under linearized versions of the same nonlinear system, and we provide a comparison of the two approaches.