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.

This paper seeks to provide a theoretical explanation for the weak association between measures of financial structure—as defined by the mixture of bank-based and market-based financial systems in an economy—and economic development. Lenders fund risky investment projects of firms by drawing up loan contracts in the presence of an informational asymmetry. An optimal contract entails the issue of debt, equity, or a mix of the two. The equilibrium choice of contract and the financial structure depend on the state of the economy, which in turn depends on the contracting regime. Based on this analysis, the paper provides a theory that can explain the wide diversity of financial structure among middle-income countries.

The limited asset-market participation hypothesis has triggered a debate on DSGE models' determinacy when the central bank implements a standard Taylor rule. We reconsider the issue here in the context of an exogenous money supply rule, documenting the role of nominal and real frictions in determining these results. A general conclusion is that frictions matter for stability insofar as they redistribute income between Ricardian and non-Ricardian households when shocks hit the economy. Finally, we extend the model to allow for the possibility that consumers who do not participate in the market for interest-bearing securities hold money. In this case, endogenous monetary transfers between the two groups make it possible to smooth consumption differences, and the model is determinate, provided that the non-negativity constraint on individual money holdings is satisfied.

Estimated microproduction functions confront two major problems—those of (1) unknown functional forms and (2) the measurement of capital independent of the distribution of output among the factors of production. The latter problem has emerged unresolved from the earlier Cambridge capital controversy. In the presence of these two problems, all specifications of microproduction functions have involved nonunique coefficients and error terms. We provide a method of deriving time-varying coefficients that produces unique coefficients and error terms. Specifically, we respecify the microproduction function in such a way that its coefficients are the sums of (i) the appropriate partial derivatives and (ii) exact representations of excluded-variable biases. By decomposing the total coefficients, we obtain the unique coefficients and a unique error term. Our treatment of heterogeneous capital is not subject to the criticisms of that concept that emerged during the Cambridge controversy.

Models of endogenous growth have not been able to account for the variety of empirically observed distributional properties of the returns to innovation, in part, because of the limitations necessarily imposed on competition to cope with increasing returns to scale. Exponential growth, fat tails, Pareto–Levy distributed upper tails, and upper value outliers, are associated with increasing returns to scale and innovation. At the same time, properties such as bifurcated research investment strategies, bimodal returns to innovation, and Laplace distributed firm growth rates are products of competition. We build an agent-based model of endogenous technical change in which heterogeneous investments in patented knowledge and increasing returns to scale emerge these distributional properties within a competitive market. The combination of heterogeneous agents, costly information, and patents allow for a competitive landscape to persist amidst increasing returns. The ability of model to foster a coexistence of competition and increasing returns underlies the observed distributional properties.

This paper estimates univariate and multivariate trend-cycle decomposition models of GDP and considers the novel possibility of regime switches in the growth of potential output. We compute both ex post and real-time estimates of the output gap to check the stability of our estimates to GDP data revisions. We find some evidence of regime changes in the growth of potential output during the recessions experienced by the euro area. We also run a forecasting experiment to evaluate the predictive power of the output gap for inflation. The benchmark autoregressive model tends to obtain the best forecasts for one-quarter-ahead forecasts, but the output gap measures help to forecast inflation for longer horizons.

This paper deals with the development of growth models from the optimal one-sector neoclassical approach of Cass–Malinvaud–Koopmans vintage, through two-sector, multisector, and turnpike models, and proceeds to discuss their displacement by the single-sector stochastic growth model. We also focus on the definitional shift regarding the turnpike. This is done by surveying both unpublished and published work by Uzawa, Cass, Koopmans, and McKenzie regarding growth and the turnpike, the cross-fertilization between them, and how this brought about the conflation of optimality and the turnpike, and the metamorphosis of the notion of the turnpike, from that of Dorfman–Samuelson Solow, to the Koopmans–McKenzie “amalgam” of models. Finally, the appearance of endogenous growth models, based on the work of Shell and Uzawa, is dealt with.

The transcript of a panel discussion marking three decades of the real business cycle approach to macroeconomic analysis as manifested in Kydland and Prescott's “Time to Build” (Econometrica, 1982) and Long and Plosser's “Real Business Cycles” (Journal of Political Economy, 1983). The panel consists of Edward Prescott, Finn Kydland, Charles Plosser, John Long, Thomas Cooley, and Gary Hansen. The discussion is moderated by Sumru Altug and Warren Young. The panel touches on a wide variety of issues related to real business cycle models, including their history and methodology, starting with the work of Prescott and Kydland at Carnegie Tech and Plosser and Long at Rochester; their applications to policy; and their role in the recent financial crisis and likely future.

The panel discussion was held in a session sponsored by the History of Economics Society at the Allied Social Sciences Association (ASSA) meetings in the Randle A Room of the Manchester Grand Hyatt Hotel in San Diego, California.

We build a stochastic discount factor—SDF—using U.S. domestic financial data only, and provide evidence that it accounts for stylized facts about foreign markets that escape SDFs generated by consumption-based models. When our SDF is interpreted as the projection of the pricing kernel from a fully specified model in the space of returns, our results indicate that a model that accounts for the behavior of domestic assets goes a long way toward accounting for the behavior of foreign asset prices. In our tests, we address predictability, a defining feature of the forward premium puzzle—FPP—by using instruments that are known to forecast excess returns in the moment restrictions associated with Euler equations both in the equity and in the foreign markets.

Recent empirical findings suggest that macroeconomic variables are seldom normally distributed. For example, the distributions of aggregate output growth-rate time series of many OECD countries are well approximated by symmetric exponential-power (EP) densities with Laplace fat tails. In this work, we assess whether real business cycle (RBC) and standard medium-scale New Keynesian (NK) models are able to replicate this statistical regularity. We simulate both models, drawing Gaussian- vs Laplace-distributed shocks, and we explore the statistical properties of simulated time series. Our results cast doubts on whether RBC and NK models are able to provide a satisfactory representation of the transmission mechanisms linking exogenous shocks to macroeconomic dynamics.