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The adoption as policy models by central banks of representative agent New Keynesian dynamic stochastic general equilibrium models has been widely criticised, including for their simplistic micro-foundations. At the Bank of England, the previous generation of policy models is seen in its 1999 medium-term macro model (MTMM). Instead of improving that model to correct its considerable flaws, many shared by other non-DSGE policy models such as the Federal Reserve’s FRB/US, it was replaced in 2004 by the DSGE-based BEQM. Though this clearly failed during and after the global financial crisis, it was replaced in 2011 by the DSGE COMPASS, complemented by a ‘suite of models’. We provide a general critique of DSGE models for explaining, forecasting and policy analyses at central banks, and suggest new directions for improving current empirical macroeconomic models based on empirical modelling broadly consistent with better theory, rather than seeking to impose simplistic and unrealistic theory.

We develop a micro simulation model for the macroeconomic business cycle. Our model is based on three main ideas. First, we want to specify how macroeconomic coordination is achieved without a dominating influence of price mechanisms. Second, we want to incorporate the stock-flow-consistent (SFC) approach that has become popular in post-Keynesian macroeconomics. Existing macroeconomic models often pay no attention to how short-run outcomes (in the form of surpluses or deficits on the account balances of individual agents, or groups of agents) accumulate into long-run debt. The SFC approach models such deficits and surpluses, and their accumulation, explicitly, and imposes a logic in which these long-run balances co-determine the macroeconomic coordination outcome. Third, we want to allow for bankruptcies as a major mechanism in the business cycle. In reality, bankruptcies are a way in which long-run balances get adjusted, but most often the SFC models do not allow bankruptcies as a way in which long-run balances adjust. In our model, bankruptcies arise because agents do not adapt their behavior quickly enough as debt, or assets, accumulate. This is parametrized, so that bankruptcies can disappear in the simulation runs, which enables us to compare the nature of business cycles with and without bankruptcies. Our results show a clear business cycle that is driven by accumulation of financial assets and the effects this has on the real economy. By changing some of the key parameters, we show how the nature of the business cycle changes as a result of changes in the assumed behavior of agents.

Macroeconomics has been under scrutiny as a field since the financial crisis, which brought an abrupt end to the optimism of the Great Moderation. There is widespread acknowledgement that the prevailing dynamic stochastic general equilibrium (DSGE) models performed poorly, but little agreement on what alternative future paradigm should be pursued. This article is the elaboration of four blog posts that together present a clear message: current DSGE models are flawed, but they contain the right foundations and must be improved rather than discarded. Further, we need different types of macroeconomic models for different purposes. Specifically, there should be five kinds of general equilibrium models: a common core, plus foundational theory, policy, toy, and forecasting models. The different classes of models have a lot to learn from each other, but the goal of full integration has proven counterproductive. No model can be all things to all people.

The paper outlines a structural macroeconometric model for the economy of Russia. The aim of the research is to analyze how the domestic economy functions, generate forecasts for important macroeconomic indicators and evaluate the responses of main endogenous variables to various shocks. The model is estimated based on quarterly data starting from 2001 to 2019. The majority of the equations are specified in error correction form due to the non-stationarity of variables. Stochastic simulation is used to solve the model for expost and ex-ante analysis. We compare forecasts of the model with forecasts generated by the VAR model. The results indicate that the present model outperforms the VAR model in terms of forecasting GDP growth, inflation rate and unemployment rate. We also evaluate the responses of main macroeconomic variables to VAT rate and world trade shocks via stochastic simulation. Finally, we generate ex-ante forecasts for the Russian economy under the baseline assumptions.

This paper develops a new theory of fluctuations—one that helps accommodate the notions of \"animal spirits\" and \"market sentiment\" in unique-equilibrium, rational-expectations, macroeconomic models. To this goal, we limit the communication that is embedded in a neoclassical economy by allowing trading to be random and decentralized. We then show that the business cycle may be driven by a certain type of extrinsic shocks which we call sentiments. These shocks formalize shifts in expectations of economic activity without shifts in the underlying preferences and technologies; they are akin to sunspots, but operate in unique-equilibrium models. We further show how communication may help propagate these shocks in a way that resembles the spread of fads and rumors and that gives rise to boom-and-bust phenomena. We finally illustrate the quantitative potential of our insights within a variant of the RBC model.

Most macroeconomic models for monetary policy analysis are approximated around a zero inflation steady state, but most central banks target an inflation rate of about 2 percent. Many economists have recently proposed even higher inflation targets to reduce the incidence of the zero lower bound constraint on monetary policy. In this survey, we show that the conduct of monetary policy should be analyzed by appropriately accounting for the positive trend inflation targeted by policymakers. We first review empirical research on the evolution and dynamics of U.S. trend inflation and some proposed new measures to assess the volatility and persistence of trend-based inflation gaps. We then construct a Generalized New Keynesian model that accounts for a positive trend inflation. In this model, an increase in trend inflation is associated with a more volatile and unstable economy and tends to destabilize inflation expectations. This analysis offers a note of caution regarding recent proposals to address the existing zero lower bound problem by raising the long-run inflation target.

Systemic risk arises when shocks lead to states where a disruption in financial intermediation adversely affects the economy and feeds back into further disrupting financial intermediation. We present a macroeconomic model with a financial intermediary sector subject to an equity capital constraint. The novel aspect of our analysis is that the model produces a stochastic steady state distribution for the economy, in which only some of the states correspond to systemic risk states. The model allows us to examine the transition from “normal” states to systemic risk states. We calibrate our model and use it to match the systemic risk apparent during the 2007/2008 financial crisis. We also use the model to compute the conditional probabilities of arriving at a systemic risk state, such as 2007/2008. Finally, we show how the model can be used to conduct a macroeconomic “stress test” linking a stress scenario to the probability of systemic risk states.

This study conducts a comprehensive review of macroeconomic models within the Water, Energy, Food, and Ecosystem (WEFE) nexus, considering four different approaches: computable general equilibrium (CGE) models, integrated assessment models (IAMs), agent-based models (ABMs), and dynamic stochastic general equilibrium (DSGE) models. Specifically, we examine how macroeconomic models represent not only the WEFE nexus as a whole but also its individual components and their combinations. Spanning a collection of 77 papers published in the last 20 years, this review underscores the prevalence of CGE models and IAMs, followed by ABMs, as dominant avenues of research within this field. CGE models frequently investigate interconnections between pairs of WEFE elements, while IAMs focus on the whole nexus. At the same time, ABMs do not exhibit a clear pattern, whereas DSGE models predominantly concentrate on the energy component alone. Overall, our findings indicate that the development of DSGE models and ABMs is still in its early stages. DSGE models potentially allow the analysis of uncertainty and risk in this field, while ABMs might offer new insights into the complex interactions between natural and human systems but still lack a common framework.

I develop a behavioral macroeconomic model in which agents have cognitive limitations. As a result, they use simple but biased rules (heuristics) to forecast future output and inflation. Although the rules are biased, agents learn from their mistakes in an adaptive way. This model produces endogenous waves of optimism and pessimism (\"animal spirits\") that are generated by the correlation of biased beliefs. I identify the conditions under which animal spirits arise. I contrast the dynamics of this model with a stylized DSGE-version of the model and I study the implications for monetary policies. I find that strict inflation targeting is suboptimal because it gives more scope for waves of optimism and pessimism to emerge thereby destabilizing output and inflation.

We resume the line of research pioneered by C. A. Sims and Zha (Macroeconomic Dynamics, 2006, 10, 231–272) and make two novel contributions. First, we provide a formal treatment of partial fundamentalness—that is, the idea that a structural vector autoregression (VAR) can recover, either exactly or with good approximation, a single shock or a subset of shocks, even when the underlying model is nonfundamental. In particular, we extend the measure of partial fundamentalness proposed by Sims and Zha to the finite-order case and study the implications of partial fundamentalness for impulse-response and variance-decomposition analysis. Second, we present an application where we validate a theory of news shocks and find it to be in line with the empirical evidence.