“The gulfs between doctrine and observation, between theory and practice, are chronic sources of malaise in our discipline.”   -James Tobin (1993) 

The new-Keynesian consensus on monetary policy is deceptively straightforward. In effect, it abstracts the economy into a set of three micro-founded equations that seek to capture the output behaviour, inflation-unemployment trade-off, and central bank monetary policymaking. This abstraction has come at a great cost. The new-Keynesian model has repeatedly failed as a predictive framework for major macroeconomic crises since its introduction, starting from the 2008 crisis to the inflationary surge in the aftermath of the pandemic. These predictive failures are compounded by a record of policy guidance that has repeatedly steered post-crisis recovery in the wrong direction. Two decades on from Robert Lucas’ famous proclamation that macroeconomics in its original sense had succeeded, macroeconomics has made no desirable progress on the key economic phenomenon that it seeks to explain, let alone have anything meaningful to say about the pressing contemporary challenges of climate change and inequality—resulting in a discipline that projects a false sense of completeness in theory while offering little guidance on real-world policy concerns. This gulf between theory and practice suggests the need for an urgent rethinking of not just the economic and econometric underpinnings of the discipline, but also the moral purpose of economic policymaking in society.  

New-Keynesian Model

The new-Keynesian model consists of, first, the IS curve, which posits that the output gap is a function of the expected output gap and the real interest rate -which is problematic for a combination of reasons discussed below. The second equation is the expectations augmented Phillips curve, which suggests that inflation is a function of inflation expectations and the unemployment rate. Little evidence exists to suggest that expectations play a major role in inflation dynamics. Instead, there is convincing evidence (Adams & Barett 2024) otherwise, which identifies that the role of expectations might be heterogeneous across different states of the macroeconomy. The third new-Keynesian equation is the Taylor rule, which suggests that the central bank raises interest rates in response to the output gap or inflation increasing above the target rate. There is little empirical evidence (Nakamura, Riblier & Steinsson 2025) to suggest that central banks follow the Taylor rule. Importantly, the rule, together with the IS equation, builds on the assumption that any government will inherently crowd out private investment by forcing the central bank to raise interest rates. However, this misunderstands fiscal policy, trivialises the effects of government spending by failing to recognise the complex and systemic ways in which government spending affects the economy, depending on how well targeted it is. 

Such neglect of the heterogeneous effects of policy is symptomatic of a discipline that fails to integrate a systems thinking approach–despite aiming to model what is undoubtedly a complex system (Keen, 1995 and 2013). 

The equations are different from original Keynesian theory in that they are “micro-founded”, referring to the fact that they build on the optimising behaviour of a representative consumer who maximises consumption subject to a constraint. Policy implications are then drawn by building a general equilibrium model and introducing imaginary “shocks” which exogeneously affect the macroeconomy.

There is little theoretical or empirical evidence to suggest that the microfoundations approach is superior to the Keynesian models it sought to replace. For example, the Real Business Cycle (RBC) model upon which the new-Keynesian framework was built, predicts unreasonably high intertemporal elasticity of substitution of labour supply. Similarly, the presence of expectations and micro-foundations results in the new-Keynesian model predicting an unrealistically high impact (Del Negro, Giannoni & Patterson 2023) of future monetary policy announcements on current economic activity, which is just one of several empirical failures of this approach (Mavroeidis, Plagborg-Moller & Stock 2014).

Confronted by these empirical failures, mainstream macroeconomists have attempted to match the data by introducing a variety of ad-hoc methods, the use of which should itself raise serious doubts about the theoretical grounding of using micro-foundations. If micro-foundations and rational expectations are an appropriate starting point for understanding economic behaviour, then such models should not require any ad-hoc additions or assumptions to explain the data in the first place. On a more elementary level, there is no microeconomic evidence suggesting the presence of the exogenous shocks that drive business cycles in the RBC model, nor does theory offer any indication of what such a shock might constitute.

The failure to predict the financial crisis, despite warnings (Keen 2009; Glodley and Izurieta 2001; Shriller 2003) from heterodox researchers within the profession, should have marked a sobering inflexion point in macroeconomic research and prompted a more humble evaluation of the econometric and theoretical weaknesses of the new-Keynesian framework. Instead, these failures have been met with deeper institutional commitment to Dynamic Stochastic General Equilibrium (DSGE) new-Keynesian models, an approach that risks distancing macroeconomic theory even further from the realities of policymaking.

Misguiding Policy

Abstraction is inevitable in economic modelling, but the abstractions of the new-Keynesian framework are far from neutral and creates bias in the application of these models to important policy questions. Most significantly, new-Keynesian models understate the cost of recessions by ignoring path dependence and hysteresis. Hysteresis defines a scenario where temporary economic shocks (e.g., a recession) have permanent, lasting effects on long-term economic performance. Instead, new-Keynesian models assume that an economy has an exogenous potential output, to which it returns after any fluctuations. This blind spot is not specific to monetary policymaking. Conventional microeconomics does not recognise that once trading out of equilibrium occurs, phenomena like hysteresis can easily occur, thereby misguiding the micro-foundations approach (Fisher 1983). Broadly, the neglect of hysteresis is symptomatic of macroeconomics becoming increasingly singularly focused on inflation reduction, with unemployment becoming a policy tool, instead of a policy target (Stiglitz 1997). 

Once we acknowledge that these models do not consider path dependence, the reliability of another key component, the natural rate of interest (r*), is also in question. First, we should not expect a single, unobservable variable, such as r*, to uniquely serve as a guide to something as complex as monetary policy, when interest rates have heterogeneous effects across different channels at different times depending upon the state of the macroeconomy. Second, even if one deduces r*, assuming that output returns to an exogenous natural level, it ignores the fact that the underlying factors–such as trend productivity–which determine r* are themselves a function of short-term central bank policy. Such abstractions necessarily result in policy slogans like ‘closing the output gap controls inflation’ and ‘this gap can necessarily be closed simply by raising interest rates’, which yield elegant results in theory but, since it is not based on the real world, are dangerous as a guide for policymaking (Rudd 2024).

A related policy guide that is often measured with significant uncertainty is the non-accelerating inflation rate of unemployment (NAIRU). The NAIRU has remained the centrepiece in justifications for raising the rate of unemployment to combat inflation, even as there remains significant debate over the reliability of a cyclically invariant single steady state unemployment rate (Lang, Setterfield & Shakaki 2020). Regardless of this criticism, the NAIRU essentially cannot guide policy because of how imprecisely it is estimated. For example, the Reserve Bank of Australia’s NAIRU estimates have a 90% confidence interval of 0.7% (Gross 2025). Estimating that the NAIRU could be anywhere between 4-5%, in the case of Australia, corresponds to a policy decision that influences hundreds of thousands of workers, with little confidence in the underlying theory or econometric robustness of the estimate to warrant the high cost of unemployment and hysteresis.

Little progress has been made on this front since Solow’s admission:

“The way that modern macroeconomics tosses around the notion of a ‘natural rate of unemployment’ is a sort of intellectual scandal … The coarseness of the definition and the weakness of the empirical results … suggest that we are in the presence of something that is believed for extra-scientific reasons.” (Solow, Budd & Weizsacker 1987).

The rise of the new-Keynesian framework has coincided with an excessive focus on inflation control and a systematic minimisation of hysteresis. This neglect, coupled with an array of research that misunderstood and minimised the role of fiscal policy, has misinformed central banks’ responses to the largely supply-driven inflation that followed the pandemic. The sidelining of fiscal policy ruled out a more targeted, fiscally driven response—one that could have achieved stabilisation at far lower distributional cost. Policies that stimulated the supply-side–such as The Inflation Reduction Act, CHIPS and Science Act–and targeted tax policy measures to contain market power all represented more effective, judicious and specialised measures instead of blunt rate hikes that operate by raising unemployment, and once again, risking significant welfare costs through hysteresis. Unfortunately, the new-Keynesian framework is not flexible enough to guide such policy responses, nor understand the adverse distributional consequences of blunt monetary policy measures.

DSGE and the Regress of Identification

Even setting aside its theoretical limitations, econometric assessments of DSGE models cast serious doubt on the reliability of the new-Keynesian framework as a basis for policymaking. DSGE models build upon the RBC model by allowing for some role of monetary policy in the short run. Fiscal policy remains constrained by the Taylor rule and unrealistic budget constraints. DSGE models allow for this role of monetary policy by assuming sticky prices, which is an attempt to more closely represent the real world. However, the most striking result of the RBC model is that recessions are a result of exogenous shocks and that policy has no role to play. Such a prediction stems from the logical impossibility that recessions arise from purely exogeneous shocks and is irreconcilable with empirical evidence from economic history. For example, it is unreasonable to argue that Volcker’s interest rate hikes had no effect on the economy. Similarly, to anyone familiar with the mechanics of government spending, coupled with empirical evidence on the fiscal multipliers, it should be clear that policy can indeed have significant effects—especially after a recession. However, they continue to model imaginary shocks such as those in the RBC model, ignoring complex cause and effect links in the real world. A systems thinking approach further identifies the theoretical issues in analysing something as complex as an economy through a function of interactions between the rational, fully informed agents who arrive at optimal behaviour that is consistent with an on-equilibrium outcome. 

Apart from weak theoretical foundations, such unobservable shocks worsen the identification problem, which the new-Keynesian approach was supposed to solve. The identification problem refers to the difficulty of determining whether a model’s estimated parameters actually reflect real causal mechanisms — rather than just statistical correlations. In specifying these shocks, the researcher must make assumptions about the distribution of these shocks. In contrast to Keynesian models where such assumptions were transparent, the DSGE approach disguises assumptions under a layer of math, sufficient to confuse most policymakers and non-economists. Even if one penetrates this technical smokescreen, any constructive debate over the distributional assumptions of these shocks is also forestalled by the fact that they are imaginary, meaning that there can be no reasonable debate about the veracity of the researcher’s assumptions. As such, the whole probabilistic shock framework is conceptually mistaken, not just mathematically obscured.

Once a researcher enters the realm of adding such imaginary shocks, econometric theory shows that the final results from the model become an artefact of the researcher’s prior assumptions. As Onatski and Williams showed, the workhorse Smets-Wouters DSGE model produces highly varying structural estimates based on the assumptions one make. Similarly, research in Bayesian econometrics suggests that even with access to a large sample, it is plausible that any inference about structural parameters stems from the prior assumptions specified by the researcher. The presence of expectations in the new-Keynesian equations makes the identification problem even more severe by doubling the number of parameters to be estimated with the same number of structural equations. 

There have been recent developments in the field of DSGE modelling, such as the inclusion of heterogeneous agents, and recognising that financial crises develop endogenously. However, recent evidence suggests that even such additions do not render empirical credibility, because such models still start from theoretically inconsistent frameworks—such as Ricardian equivalence—upon which an unrealistic optimisation problem with imaginary shocks is superimposed. Such ad-hoc additions also do not address the fact that DSGE models still build around an unrealistic rational consumer optimising consumption choices over a lifetime, and use the new-Keynesian Phillips curve, which, as discussed above, has serious empirical and theoretical flaws that have been most exposed in times of crisis—such as the 2008 crisis and the inflationary surge of 2021. This all just makes the math more complicated without getting the model closer to the real world or to Keynes’s original insights.

Another admission regarding the econometric analysis of these models, comes from  Paul Romer:-

“The treatment of identification now is no more credible than in the early 1970s but escapes challenge because it is so much more opaque.”

Beyond Conventional Central Banking

While limitations in explaining core macroeconomic phenomena discussed above should already be sobering, macroeconomics, in its current state, remains even worse placed to offer any policy insight on the pressing contemporary issues. One such issue is inequality. Built on micro-foundations that conveniently ignore distributional concerns, discussions on inequality are unsurprisingly divorced from both macroeconomic research and central bank policy. This neglect is  evident in remarks by the former President of the ECB, Mario Draghi:-

“I find it hard to reach the conclusion that, over a longer time frame, the outcome of our policies has been – or will be – to redistribute wealth and income in an unfair or unequal way.” 

Similarly, Jerome Powell argued:-

 “No, we don’t think monetary policy is exacerbating inequality. We think, in fact, it is helping those who didn’t have jobs get jobs.”

Such arguments are logically inconsistent when the models used by central banks ignore hysteresis, systematically downplaying permanent economic damage and its disproportionate burden which the new-Keynesian policy prescription of prioritising inflation control over full employment has placed on marginalised workers. The misguided rate hikes in response to the pandemic-induced inflation undoubtedly worsened existing inequalities, empirical evidence shows that it takes significantly longer3 for marginalised groups to recover to pre-recession levels of employment. To place this in the context of the NAIRU discussion above, if the NAIRU for the American economy is estimated to be 4%, the implicit unemployment rate of Black Americans, which is nearly double that of white Americans, would be 8%.

The insistence on inflation targeting–with studies across schools of economic thought showing little support for the efficacy of such a regime–has driven policymakers to raise interest rates at the cost of high unemployment across various times, including after the pandemic, which has repeatedly raised unemployment, disadvantaged indebted households, and further exacerbated inequality. By prioristing low inflation over full employment, inflation targeting has aligned central bank policy with bondholders’ interests, who benefit from lower inflation because it preserves the real value of their returns. The use of quantitative easing after the crisis, while helpful in preventing a sharper recession, also disproportionately benefited asset holders.

The result is then central banks that are increasingly designing policies that worsen distributional inequalities–with fiscal policy absent after the event to correct these imbalances.

Similarly, the climate crisis requires urgent action from policymakers and potentially central bankers. Asset stranding (when an asset becomes unusable or economically unviable

before the end of its expected lifetime), increased financial instability due to extreme climate events, and rising insurance costs all pose a systemic risk to the financial system, which the current DSGE framework is incapable of capturing, let alone addressing. DSGE models treat climate change as another exogenous shock, which does not sufficiently capture the complex links that are critical to understanding climate change dynamics and trivialises the green transition debate. Adding more imaginary shocks only serves to worsen the identification problem. Such models also rule out any meaningful discussion on the role of fiscal policy in enabling the green transition by assuming that the government has a flow budget constraint, whereby government spending is fully financed by tax revenues. Not only does making such an assumption fundamentally misunderstand the fiscal space available to countries, but it also builds into the model the assumption that any government spending to support the green transition necessarily crowds out private spending, which is antithetical to empirical evidence on the role of public investment in mobilising investment in renewables.

While there remains a very strong argument against the potential politicisation that may result from requiring central bankers’ reconcile the need for stimulating the economy in times of crises in an equitable manner and addressing climate change, using the incredibly narrow set of policy tools at their disposal, such a predicament is also result of mainstream macroeconomics disabling the role of the fiscal policy–which is a far more flexible and democratic tool, better suited to address such complex policy challenges. As Thornton (2023) outlines, a higher interest rate is not the only tool for managing inflation.

Distorting the Policy Discourse

Too often, the abstractions characteristic of the new-Keynesian DSGE framework render economic analysis trivial, with policy relying on “conventional wisdom” such as asserting that government spending necessarily crowds out private investment, interest rates are necessarily disinflationary or that there always exists a trade-off between unemployment and inflation. Such “simple-minded” phrases, as Hyman Minsky termed them, fail to grasp the complexity in economic systems and policymaking. It is bad practice–albeit convenient in DSGE modelling–to assume crowding out when there exist countless studies highlighting the difficulty in estimating a government multiplier, not to mention that the theoretical foundation of the crowding out theory itself remains subject to debate. The IMF walking back its catastrophic prediction of the efficacy of government spending represents a painful example of how mainstream theory has misguided policy at a very high human cost. 

Similarly, the inflation-unemployment tradeoff argument fails to appreciate the different ways in which a reduction in unemployment is achieved. Naturally, a reduction in unemployment through increased defence spending, for example, will be inflationary, but this might not necessarily hold for targeted reductions in unemployment that increase the production of consumer goods, or job guarantee programs that may smooth the volatility of inflation. 

The result of these abstractions is, then, a discipline that proposes absurd policy prescriptions that refuse to acknowledge the fundamental uncertainty inherent in economic systems. An example of such a policy prescription, stemming from the new-Keynesian bias towards monetary policy, is offered by Simon Wren-Lewis:-

“…in a New Keynesian model where social welfare is derived from agent’s utility, and monetary policy is unconstrained, there is no role for changes in government spending to assist monetary policy in demand management.”

Notwithstanding the criticism of the new-Keynesian models above, it is exceptionally myopic to believe that policy should be driven by unelected technocrats at central banks and not by elected policymakers. Against the backdrop of globalisation and deeply entrenched inequality, such handwaving, disguised in technical sophistication, has resulted in a society where macroeconomic and monetary policymaking debates are increasingly opaque, dominated by technocrats, with little consideration for the moral imperatives embedded in policymaking. Gliding over the issues of power, redistribution, morality and authority inherent to macroeconomic policymaking is also poor Social Science and represents a blind spot to integrating a pluralistic framework that recognises how misguided policies of the past have shaped contemporary economic, political and social outcomes. 

As a society, it is critical for us to question if we are confident in a theory with an array of predictive failures in forecasting macroeconomic crises, explaining why they occurred, and offering any reliable or conclusive causal evidence on its impact, yet so confident in itself to proclaim that it has reached a consensus on major policy issues. A more pluralistic approach, one that places moral, distributional and other concerns of society at its heart, while acknowledging the inherent uncertainty in economic policymaking and confronting the econometric flaws in contemporary models with more humility, would be a far more useful guide to practitioners of macroeconomics in serving the society.

References

Keen, S. (2009). The global financial crisis, credit crunches and deleveraging. Journal of Australian Political Economy, (64), 22-36. http://media.wix.com/ugd/b629ee_f08e1650ad63c0979f86c81c793a2953.pdf

Keen, S. 2013. “A monetary Minsky model of the Great Moderation and the Great Recession”: https://www.sciencedirect.com/science/article/pii/S0167268111000266

Keen, S. 1995, Finance and Economic Breakdown: Modeling Minsky’s “Financial Instability Hypothesis” Journal of Post Keynesian Economics , Summer, 1995, Vol. 17, No. 4 (Summer,1995), pp. 607-635

Thornton, T, ‘Are higher interest rates the only tool for managing inflation?’ Rethinking Economics International Blog. Available online: https://rethinkeconomics.org/blog/2023/08/03/managing-high-inflation

Adams, Jonathan J. and Philip Barrett (2024). “Shocks to inflation expectations”. In: Review of Economic Dynamics 54, p. 101234. issn: 1094-2025. doi: https://doi.org/10.1016/j.red.2024.101234. url: https://www.sciencedirect.com/science/article/pii/S1094202524000231.

Annicchiarico, Barbara and Fabio Di Dio (2015). “Environmental policy and macroeconomic dynamics in a new Keynesian model”. In: Journal of Environmental Economics and Management 69, pp. 1–21. issn: 0095-0696. doi: https://doi.org/10.1016/j.jeem.2014.10.002. url: https://www.sciencedirect.com/science/article/pii/S0095069614000850.

Arestis, Philip et al. (2014). “Inflation targeting and inflation convergence: International evidence”. In: Journal of International Financial Markets, Institutions and Money 31, pp. 285–295. issn: 1042-4431. doi: https://doi.org/10.1016/j.intfin.2014.04.002. url: https://www.sciencedirect.com/science/article/pii/S104244311400050X.

Arthur, W. Brian (2021). “Foundations of Complexity Economics”. In: Nature Reviews Physics 3.2, pp. 136–145. doi: 10.1038/s42254-020-00273-3.

Assa, Jacob and Marc Morgan (2025). “The General Relativity of Fiscal Space: Theory and Applications”. In: Review of Political Economy 0.0, pp. 1–35. doi: 10.1080/09538259.2025. 2479732. url: https://doi.org/10.1080/09538259.2025.2479732.

Ball, Laurence and Niamh Sheridan (Mar. 2003). Does Inflation Targeting Matter? Working Paper 9577. National Bureau of Economic Research. doi: 10.3386/w9577. url: http: //www.nber.org/papers/w9577.

Baumeister, Christiane and James D. Hamilton (2015). “SIGN RESTRICTIONS, STRUCTURAL VECTOR AUTOREGRESSIONS, AND USEFUL PRIOR INFORMATION”. In: Econometrica 83.5, pp. 1963–1999. issn: 00129682, 14680262. url: http : / / www.jstor.org/stable/43616994 (visited on 02/06/2026).

Blanchard, Olivier J. and Daniel Leigh (May 2013). “Growth Forecast Errors and Fiscal Multipliers”. In: American Economic Review 103.3, pp. 117–20. doi: 10.1257/aer.103.3.117. url: https://www.aeaweb.org/articles?id=10.1257/aer.103.3.117.

Cerra, Valerie, Antonio Fatás, and Sweta C. Saxena (Mar. 2023). “Hysteresis and Business Cycles”. In: Journal of Economic Literature 61.1, pp. 181–225. doi: 10 . 1257 / jel . 20211584. url: https://www.aeaweb.org/articles?id=10.1257/jel.20211584.

Cruz-e-Silva, Victor and Marco António Ribas Cavalieri (2022). “Patterns of interdisciplinary citations and asymmetry between economics and the neighboring social sciences from 1959 to 2018”. In: Nova Economia 32.1, pp. 261–291. doi: 10.1590/0103-6351/6591. url: https://doi.org/10.1590/0103-6351/6591.

Dafermos, Yannis et al. (Oct. 2024). Climate Change and Macroeconomic Models: Why General Equilibrium Models Do Not Work. Accessed: 2026-02-07. Institute for New Economic Thinking. url: https : / / www . ineteconomics . org / perspectives / blog / climate -change- and- macroeconomic- models- why- general- equilibrium- models- do- notwork.

Del Negro, Marco, Marc P. Giannoni, and Christina Patterson (2023). “The Forward Guidance Puzzle”. In: Journal of Political Economy Macroeconomics 1.1, pp. 43–79. doi: 10.1086/724214. eprint: https://doi.org/10.1086/724214. url: https://doi.org/10.1086/724214.

Deleidi, Matteo, Mariana Mazzucato, and Gregor Semieniuk (2020). “Neither crowding in or out: Public direct investment mobilising private investment into renewable electricity projects”. In: Energy Policy 140, p. 111195. issn: 0301-4215. doi: https://doi.org/10.1016/j.enpol.2019.111195. url: https://www.sciencedirect.com/science/article/pii/S0301421519307803.

Eichenbaum, Martin S, Joao Guerreiro, and Jana Obradovic (Jan. 2026). Ricardian NonEquivalence. Working Paper 34691. National Bureau of Economic Research. doi: 10.3386/w34691. url: http://www.nber.org/papers/w34691.

Fisher, Franklin M. (1983). “Introduction: Disequilibrium analysis and the theory of value”. In: Disequilibrium Foundations of Equilibrium Economics. Econometric Society Monographs. Cambridge University Press, pp. 1–16.

Godley, Wynne and Alex Izurieta (Oct. 2001). The Developing U.S. Recession and Guidelines for Policy. Strategic Analysis. Strategic Analysis 01-10. Levy Economics Institute of Bard College. url: https : / / www . levyinstitute . org/publications/the -developing-us-recession-and-guidelines-for-policy/.

Gross, Isaac (2025). “The NAIRU in Australia”. In: Australian Economic Review 58.3, pp. 242–245. doi: https : / / doi . org / 10 . 1111 / 1467 – 8462 . 70031. eprint: https:/ / onlinelibrary . wiley . com / doi / pdf / 10 . 1111 / 1467 – 8462 . 70031. url: https://onlinelibrary.wiley.com/doi/abs/10.1111/1467-8462.70031.

Krugman, Paul (June 2013). “How the Case for Austerity Has Crumbled”. In: The New York Review of Books. url: https://www.nybooks.com/articles/2013/06/06/howcase-austerity-has-crumbled/.

Lang, Dany, Mark Setterfield, and Ibrahim Shikaki (2020). “Is there scientific progress in macroeconomics? The case of the NAIRU”. In: European Journal of Economics and Economic Policies: Intervention 17.1, pp. 19–38. doi: 10.4337/ejeep.2019.0041. url: https://doi.org/10.4337/ejeep.2019.0041.

Lee, Donggyu (July 2024). Unconventional Monetary Policies and Inequality. Staff Report 1108. Revised October 2024. Federal Reserve Bank of New York. doi: 10.59576/sr.1108. url: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr1108.pdf.

Lucas, Robert E. Jr. (2004). The Industrial Revolution: Past and Future. AIER Economic Education Bulletin, Vol. XLIV No. 8. Accessed 2026. url: https://www.aier.org/wpcontent/uploads/2013/11/EEB-8.04-IndustRev.pdf. Mavroeidis, Sophocles, Mikkel Plagborg-Møller, and James H. Stock (Mar. 2014). “Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve”. In: Journal of Economic Literature, 52.1, pp. 124–88. doi: 10.1257/jel.52.1.124. url: https://www.aeaweb.org/articles?id=10.1257/jel.52.1.124.

Menand, Lev (2022). The Fed Unbound: Central Banking in a Time of Crisis. Columbia Global Reports. url: http : / / www . jstor . org / stable / jj . 15507148 (visited on 02/06/2026).

Mitchell, William F. (1998). “The Buffer Stock Employment Model and the NAIRU: The Path to Full Employment”. In: Journal of Economic Issues 32.2, pp. 547–555. issn: 00213624. url: http://www.jstor.org/stable/4227333 (visited on 02/07/2026).

Nakamura, Emi, Venance Riblier, and Jón Steinsson (Sept. 2025). Beyond the Taylor Rule. Working Paper 34200. National Bureau of Economic Research. doi: 10.3386/w34200. url: http://www.nber.org/papers/w34200.

Onatski, Alexei and Noah Williams (2010). “Empirical and policy performance of a forwardlooking monetary model”. In: Journal of Applied Econometrics 25.1, pp. 145–176. doi: https://doi.org/10.1002/jae.1131. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/jae.1131. url: https://onlinelibrary.wiley.com/doi/abs/10.1002/jae.1131.

Ramey, Valerie A. (Sept. 2011). “Can Government Purchases Stimulate the Economy?” In: Journal of Economic Literature 49.3, pp. 673–85. doi: 10.1257/jel.49.3.673. url: https://www.aeaweb.org/articles?id=10.1257/jel.49.3.673.

Romer, Paul (2016). The Trouble With Macroeconomics. Working Paper. Delivered as the Commons Memorial Lecture, January 5, 2016. Stern School of Business, New York University. url: https://paulromer.net/trouble- with- macroeconomics- update/WPTrouble.pdf.

Rudd, Jeremy B. (2024). “What Does Monetary Policy Do?” In: A Practical Guide to Macroeconomics. Cambridge University Press, pp. 193–206.

Sandel, Michael J. (Nov. 2013). “Market Reasoning as Moral Reasoning: Why Economists Should Re-engage with Political Philosophy”. In: Journal of Economic Perspectives 27.4, pp. 121–40. doi: 10.1257/jep.27.4.121. url: https://www.aeaweb.org/articles?id=10.1257/jep.27.4.121.

Shiller, Robert J. (Mar. 2003). “From Efficient Markets Theory to Behavioral Finance”. In: Journal of Economic Perspectives 17.1, pp. 83–104. doi: 10.1257/089533003321164967. url: https://www.aeaweb.org/articles?id=10.1257/089533003321164967.

Solow, Robert, Alan Budd, and Christian von Weizsacker (1987). “The Conservative Revolution: A Roundtable Discussion”. In: Economic Policy 2.5, pp. 181–200. issn: 02664658, 14680327. url: http://www.jstor.org/stable/1344624 (visited on 02/06/2026).

Stiglitz, Joseph (Mar. 1997). “Reflections on the Natural Rate Hypothesis”. In: Journal of Economic Perspectives 11.1, pp. 3–10. doi: 10 . 1257 / jep . 11 . 1 . 3. url: https : //www.aeaweb.org/articles?id=10.1257/jep.11.1.3.

Stiglitz, Joseph E. (2018). “Where modern macroeconomics went wrong”. In: Oxford Review of Economic Policy 34.1-2, pp. 70–106. doi: 10.1093/oxrep/grx057.

Stiglitz, Joseph E. and Ira Regmi (2022). The Causes of and Responses to Today’s Inflation 

Tobin, James (Mar. 1993). “Price Flexibility and Output Stability: An Old Keynesian View”. In: Journal of Economic Perspectives 7.1, pp. 45–65. doi: 10.1257/jep.7.1.45. url: https://www.aeaweb.org/articles?id=10.1257/jep.7.1.45.

U.S. Bureau of Labor Statistics (Jan. 2026). The Employment Situation – December 2025. https://www.bls.gov/news.release/pdf/empsit.pdf. Accessed 2026.

Williams, Jhacova (Sept. 2020). Laid Off More, Hired Less: Black Workers in the COVID19 Recession. Accessed: 2026-02-07. RAND Corporation. url: https://www.rand.org/pubs/commentary/2020/09/laid- off- more- hired- less- black- workers- in- thecovid.html.