Optimal fiscal policy under learning

Caprioli, Francesco

doi:10.1016/j.jedc.2015.05.008

Cited by 8 publications

(5 citation statements)

References 62 publications

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“…This argument is strongly supported by the fact that empirical evidence of agents' heterogeneity and limited cognitive abilities has been provided by using both laboratory and survey data (see Carroll, 2003;Branch, 2004;Pfajfar and Santoro, 2010;Froot, 1987, 1988;Hommes, 2011). Still, researchers have started incorporating behavioral economics elements in dynamic macro models only recently (see Driscoll and Holden, 2014), and this is especially true in relation to fiscal policy (see Evans et al, 2009Evans et al, , 2012Gasteiger and Shoujian, 2014;Caprioli, 2015;Gabaix, 2016). Furthermore, some contributions have highlighted how the linearity implied by rational expectations DSGE models makes them not fully suitable for fiscal policy analyses.…”

Section: Introductionmentioning

confidence: 99%

Animal Spirits and Fiscal Policy

Foresti

2020

Journal of Economic Behavior & Organization

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In this paper, we extend the behavioral macroeconomic model proposed by De Grauwe (2011) by including fiscal policy. In this model, agents have limited cognitive capabilities and use simple heuristics to forecast output and inflation. However, thanks to a learning mechanism, agents can revise their forecasting rule according to its performance. This feature produces endogenous and self-fulfilling waves of optimism and pessimism (animal spirits). This framework allows us to show that the short-run spending multiplier is state-dependent. The multiplier is stronger under either extreme optimism or pessimism and reduces in periods of tranquility. Furthermore, the more the central bank focuses on output stabilization, the smaller the multiplier. We also show that periods of increasing public debt are characterized by intense pessimism, while intense optimism occurs in periods of decreasing debt. This allows us to show that governments face a trade-off between the stabilization of the output gap and the stabilization of public debt. We also evaluate our model at the zero lower bound and find that the lower the inflation target, the more likely the system can be gripped in a deflationary spiral that is dynamically unstable and characterized by chronic pessimism and exploding public debt.

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Section: Introductionmentioning

confidence: 99%

Animal Spirits and Fiscal Policy

Foresti

2020

Journal of Economic Behavior & Organization

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“…The welfare gains to some generations in my model come from the same underlying feedback loop between saving and capital. In a somewhat similar vein, Caprioli (2015) explores the trade‐off between distortions from taxes and distortions from expectations. Caprioli shows that if agents form expectations using a learning algorithm, a benevolent planner could choose policy to manipulate expectations, reducing taxes and issuing debt at times of pessimism and doing the opposite at times of optimism.…”

Section: Policy Uncertaintymentioning

confidence: 99%

“…4. See also Honkapohja (2013, 2019), Gasteiger andZhang (2014), andCaprioli (2015). The adaptive learning model developed in this paper is a special case of Finite Horizon Learning in an overlapping generations model as developed in Cottle Hunt (2019), based on Branch, Evans, and McGough (2013).…”

Section: Householdsmentioning

confidence: 99%

Adaptive Learning, Social Security Reform, and Policy Uncertainty

Hunt¹

2021

J of Money Credit Banking

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I develop an adaptive learning model to study the welfare effects of Social Security policy uncertainty in an aging economy. Agents combine full knowledge of the political process (which Social Security reforms are possible and when they could occur) with limited knowledge about the structure of the economy. The adaptive learning amplifies cyclical dynamics along the transition path to the new steady state. This magnifies the welfare effects of policy uncertainty, compared to a standard rational expectations model. The ex ante consumption equivalent variation that equates the expected utility of consumption (with policy uncertainty) to the utility of expected consumption (across the possible policies without uncertainty) ranges between −0.29% and 0.21% of lifetime consumption in the adaptive learning model compared to −0.012% to −0.018% in the standard model. The welfare cost to future generations is also larger in the adaptive learning model compared to the rational model.

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“…The success of these technologies depends on their use by citizens who have serious health‐care needs. Similarly, in taxation policy, some adults may become more equal than others if they are more technology‐savvy and have the capacity to adjust their behavior if they know what formulas the government uses (Caprioli, ). More seriously, exclusion of—or discrimination against—marginalized populations can become embedded in predictive formulas that support the delivery of public services.…”

Section: Big Data Governance In Three Policy Areasmentioning

confidence: 99%

Big Data and Dahl’s Challenge of Democratic Governance

Ingrams

2019

Review of Policy Research

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Big data applications have been acclaimed as potentially transformative for the public sector. But, despite this acclaim, most theory of big data is narrowly focused around technocratic goals. The conceptual frameworks that situate big data within democratic governance systems recognizing the role of citizens are still missing. This paper explores the democratic governance impacts of big data in three policy areas using Robert Dahl’s dimensions of control and autonomy. Key impacts and potential tensions are highlighted. There is evidence of impacts on both dimensions, but the dimensions conflict as well as align in notable ways and focused policy efforts will be needed to find a balance.

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Optimal fiscal policy under learning

Cited by 8 publications

References 62 publications

Animal Spirits and Fiscal Policy

Animal Spirits and Fiscal Policy

Adaptive Learning, Social Security Reform, and Policy Uncertainty

Big Data and Dahl’s Challenge of Democratic Governance

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