Heterogeneous Agent Model Research Articles

Present Bias Amplifies the Household Balance-Sheet Channels of Macroeconomic Policy

Abstract We study the effect of monetary and fiscal policy in a heterogeneous-agent model where households have present-biased time preferences and naive beliefs. The model features a liquid asset and illiquid home equity, which households can use as collateral for borrowing. Because present bias substantially increases households’ marginal propensity to consume (MPC), present bias increases the effect of fiscal policy. Present bias also amplifies the effect of monetary policy, but at the same time, slows down the speed of monetary transmission. Interest rate cuts incentivize households to conduct cash-out refinances, which become targeted liquidity injections to high-MPC households. Present bias also introduces a motive for households to procrastinate refinancing their mortgages, which slows down the speed with which this monetary channel operates.

Endogenous cycles in heterogeneous agent models: a state-space approach

Endogenous cycles in heterogeneous agent models: a state-space approach

A multi-agent motion simulation method for emergency scenario deduction

Simulating crowd motion in emergency scenarios remains a challenge in computer graphics due to crowd heterogeneity and environmental complexity. However, existing crowd simulation methods homogenize the agent model and simplify target selection and motion navigation of emergency crowds. To address these problems, we propose a multi-agent motion simulation method for emergency scenario deduction. First, we propose a multi-agent model to simulate crowd heterogeneity. This model includes a personality-based heterogeneous agent model and an agent perception model that considers vision, hearing, and familiarity with the environment. Second, we propose a target selection strategy based on the motion patterns of actual pedestrians. This strategy employs mathematical models and our agent perception model to guide agents in selecting appropriate targets. Finally, we propose a global navigation algorithm that combines random sampling with heuristic search methods. Concurrently, we use our multi-agent model to adjust the agent’s local motion planning to deduce the motion states of emergency crowds naturally. Experimental results validate that our method can realistically and reasonably simulate crowd motion in emergency scenarios.

An endogenous evolution mechanism model of asset prices based on time-varying risk aversion coefficient

In the traditional heterogeneous agent model, investors are assumed to be risk averse, and the wealth expected utility function maximization principle is used to form the optimal asset quantity demand. In such models, the risk aversion coefficient of investors is often assumed to be constant. This paper considers that the risk aversion coefficient of investors is time-varying and changes with the change of wealth, and establishes an endogenous evolutionary mechanism model formed by fundamental analysts, technical analysts, and market makers. Compared with the fixed risk aversion coefficient model, this paper analyzes the investor’s behavior, the interaction between investor behaviors, and the influence of different types of investors on the stability of the market. At the same time, we test asset price and asset behavior and conclude that investor behavior affects the stability of the system model. The numerical simulation of the corresponding stochastic model shows that the model can simulate the basic characteristics of financial time series, such as the partial peak and thick tail of asset return series, and the long memory of fluctuations.

The Tyranny of the Ribbon: The Effects of Prioritizing New Infrastructure over Maintenance

In the allocation of resources toward public infrastructure, spending on maintenance is typically neglected. Using a heterogeneous agent model, we find that reallocating funding away from new infrastructure investment and toward maintenance raises the effectiveness of existing public infrastructure and thus yields gains in GDP and in aggregate welfare. Richer households experience larger welfare gains than poorer households. A reallocation that takes funding away from maintenance is found to have adverse effects on GDP and aggregate welfare. Raising new funding with taxation yields larger positive benefits if this additional spending is divided between maintenance expenditures and investment in new infrastructure. Giving in to the “Tyranny of the Ribbon” by policymakers, that is, raising new funding to be spent only on new infrastructure, is shown to be the worse option.

Exchange rate stability and expectation management under heterogeneous expectations

This study develops a heterogeneous agent model comprising fundamentalists, chartists, carry traders, and stabilizers to determine the RMB exchange rate. The stabilizers act as agents of the central bank and relevant government agencies to manage expectations when the exchange rate appreciates or depreciates excessively. Using the unscented Kalman filter method, our study finds that the principal parameters of the heterogeneous agent model are statistically and economically significant, and the heterogeneous agent model can adequately characterize the main features of expectation management. Furthermore, the stabilizers can significantly reduce excess volatility and promote market exchange rates to their fundamental values. Additionally, the timing of the stabilizers' actions is consistent with that of the central bank's implementation of expectation management.

Cooperative target allocation for heterogeneous agent models using a matrix-encoding genetic algorithm

Heterogeneous platforms collaborate to execute tasks through different operational models, resulting in the task allocation problem that incorporates different agent models. In this paper, we address the problem of cooperative target allocation for heterogeneous agent models, where we design the task-agent matching model and the multi-agent routing model. Since the heterogeneity and cooperativity of agent models lead to a coupled allocation problem, we propose a matrix-encoding genetic algorithm (MEGA) to plan reliable allocation schemes. Specifically, an integer matrix encoding is resorted to represent the priority between targets and agents in MEGA and a ranking rule is designed to decode the priority matrix. Based on the proposed encoding-decoding framework, we use the discrete and continuous optimization operators to update the target-agent match pairs and task execution orders. In addition, to adaptively balance the diversity and intensification of the population, a dynamical supplement strategy based on Hamming distance is proposed. This strategy adds individuals with different diversity and fitness at different stages of the optimization process. Finally, simulation experiments show that MEGA algorithm outperforms the conventional target allocation algorithms in the heterogeneous agent scenario.

A Macro Study of the Unequal Effects of Climate Change

This paper develops a macro heterogeneous-agent model to quantify the distributional impacts of higher temperatures in the US. Households adapt to temperature by using energy and equipment for heating and cooling. A key insight is that temperature acts as a transfer from nature, augmenting household income by the value of heating or cooling provided by nature. The welfare effects of climate change vary substantially with income, increasing welfare inequality in the colder parts of the US. This heterogeneity results from the effects of climate change on transfers from nature and on households’ extensive-margin decisions to purchase heaters and air conditioners.

Wealth inequality and the distributional effects of maximum loan-to-value ratio policy

Macroprudential policy improves economic outcomes by reducing the likelihood and severity of financial crises. Yet, are there unintended long run consequences to the introduction of a macroprudential policy regime, and are they conditional on the level of wealth inequality? I answer these questions by looking at the effect of a reduction in the maximum loan-to-value (LTV) ratio on homeownership rates, house prices and housing wealth inequality across two economies with different initial wealth dispersion. I use a heterogeneous agent model in which households face an endogenous borrowing limit in the form of a collateral constraint. A reduction in the LTV limit tightens the borrowing constraint and changes the levels of wealth at which households rent or become constrained or unconstrained owners. The key finding of this paper is that initial conditions matter; a greater share of households is affected when wealth is distributed more equally. This larger impact on aggregate demand leads to a stronger fall in house prices and a larger rise in the share of constrained homeowners and housing wealth inequality. The effects are also non-linear in the LTV ratio, with progressively stronger effects at lower LTV ratios.

Credit easing, income gap, and housing price fluctuation

The COVID-19 pandemic has caused a global economic recession, forcing several countries to adopt credit easing policies. This study examines the impact of credit easing on housing price fluctuations from an income gap perspective. Through the construction and analysis of a heterogeneous agent model, we reveal that credit easing promotes the rise of house prices and a positive correlation exists between the income gap and the effect of credit easing on house prices. Additionally, credit easing substantially enhances the housing demand of low-income people, increasing debt and house prices. The analysis using panel data from 36 countries from 1970 to 2018 confirms that compared to rising income for high-income people, a decline in the income level of low-income people significantly increases credit easing effect on house prices.

Optimal unemployment insurance in a THANK model

We characterize optimal unemployment insurance (UI) in a heterogeneous-agent model with unemployment risk and sticky prices. In the long run, the optimal reform calls for a lower replacement rate that raises vacancies and lowers unemployment. In the short run, the optimal reform raises the replacement rate initially to smooth real wage adjustments along the transition and attenuate short-run welfare losses. Once at its optimal level, the replacement rate should vary counter-cyclically in response to demand shocks. Productivity shocks generate quasi-efficient fluctuations and call for a quasi-constant replacement rate. The aggregate welfare gains from an optimal reform are large, around 1% of equivalent consumption. The aggregate welfare gains from an optimal UI policy over the business cycle are smaller, around 0.2%, and essentially vanish with flexible prices because the aggregate demand stabilization motive is muted.

Household Consumption and Dispersed Information

By introducing an information friction to a heterogeneous agent model, we are able to explain two patterns of small economies experiencing large income changes: (1) excess volatility in consumption and (2) household consumption elasticities that have low correlation with income. With a standard dispersed information structure, households cannot distinguish aggregate income shocks from idiosyncratic ones. Their consumption responds excessively to aggregate shocks, which they incorrectly forecast to be too persistent. This effect occurs homogeneously across the income distribution, lowering the correlation of the consumption elasticity with income. We corroborate our central mechanism using survey data on household expectations of their future earnings.

Pseudospectral methods for continuous-time heterogeneous-agent models

We propose a pseudospectral method to solve heterogeneous-agent models in continuous time. The solution is approximated as a sum of smooth global basis functions, in our case polynomials represented by their values at Chebyshev nodes. We illustrate the method by applying it to a Krusell-Smith model. It solves the differential equations characterizing the steady-state efficiently and precisely, despite using only very few nodes. System dynamics are then automatically differentiated to simulate a linearized model. The full solution takes a third of a second and only uses standard software. A benchmark against finite differences shows that pseudospectral methods achieve far greater precision for a given number of nodes and for a given runtime. We conclude by discussing the methods' applicability, which is promising for smooth multi-dimensional models.

Inefficient Automation

Abstract How should the government respond to automation? We study this question in a heterogeneous agent model that takes worker displacement seriously. We recognize that displaced workers face two frictions in practice: reallocation is slow and borrowing is limited. We analyze a second best problem where the government can tax automation but lacks redistributive tools to fully alleviate borrowing frictions. The equilibrium is (constrained) inefficient and automation is excessive. Firms do not internalize that automation depresses the income of automated workers early on during the transition, precisely when they become borrowing constrained. The government finds it optimal to slow down automation on efficiency grounds, even when it does not value equity. Quantitatively, the optimal speed of automation is considerably lower than at the laissez-faire. The optimal policy improves efficiency and delivers meaningful welfare gains.

Heuristic decision-making and behavioral heterogeneity in the Chinese stock market

ABSTRACT This paper applies a heuristic decision-making approach to a heterogeneous agent model (HAM) with two types of investors and use the heuristic HAM to investigate excess volatility in the Chinese stock market. In this heuristic HAM, we use simple moving averages instead of complex capital asset pricing models to assess the benchmark fundamentals which play an essential role in valuing the excess volatility of financial markets. We test the model using historical observations of China securities index 300. Our estimation results show that the model can replicate observed price dynamics and the estimated market sentiment matches the booms and busts well. Our analysis of the most volatile episodes, in particular, the 2008 financial crisis, the 2015 Chinese stock market crash, and the COVID-19 epidemic, further corroborates the existence of behavioural heterogeneity in the Chinese stock market. This work offers several implications for assessing the risk in financial markets and measuring the stability of the financial system.

Building the city under financial frictions

Weak financial institutions may affect developing countries due to slowing the much-needed construction process of residential housing. Using novel data collected from Nairobi, I document considerable heterogeneity in the construction duration of new residential buildings, with about 40% of buildings started in 2009 still unfinished in 2018. To understand the role of financial development in constructing residential housing, I develop a heterogeneous agent model with financial frictions in which households construct individual housing units. Counterfactual simulations show that improvements in credit provision can substantially speed up the expansion of the aggregate housing stock and increase the city's density by enabling the construction of taller buildings. The model also predicts that investments in incomplete structures emerge as an alternative savings vehicle in the absence of reliable savings accounts.

Heterogeneity of agents, transaction costs and oil price dynamics

ABSTRACT In this study, we model the dynamics of oil prices based on a dynamic heterogeneous agent model. Two types of agents including fundamentalists and chartists trade in the market, and the transaction costs are taken into consideration. The empirical results show that it is useful to improve forecasting performance if the model allows the agents to learn from their past performance and considers the transaction costs. Examination of the agents’ dynamic weights shows that fundamentalists and chartists co-existed in the oil market with almost equal weights, and the fundamentalists dominated the market when the oil prices had large deviations from the fundamental level.

Unveiling the impact of income taxes on inequality in a HACT model

I introduce a tax shock into a “standard” heterogeneous agent model in continuous time (HACT) to quantify the effect of an income tax on inequality. I find that an income tax, collecting 15% of output, reduces the Gini coefficient by up to 16.9% in an economy with a perfect credit market and up to 24.3% in financial autarky. The tax has a modest effect on production labor income inequality, reduces inequality in entrepreneurial income under financial autarky, but raises it when entrepreneurs operate in a perfect credit market. I also explore the effect of the tax on other well-known income inequality measures discussed in the literature.

Household Debt and Borrower-Based Measures in Finland: Insights from a Heterogeneous Agent Model

Household Debt and Borrower-Based Measures in Finland: Insights from a Heterogeneous Agent Model

Schooling, Skill Demand, and Differential Fertility in the Process of Structural Transformation

Demography and structural transformation are interrelated, and depend critically on education. At the turn of the twentieth century, US parents began having fewer children while increasing educational investment per child. This quantity-quality tradeoff facilitated job reallocation from the low-skilled agricultural sector to the high-skilled nonagricultural sector. This transformation is examined in a heterogeneous agent model with a nondegenerate human capital distribution, focusing on how fertility and education decisions affect structural transformation. The result shows that the quantity-quality decisions account for up to approximately one-third of the decline in the agricultural employment share. (JEL E24, I21, I26, J11, J13, J24, N31)