QBF Workshop, Nice, December 8-10 2010
EURACE
Agent-based modeling for
macro-economy
policy design
Silvano CINCOTTI
University of Genoa - Italy
1
www.eurace.org
Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Summary
 
Economy as a complex system
 
The EURACE project
 
Technological solutions
 
Economic solutions
 
Policy design example
 
Final remarks
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Economies as
Complex Systems
 
 
 
 
Large efforts and interests have been
devoted to “Social” Complex Systems
Economies are Complex Social Adaptive
Systems where agents make forwardlooking decisions
There is a world economic crisis which
needs innovative ways of analyzing and
understanding the issues
Agent-based Computational Economics
(ACE) investigates how aggregate
outcomes arise from the micro-processes
of interactions among many agents
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Why the Agent-based Computational
Economics approach?
 
 
Well suited to study economies as complex adaptive
systems
Feedback from other disciplines
 
 
 
mathematics, physics, engineering, biology, computing science
and software engineering
Widespread dissatisfaction with the mainstream
approaches rooted on the representative agent
hypothesis and equilibrium theory
Tremendous development in the last 25 years
 
 
Joseph E. Stiglitz, Nobel Laureate 2001 in Economic Sciences,
pioneered heterogeneous economic agents by analysis of
markets with asymmetric information (Quarterly Journal of
Economics 1976)
Thomas Schelling, Nobel Laureate 2005 in Economic Sciences,
pioneered ACE presenting an agent-based model on racial
segregation (Micromotives and Macrobehavior 1978)
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EURACE Project Grand-Challenge
 
 
 
 
Constructing a software platform to
perform large-scale agent-based
simulations on high performance
computer
Developing a model of the European
economy, implemented on the simulation
platform.
Reproducing statistical regularities at the
macro-level.
Investigating/experimenting with
macroeconomic policy scenarios.
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
EURACE Context
 
 
 
1/3
Macroeconomic policy design plays a
fundamental role in social welfare and requires
a coordinated application of economic policy
measures
EURACE worked in the agent-based
computational economics framework
Economics is represented through
heterogeneous interacting agents
 
Actors
 
 
Markets
 
 
Households, Firms, Banks, Etc
Goods, Labour, Credit, Monetary, Assets, Etc
Policy Institutions
 
Central Bank, Government, Regulatory Bodies, Etc
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
EURACE Context
Regulatory
bodies
regulations
Central
bank
Firms
financing
labour
reserves
revenues
taxes
Government
2/3
interests
interests
Labour
market
taxes
Goods
market
Assets
market
Banks
savings
wages
public
spending
goods
Households
dividends,
interests
interests
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
EURACE Context
 
 
 
3/3
X-Agents are the essential software elements in
EURACE
To define an economic agent as a X-Agent, we
need to define:
  The markets on which the agent can be
active.
  The activities of the agent on each market.
  The decisions the agent has to make on
each market.
  The messages an agent can exchange.
The X-agent framework is adopted for modeling
economic systems and extended to run on
parallel clusters of computers
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Why is EURACE different ?
 
Its main distinctive and innovative features are:
 
 
 
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 
 
 
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its closure
the presence of real and financial markets
the wide use of empirically documented behavioural
rules
the different levels of time and space granularity
asynchronous decision-making across different agents
the explicit spatial structure
the very large number of agents
the use and development of innovative software
frameworks
User-transparent scalability on different computational
arcitectures
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
EURACE Objectives
 
From a scientific point of view
 
 
From a technological point of view
 
 
The study and the development of multi-agent models that
reproduce, at the aggregate economic level, the emergence
of global features as a self-organized process from the
complex pattern of interactions among heterogeneous
individuals
The development, with advanced software engineering
techniques, of a software platform in order to realize a
powerful environment for large-scale agent-based economic
simulations
From a societal point of view
 
Outstanding impact on the economic policy design
capabilities, allowing “what-if” analysis in order to optimize
the impact of regulatory decisions that will be quantitatively
based on European economy scenarios
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QBF Workshop, Nice, December 8-10 2010
EURACE
EURACE
Technological solutions
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Research challenges faced
 
The Agent-based modelling framework
required the following features:
 
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 
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 
Ability to run many millions of complex agents
Should run on any supercomputer or desktop
(Linux, Mac, Windows)
Allows economists to design models with no
knowledge of programming
Strong testing techniques to assure quality – most
large scientific software has serious bugs
Ability to combine different models sharing agents
in a coherent and correct way
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Basic philosophy of FLAME
 
Agents defined as communicating Xmachines
 
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 
General computational model
Intuitive and easy to use in many
different applications
 
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X-machine concept introduced by Eilenberg
[1974] and then ignored until recently
Economics, biology, management, sociology
Messages sent to message boards
Agents are distributed across processors
using new techniques
Efficient implementation utilises the
available hardware – serial or parallel
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
What agents do?
 
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They have a memory – it contains their
identity, location, other information such
as their assets, employment state etc.
They have a state which determines
what they can do next
They can read messages sent to them
They can write messages to other agents
They can carry out other operations if
these are permitted – accept job offer,
buy shares, apply for credit etc.
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The X-machine
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
FLAME Block Diagram
X parser
files
make
Libmboard
Model.xml
Xparser.exe
Main.exe
Functions.c
1-N
Xml files
0.xml
Your files
Xparser files
www.eurace.org
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How do you define agents?
 
Use XMML – e.g.
www.eurace.org
etc.
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Firm in labour market –
state table
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
A firm’s functions
 
Firm send vacancies. If additional workers are needed the
firm sends vacancies messages
 
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especially the different wage offers for the different general skill
groups.
Firm send redundancies. If the firm wants to decrease the
workforce it sends redundancies.
Firm idle. Firm does nothing.
Firm reads job applications sends job offer or rejection.
Firm reads the application, ranks the applicants according
to their general and specific skills and sends as many job
offers to the first ranked applicants as the firm has
vacancies to fill. The other applicants are refused.
Firm read job responses. The firm reads the responses to
their job offers and updates the number of employees
and the number of vacancies.
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Firm messages
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Parallel implementation
Parallel agents grouped on
parallel nodes.
Messages synchronised
Message board library
allows both serial and
parallel versions to work
Implementation details hidden
from modellers
System automatically manages
the simulation
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C. Greenhough, D. Worth
www.eurace.org
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
QBF Workshop, Nice, December 8-10 2010
EURACE
EURACE
Economic solutions
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
A snapshot of EURACE
 
The EURACE model represents a fully
integrated macro-economy consisting of:
 
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 
the real sector (production of consumption and
capital goods with labor, capital goods and
energy as factors of production and relative
markets; technological innovation);
the credit sector (financing production plans of
firms);
the financial sector (exchange of claims on the
equity capital of producers as well as of
governments liabilities);
the public sector (policy making, i.e., fiscal
policy made by Governments and monetary
policy set by the Central Bank).
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Structure of the EURACE Model
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Key features of EURACE
 
 
 
 
Technology (FLAME, GUIs, parallelization)
Spatial structure and local interactions
Realistic time scales and asynchronous interactions
Decentralized markets (Walrasian auctioneer banned
expect for the financial market):
 
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 
market clearing is not for granted
no law of one price
Adaptive and empirically grounded behavioral rules
(optimization banned)
Balance sheet approach in modeling agents
Validation based on the reproducibility of well-known
empirical regularities and the consistency of balance
sheets
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Building Blocks: Agents
 
 
 
Households
 
Buyer on Consumption Goods Market
 
Seller on Labor Market
 
Deposits at banks
 
Buyer/Seller of firm shares and government bonds
Consumption Goods Producers
 
Buyer on Labor and Investment Goods Markets
 
Seller on Consumption Goods Markets
 
Loans from banks
 
Distributes dividends to shareholders
Investment Goods Producer
 
Buyer on Labor Market
 
Seller on Investment Goods Market
 
Loans from banks
 
Distributes dividends to shareholders
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Building Blocks: Agents
 
Commercial Banks
 
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Government
 
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 
 
 
Collect deposits from Households
Give loans to firms
Access standing facilities of the central bank
Decides fiscal policies
Receives taxes
Pays benefits
Issues bonds
Central bank
 
Sets monetary policies
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Building Blocks: Markets
 
Consumption Goods Markets
 
 
 
Consumption goods producers offer (and
store) goods at selected geographically
distributed local market outlets
Consumers visit their local outlets and
make purchasing decisions based on
price/quality information they collect
about the goods offered at the outlets
Suppliers on the consumption goods
market act globally (without spatial
frictions) whereas consumers buy locally
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Building Blocks: Markets
 
Labor Market
 
 
 
 
 
Firms post job vacancies based on planned
output.
Searching workers send applications based on
posted salaries.
Firms rank applications based on skills and
make offers.
Workers rank offers (wage - commuting costs),
compare best offer to their reservation wage and
accept/reject.
Labor Market is global with spatial frictions
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Building Blocks: Markets
 
Financial Market
 
 
Households trade stocks and bonds
Households strategy are based on
prospect theory
 
Firms issue or buyback stocks
 
Governments issue T-bills when needed
 
Assets prices are set by a clearing house
 
Financial Market is global
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Building Blocks: Decision Rules
 
Strong micro-foundation of decision rules:
 
Firms and Households act rule-based using backward
looking expectations.
 
Households decisions in the financial market are based
on prospect theory
 
 
 
Operational decisions of firms are modelled using
standard decision rules from the Operations Management
literature:
  Pricing (markup)
  Inventory and Production Planing
Savings/consumption decisions of household are based
on empirically-founded rules derived from the bufferstock theory of consumption, see Deaton (1991) and
Carrol (1993)
Purchasing Decisions of Households are modelled using
standard logit-models from the Marketing literature
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The balance sheet approach
 
 
 
 
A double-entry balance sheet with a detailed account of
all monetary and real assets as well as monetary
liabilities is defined for each agent.
Monetary and real flows given by agents' behaviors and
interactions determine the period by period balance sheet
dynamics.
A stock-flow model is then created and used to check
that all monetary and real flows are accounted for, and
that all changes to stock variables are consistent with
these flows.
This provides us with a solid and economically wellfounded methodology to test the consistency of the
model and it increases the credibility that can be attached
to the model's results
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Household (H): balance sheet
overview
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Firm (f): balance sheet
overview
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Bank (b): balance sheet
overview
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Government (g)
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Central Bank (c): balance
sheet overview
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Verification rules
 
 
Balance sheet accounting identities can be
devised across agents and used to test the
model and validate implementation
Examples:
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…also at aggregate level
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Strategy for the Analysis of Model
Dynamics and Policy Experiments
 
Calibration of key parameters using empirical
data and checking ability of the model to
reproduce ‘stylized facts’.
 
Running simulation batches for different policy
interventions.
 
Formulating hypotheses about the effect of
policy induced parameter changes.
 
Carrying out statistical tests to check
significance of the hypothesized effects.
 
Gaining a qualitative understanding of the
relevant economic mechanisms responsible for
the observed phenomena by examining
evolution of key variables.
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QBF Workshop, Nice, December 8-10 2010
EURACE
EURACE
Policy design example
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Economic Policy Issues
 
Explore effects of different types of policy interventions
on economic growth and stabilization
 
 
 
 
 
Fiscal policy
Monetary policy
Education policy (general skills)
Innovation policy (public R&D, diffusion)
Labor market policy
 
Explore interaction between different types of policies.
 
Consider heterogeneities on a regional and on an
individual level.
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
A policy experiment: Spatial allocation of
economic policy measures
 
 
 
Consider issue of spatial allocation of economic
policy measures.
Different regions are characterized by different
distributions of agents‘ characteristics.
Effect of a certain policy measure depends on
characterisitics of neighbouring regions and
policies implemented in these regions.
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Lighttower vs. Equal Treatment
 
 
 
 
Economy with 2 regions.
Both regions characterized by low general skill
distribution of workers.
Government intends to allocate certain funds to
improve workers’ general skills.
2 Options:
 
 
Equal Treatment: improve average general
skills of in both regions to medium level.
Lighttower Policy: No investment in region 1,
but general skills of majority in region 2 is
increased to high level.
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Lighttower vs. Equal Treatment
 
 
How do the policy effects differ between
the equal treatment and the lighttower
scenario?
How are the effects of the different
policies affected by the size of the spatial
frictions on the labor market (workers
commuting costs)?
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Output (av. over 50 runs)
- lighttower
- equal treatm.
- lighttower
- equal treatm.
- no treatm.
- no treatm.
comm = 0
comm = 0.05
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Output in lighttower scenario
- high skill reg.
- low skill reg.
- total
comm = 0
- high skill reg.
- low skill reg.
- total
comm = 0.05
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Capital stock in lighttower scenario
- high skill reg.
- low skill reg.
comm = 0
- high skill reg.
- low skill reg.
comm = 0.05
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Labor income in lighttower scenario
- high skill reg.
- low skill reg.
comm = 0
- high skill reg.
- low skill reg.
comm = 0.05
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Remarks
 
 
EURACE model capture interplay of
technological change and spatial skill dynamics.
Effects of regional skills differences are strongly
influenced by the interaction of the mobility of
consumption goods and the mobility of labor
(commuting costs).
 
Reduction of spatial frictions does not
necessarily increase total output!
 
Spatial distribution of policy measures is
important!
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
Another policy experiment:
fiscal and monetary policies
 
These computational experiments aims to
investigate the overall performance of the
EURACE economy with respect to two different
and alternative fiscal and monetary policies:
 
 
 
Quantitative easing policy (QE), (Central Bank
buys unsold government bonds)
No quantitative easing, but endogenous fiscal
tightening policy (FT)
The results may provide insights for designing
suitable policies in the European economic
scenario, where monetary authorities are
implementing quantitative easing monetary
policies
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Overview of the two policies
 
Fiscal tightening policy (FT)
 
 
 
it pursues a zero government budget deficit objective
by increasing tax rates if necessary.
the budget deficit, if any, is funded by both the
increase of taxes and the issue of new government
bonds which are sold in the market.
Quantitative easing policy (QE)
 
 
 
the zero government budget deficit is NOT an issue.
Tax rates are then maintained at a low constant level.
the budget deficit, if any, is funded just by the issue
of new government bonds which are sold directly in
the secondary market.
The Central Bank participate in the secondary bond
market to buy an amount of gov bonds equal to the
new issue.
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Government budget items
 
Revenues:
 
 
Expenses:
 
 
 
taxes on corporate profits and household labor and
capital income
unemployment benefits
Interests on debt
Note:
 
 
we define government liquidity as the cumulated
budget surplus
the government bond is an infinite maturity bond with
constant coupon
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Policy actions
 
FT policy:
 
 
QE policy:
 
 
the government raises the tax rates on a yearly
basis if its present liquidity summed to past year
budget deficit is negative.
the central bank buys government bonds in the
secondary market. Therefore, new money (fiat
money) is created from nothing and injected into
the economy
Reference context
 
Different levels of firms financial fragility by
fixing exogenously the ratio (d) of earnings that
firms pay out as dividends
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Key Real Variables (I)
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Key Real Variables (II)
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d
policy
Physical
capital
growth rate
(%)
Real GDP
growth rate
(%)
Unemployment
rate (%)
0.6
FT
QE
0.140 (0.016)
0.243 (0.060)
0.023 (0.020)
0.058 (0.057)
19.9 (2.5)
10.86 (0.28)
0.7
FT
QE
0.159 (0.022)
0.264 (0.067)
0.040 (0.022)
0.087 (0.038)
19.2 (3.5)
10.28 (0.27)
0.8
FT
QE
0.221 (0.033)
0.234 (0.040)
0.062 (0.026)
0.031 (0.027)
15.7 (2.4)
9.94 (0.36)
0.9
FT
QE
0.299 (0.055)
0.288 (0.065)
0.070 (0.040)
0.054 (0.033)
14.1 (2.4)
8.30 (0.75)
Values report the ensemble averages and standard deviations (within round
brackets) over six different simulation runs of mean monthly rates. Each run is
characterized by a different random seed. For each simulation run, mean
monthly rates are computed over the entire simulation period, except for the
first 12 months which have been considered as a transient and discarded.
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Remarks
 
 
EURACE economy is able to reproduce
endogenous short-term fluctuations (business
cycles) as well as long-run growth
Short-term fluctuations are caused by
 
 
 
 
 
coordination failure between demand and supply of
consumption goods.
fluctuations in investment in physical capital
firms bankruptcies, i.e. disruptions in the supply chain
Long-run growth is given by the growth of
physical capital as well as labor productivity
Long-run growth QE outperforms FT for small d
 
with large d, firms access more frequently the credit
market and the larger availability of money
compensates the fiscal pressure, i.e., credit money
counterbalances fiat money
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Key Real Variables (I)
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Key Real Variables (II)
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d
policy
Physical
capital
growth rate
(%)
Real GDP
growth rate
(%)
Unemployment
rate (%)
0.6
FT
QE
0.140 (0.016)
0.243 (0.060)
0.023 (0.020)
0.058 (0.057)
19.9 (2.5)
10.86 (0.28)
0.7
FT
QE
0.159 (0.022)
0.264 (0.067)
0.040 (0.022)
0.087 (0.038)
19.2 (3.5)
10.28 (0.27)
0.8
FT
QE
0.221 (0.033)
0.234 (0.040)
0.062 (0.026)
0.031 (0.027)
15.7 (2.4)
9.94 (0.36)
0.9
FT
QE
0.299 (0.055)
0.288 (0.065)
0.070 (0.040)
0.054 (0.033)
14.1 (2.4)
8.30 (0.75)
Values report the ensemble averages and standard deviations (within round
brackets) over six different simulation runs of mean monthly rates. Each run is
characterized by a different random seed. For each simulation run, mean
monthly rates are computed over the entire simulation period, except for the
first 12 months which have been considered as a transient and discarded.
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Remarks
 
 
EURACE economy is able to reproduce
endogenous short-term fluctuations (business
cycles) as well as long-run growth
Short-term fluctuations are caused by
 
 
 
 
 
coordination failure between demand and supply of
consumption goods.
fluctuations in investment in physical capital
firms bankruptcies, i.e. disruptions in the supply chain
Long-run growth is given by the growth of
physical capital as well as labor productivity
Long-run growth QE outperforms FT for small d
 
with large d, firms access more frequently the credit
market and the larger availability of money
compensates the fiscal pressure, i.e., credit money
counterbalances fiat money
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Key nominal variables
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d
policy Private sector
money
endowment
growth rate
(%)
price inflation
rate (%)
wage inflation
rate (%)
0.6
FT
QE
-0.357 (0.053)
-0.350 (0.094)
-0.045 (0.010)
0.011 (0.042)
0.0091 (0.0031)
0.109 (0.059)
0.7
FT
QE
-0.274 (0.053)
-0.228 (0.086)
-0.0346 (0.0099)
0.021 (0.041)
0.017 (0.014)
0.104 (0.069)
0.8
FT
QE
-0.091 (0.070)
-0.113 (0.089)
0.004 (0.024)
0.024 (0.023)
0.043 (0.029)
0.072 (0.049)
0.9
FT
QE
0.13 (0.10)
0.183 (0.098)
0.105 (0.054)
0.144 (0.062)
0.134 (0.057)
0.172 (0.053)
Values report the ensemble averages and standard deviations (within round
brackets) over six different simulation runs of mean monthly rates. Each run is
characterized by a different random seed. For each simulation run, mean monthly
rates are computed over the entire simulation period, except for the first 12 months
which have been considered as a transient and discarded.
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Banks data
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Government data
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Remarks 1/2
 
Clear interdependence between fluctuations in real and
monetary variables.
 
 
 
cross-correlation between private sector money endowment
rate with GDP and price index rates
The credit money supplied by the banking system is the
source, together with the fiat money supplied by the
central bank, of the endowment of liquid resources held
by both the private sector (households, firms and banks)
and the public sector (government and central bank).
An increase (higher d) in the demand for credit by firms,
if supplied by banks, then increases the amount of liquid
resources in the economy.
 
it seems that fluctuations in real variables somewhat lead the
ones in nominal variables.
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Remarks 2/2
 
 
Higher inflation and wage rates are associated
to higher values of d
Higher inflation rates for higher values of d can
not be directly explained according to the
quantity theory of money, i.e. due to the higher
amount of liquidity in the economy
 
 
prices are not set by a fictitious Walrasian auctioneer
at the cross between demand and supply, but are set
by firms, based on their costs, which are labor costs,
capital costs and debt financing costs
Higher credit money means higher debt and
higher debt financing costs, thus again higher
price inflation through the cost channel
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EURACE
Final Remarks
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EURACE has produced….
 
 
 
 
Fundamental advances in computational
methodology, allowing for the treatment of very
large models, for a flexible and realistic treatment
of time, for the easy inclusion of spatial and
evolving network features
Advances in the modeling of a concrete complete
economy, important for future work by the
EURACE team and other researchers
Experience and guidelines on how to
  model specific markets
  integrate the markets
  treat the data generated by the model
Common language and understanding among
economists and computer scientists
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What about the crisis?
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…but something is changing
http://www.ecb.int/press/key/date/2010/html/sp101118.en.html
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…but something is changing…
 
 
 
 
“….First, we have to think about how to characterise the
homo economicus at the heart of any model…We need to
deal better with heterogeneity across agents and the
interaction among those heterogeneous agents.”
“…Second, we may need to consider a richer
characterisation of expectation formation. Rational
expectations theory has brought macroeconomic analysis
a long way over the past four decades. But there is a
clear need to re-examine this assumption.”
“…Third, we need to better integrate the crucial role
played by the financial system into our macroeconomic
models”
“….In this context, I would very much welcome
inspiration from other disciplines: physics, engineering,
psychology, biology. Bringing experts from these fields
together with economists and central bankers is
potentially very creative and valuable…”
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Take home message
 
Agent-based technologies provide a
fruitful, promising and usable
approach to address complex
problems of market design and
policy analysis
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Final Note
EURACE project (EU IST FP6 STREP grant: 035086)
has been developed within the part of the IST-FET
proactive initiative “Simulating Emergent Properties
in Complex Systems” IST-2005-2.3.4 (xi)
Special thanks to M. Raberto and A. Teglio
For further information
[email protected]
www.eurace.org
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Quantitative Behavioral Finance, CIF–NICE, December 8-10 2010
EURACE Consortium
Institution
Country
Unit Head
Competences
Role
University of Genoa
Italy
Silvano Cincotti
Agent-based computational
economics and software
engineering. Economic
policy design
Coordinator
University of Bielefeld
Germany
Herbert Dawid
Agent-based computational
economics. Economic policy
design
Partner
Université de la
Méditerranée
France
Christophe Deissenberg
Agent-based computational
economics. Economic policy
design
Partner
TUBITAK-UEKEA
Turkey
Kaan Erkan
Software engineering
Partner
University of Ancona
Italy
Mauro Gallegati
Agent-based computational
economics. Economic policy
design
Partner
University of Sheffield
UK
Mike Holcombe
Software engineering and
computer science
Partner
University of Cagliari
Italy
Michele Marchesi
Software engineering
Partner
Rutherford Appleton
Laboratory - STFC
UK
Christopher Greenough
Computer science
Partner
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Monetary and financial assets
 
 
 
 
 
cash holdings in the form of commercial
bank or central bank deposits. There is
no cash hoarding since all money is held
inside the banking sector;
bank loans
central bank standing facility
government bonds
equity shares (issued by firms and
banks)
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Real assets
 
 
 
firms inventories
physical capital
human capital
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Monetary aggregates and
policy considerations
 
 
For policy considerations, it is clearly important
to consider the monetary endowment of agents
in the private sector, i.e.,
An higher monetary endowment due, e.g., to a
loose fiscal policy and QE, leads to a higher
nominal demand that not necessarily translates
into a higher real demand. It depends on the
behavior of prices
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Computational setting
 
 
 
1,000 households, 10 firms, 1 IGfirm, 2 banks
20 years of simulation
Different levels of firms financial
fragility have been considered by
fixing exogenously the ratio (d) of
earnings that firms pay out as
dividends
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Possible economic policy
analysis in current EURACE
 
 
 
effects of economic policy measures in a
micro-founded model that encompasses
many realistic features
effects of economic policy measures in
an economy with spatial structure
consisting of a number of regions
interaction effects of policy measures of
different types that are typically studied
in isolation in mainstream analytical
models
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