[PPT] - Outline 1 Introduction and motivation 2 The stock flow PowerPoint Presentation

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#WorldInCommon

AGENCE FRANÇAISE DE DÉVELOPPEMENT | FRENCH DEVELOPMENT AGENCY

GEMMES RESEARCH PROJECT

An example: Coping With Collapse

March 7, 2019 Florent Mc Isaac–mcisaacf@afd.fr French Development Agency–Agence Française de Développement

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Outline

1 Introduction and motivation 2 The stock flow consistent approach 3 Coping with Collapse

The macroeconomic core The climate module Scope of analysis Scenario analysis

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GEMMES research program

Modeling as a tool for public policy dialogue

The "General Monetary and Multisectoral Macrodynamics for the Ecolog-

ical Shift" (GEMMES) project aims to contribute to national and interna- tional debate around the transition to a low-carbon economy.

To do so, we build a sequence of models encompassing the relevant dy-

namics in partnership with local institutions (ministries and academia) as well as international partners (World Bank, ILO, think tanks).

Modeling structures the debate by:

◮ feeding from quantitative and qualitative approaches ◮ allowing for model comparison

Hence modeling needs

◮ to be descriptive and not normative ◮ to be a tool for analysis and not the response ◮ to be pluralistic and multidisciplinary and not monolithic

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Fundamental vision behind the GEMMES project

1. Climate change requires an immediate response.
2. The low-carbon transition implies de facto a socio-economic restructura-

tion of our economies.

3. Finance-climate-economy interactions are non-trivial and are thus to be

accounted for.

4. The public sector has a fundamental role to play to steer and shape the

transition, in coordination with the private sector.

5. We take a holistic approach to sustainability, that is financial, environmen-

tal and social.

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Outline

1 Introduction and motivation 2 The stock flow consistent approach 3 Coping with Collapse

The macroeconomic core The climate module Scope of analysis Scenario analysis

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Money and finance misrepresentation: implications

Failing to understand the endogenous nature of bank money leads to un- derestimate the sources of financial instability and the consequences of shocks hitting the banking sector. [Benes et al., 2014] Failing to understand the nature and functioning of public money leads to log- ically inconsistent conclusions about fiscal and macro-prudential poli- cies. SFC models can help to provide a fully integrated picture of the real and financial economy, tackling the endogenous nature of money.

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Stock-flow modeling framework

In a monetary economy, sectors are interconnected through multi-layered bal- ance sheets. Therefore, a modeling framework based on:

A rigorous accounting framework, based Copeland’s (1949) quadruple

entry system.

A complex economic system with consistent feedback-loop between real

and financial spheres

Possible economically unstable emergent trajectories.

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Quadruple entry - Example of public spending financed

directly by the central bank

HH Prod. Banks Gov. C.B. Sum

Dep. Gov.

+G

G

GDP +Y

Y

∆M0

∆Hb
∆Hg

+∆H ∆Dep

∆Mh
∆Mf

+∆M ∆Bond +∆B

∆Bcb

Dum

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Quadruple entry - Example of public spending financed

directly by banks

HH Prod. Banks Gov. C.B. Somme M0 +G

G

GDP +Y

Y

∆M0 ∆Dep

∆Mh
∆Mf

+∆M

∆Mg

∆Bond.

∆Bb

+∆B Somme

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Example of matrices for Keen (1995)

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The equations: 2 steps

1. The accounting identities

All the identities and flows implied by the accounting e.g. for firms (F= total profit) Π = C + I − WL − iD (1) ˙ M = WL + iM − C (2)

2. The behavioral equations: economic theory comes into play

The closure: through theory we try to find an equation for each variable not directly determined by the accounting making theoretical assumptions on the behavior of the sectors. C = sWL (3) I = K(µ0 + µ1w − µ2d + µ3ue) (4)

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Example: Bank of England Model [Burgess et al., 2016]

From the Stockton Report

Consider creating a forecast with an extended horizon beyond the current three-year period: a horizon of sufficient length to allow consideration of the development and likely unwinding of major economic and financial imbalances

The building up of financial imbalances contributed to the financial crisis

and ensuing Great Recession

If we’d been looking further ahead than two years, we might have seen

this coming

We are building a new dynamic macroeconomic model of financial bal-

ances for the United Kingdom using flow of funds data from 1997 to the present.

The model contains six sectors: households, private non-financial com-

panies, the government, banks, insurance companies and pension funds, and a simplified rest of the world.

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Transaction-Flow Diagram

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Outline

1 Introduction and motivation 2 The stock flow consistent approach 3 Coping with Collapse Introduction Related Literature Modeling set-up

The macroeconomic core The climate module

Target achievements Modeling uncertainty Climate prospective

Scope of analysis Scenario analysis

Concluding remarks

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Outline

1 Introduction and motivation 2 The stock flow consistent approach 3 Coping with Collapse Introduction Related Literature Modeling set-up

The macroeconomic core The climate module

Target achievements Modeling uncertainty Climate prospective

Scope of analysis Scenario analysis

Concluding remarks

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Introduction

Publications Coping With Collapse, Ecological Economics, Volume 147, 2018, Pages 383-398. Debt and damages: What are the chances of staying under the 2C warming threshold? International Economics Volume 155, October 2018, Pages 92-108. Carbon Pricing and Global Warming, AFD Research Papers, 2018

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Introduction

Research questions

Combine two sources of global instabilities, climate and finance, in a min-

imal dynamic framework to perform a prospective analysis. Can climate change drive the global economy in a deep recession?

Provide guidance for the implementation of public policy objectives in or-

der to ensure economic stability and perform the energy shift. Is a price signal sufficient?

Cope with climate as well as economic uncertainties in order to have

a deeper understanding of our chances to meet to Paris Agreement’s

bjectives.

What are our chances to stay below +2◦C?

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Introduction

Main findings

In a business as usual scenario, climate change drives the global econ-

my towards a situation of “economic collapse.”

A price signal (carbon tax) provides indeed the right incentives to avoid

most of climate damages.

However, financial risks are not entirely precluded: in line with the Stern-

Stiglitz report (2017), a green public intervention is required to tackle both instabilities.

Our chances to achieve the Paris Agreement target stay, at most, below

25%.

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Outline

1 Introduction and motivation 2 The stock flow consistent approach 3 Coping with Collapse Introduction Related Literature Modeling set-up

The macroeconomic core The climate module

Target achievements Modeling uncertainty Climate prospective

Scope of analysis Scenario analysis

Concluding remarks

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Related Literature

The Integrated Assessment Modeling (IAM) approach A workhorse model: the Dynamic Integrated Climate Economy (DICE) model

f Nordhaus in a seminal series of papers (1993, 2013, 2014, 2016)

The Ramsey-Kass-Coopmans’ approach as a core macroeconomic model A climate feedback loop with a damage function A carbon price instrument to shape the energy shift

Various extensions of the climate-economic interactions

Endogenous technological progress (Moyer, 2014) Allocation of climate damages (Dietz and Stern, 2015) Finance and green policies (Dafermos, 2016)

This paper: assess financial instability (Keen, 1995 and Grasselli et al., 2012)

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Related Literature

The approaches of Goodwin (1967) and Keen (1995)

Endogenous business cycles as in Goodwin (1967) Minimal (bounded) rationality

◮ Sonnenschein-Mantel-Debreu (1975) or Chiappori and Ekeland (1999): “any-

thing can happen”

◮ Phenomenological approach relying on empirical data

Dynamic interaction of deep macroeconomic behaviors

◮ Lotka-Volterra relationship linking the employment rate to the wage share ◮ Short-term Phillips curve (Mankiw, 2010 or Krugman, 2014) ◮ Investment as a function of profit share ◮ Dynamics of corporates’ private debt

Multiplicity of long-term equilibria

◮ A Solovian steady-state ◮ A bad attractor leading to a breakdown in the long-run ◮ Asymptotic local stability becomes key

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Related Literature

Key modelling highlights

Leontief production function of capital and labour. Behavioural aggregated dynamics for wages negotiations and investment

decisions:

◮ Sonnenschein-Mantel-Debreu (1975): anything can happen. ◮ Phenomenological approach: behavoural function empirically estimated.

Accounting framework between economic aggregates: stock-flow consis-

tency of the model.

Exogenous growth of the population and the technical progress. Breakdown to a reduced autonomous differential system between the

wage share of the economy, the employment rate and the private debt-to- production ratio.

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Related Literature

Aggregated behaviours

The Short-term Phillips Curve (Mankiw, 2010):

A positive relationship between the employment rate and the growth of wages.

The Investment Function:

A positive relationship between the profit rate and the investment, allow- ing a Minsky moment to occur.

The Banking Sector:

Provide financing for investments net of current profits (the higher the profits, the heavier the leverage of the economy).

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Related Literature

The model λ: the employment rate. λ := L N . L: the labor force, and N: the total population. ˙ N N = β. a: the labor productivity. ˙ a a = α. w: the wage per worker, W = wL: the total wage, ω: the wage share ad Y: the production. ω = W Y = wL aL = w a

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Related Literature

The model K: the stock of capital. ˙ K = I − δK. The Leontief production function Y = min K ν , aL

=

K ν = aL.

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Related Literature

The model D: the aggregate debt. ˙ D = I − Π. with Π := Y − W − rD: the real profit of the firm, and r: the interest rate. π: the profit-to-production ratio. π = Π Y . d: the debt-production ratio. d = D Y .

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Related Literature

Aggregate behaviours

The Short-term Phillips Curve (Mankiw, 2010).

˙ w w = φ(λ).

The Investment Function : it evolves positively with the profit share.

I Y = κ (π) .

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Related Literature

The three-dimensional system One can retrieve the following set of equations: ˙ ω = ω [φ(λ) − α] ˙ λ = λ κ(1 − ω − rd) ν − δ − α − β

˙

d = d

r − κ(1 − ω − rd)

ν + δ

+ κ(1 − ω − rd) − (1 − ω)

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Related Literature

Aggregate behaviours

Phenomenological approach: φ(.) and κ(.) are empirically estimated. Sonnenschein-Mantel-Debreu (1975): anything can happen. Agent-based model.

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Related Literature

Equilibria analysis Two economically meaningful long run equilibria:

A good equilibrium locally stable:

◮ Positive employment rate and wage share, finite level of debt. ◮ Growth rate in line with the Solow or Harrod-Domar models.1

A bad equilibrium locally stable:

◮ Employment rate and wage share collapsing, infinite level of debt. ◮ Situation of economic crisis. 1The sum of the technical progress growth and the population growth.

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Related Literature

A prey-predator logic – Example of the Goodwin model (1967) ω λ λeq ωeq

Figure: Phase diagram for the Goodwin model (employment rate λ vs wage share ω).

An employment rate above its “good equilibrium” value pushes up the

wage share (Philips Curve).

A wage share above its “good equilibrium” value pushes down the em-

ployment rate (Investment Function).

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Related Literature

Endogenous monetary cycles around the good equilibrium

Expansion:

1. The rise of the profit share boosts the investment;
2. A higher investment increases the growth of capital accumulation;
3. A growing stock of capital favours economic growth.

Slowdown:

1. The growth of the economy reduces the unemployment;
2. A higher employment rate induces wages inflation;
3. The rise of wages devour the profit rate and thus the investment;
4. The decrease of investment slowdowns economic growth.

Recovery:

1. A falling economic growth make unemployment rise;
2. A lower employment rate reduces the wages;
3. Diminishing wages restore the profit rate and thus the investment.

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Outline

1 Introduction and motivation 2 The stock flow consistent approach 3 Coping with Collapse Introduction Related Literature Modeling set-up

The macroeconomic core The climate module

Target achievements Modeling uncertainty Climate prospective

Scope of analysis Scenario analysis

Concluding remarks

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Modeling set-up

Key modeling highlights Bridging climate and a global monetary economy

1. The macroeconomic core

◮ Non-neutrality of money ◮ Severe breakdowns do not appear as “black swan events” ◮ Emissions, carbon price and abatement technology (Nordhaus, 2016) ◮ Price dynamics under imperfect competition (Grasselli et al., 2014) ◮ Sigmoïd pattern of the global workforce (UN population scenarios, 2015) ◮ Dividends payments

2. The DICE climate feedback loop of Nordhaus (2016) refined with

◮ More convex damage functions (Weitzman, 2011) ◮ Allocation of environmental damages between output and capital (Dietz et al.,

2015)

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Modeling set-up

Stock-flow consistency

Households Productive Sector Banks Sum Balance Sheet Capital stock pK pK Deposits Mh Mc −M Loans −Lc Lc Equities E −Ef −Eb Sum (net worth) Xh Xf = 0 Xb = 0 X Transactions current capital Consumption −pC pC Investment pI −pI

Acc. memo [GDP]

[pY] Wages W −W Capital depr. −(δ + DK)pK (δ + DK)pK Carbon taxes pTf −pTf

Int. on loans

−rcLc rcLc Bank’s dividends Πb −Πb Productive sector’s dividends Πd −Πd

Int. on deposits

rM Mh rM Mc −rM M Column sum (balance) Sh Sc −pI + (δ + DK)pK Sb Flow of Funds Change in capital stock p ˙ K p ˙ K Change in deposits ˙ Mh ˙ Mc − ˙ M Change in loans − ˙ Lc ˙ Lc Column sum (savings) Sh Sc Sb Change in equities ˙ Ef −(Sc + ˙ pK) Change in bank equity ˙ Eb −Sb Change in net worth Sh + ˙ E ˙ pK + p ˙ K

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Modeling set-up – The macroeconomic core

Dynamics and behavioural relations

Short term Phillips curve: ˙ w w = φ(λ) Dynamic of prices (Grasselli et al., 2014): ˙ p p = η(mω − 1) Investment behavior:

I = κ(π)pY

Dynamics of private debt:

˙ D = I − Π − Πr

Taylor rule:

r = max {0, r ∗ + i + φ(i − i∗)}

Dynamics of capital:

˙ K = I − (δ + DK)K

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Modeling set-up – The macroeconomic core

Introducing climate change and public policies

Joint production process incorporating climate damages

Y 0 = min{K/ν; aL} Y = (1 − DY)

1 − A(pBS)
Y 0

Eind = σ

1 − n(A)
Y 0

Public policies and aggregated profit

Π = pY − wL − rD − pT(pC, Eind) − (δ + DK)pK,

Endogenous choice of the emission reduction rate in the productive sec-

tor n = min pc pBS

1

θ−1

; 1

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Modeling set-up – The climate module

Physical process overview

Real Output CO2 Emissions CO2 Accumulatjon Radiatjve Forcing Temperature Change Figure: Climate-economy interactions diagram.

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Modeling set-up – The climate module

CO2 emissions Global CO2 emissions are the sum of two contributions: E := Eind + Eland

Endogenous industrial emissions:

Eind := Yσ(1 − n)

◮ Proportional to real output Y ◮ Emission intensity of the economy: σ ◮ Emissions reduction rate: n

Exogenous emissions linked to land-use change Eland

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Modeling set-up – The climate module

CO2 accumulation (1/2)

Layer AT

Atmosphere

Layer UP

Biosphere Upper part of the oceans

Layer LO

Lower part of the oceans

Three-Layer Model of CO2 Accumulation CO2 Emissions Radiative Forcing Figure: CO2 accumulation in a three-layer model.

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Modeling set-up – The climate module

CO2 accumulation (2/2) Modeling through the following system:    ˙ CO2

AT

˙ CO2

COUP

2

COLO

2

 

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Modeling set-up – The climate module

Radiative forcing Radiative forcing is the sum of two contributions: F := Find + Fexo

CO2 accumulation in the atmospheric layer:

Find := F2×CO2 log(2) log

COAT

2

COATinit

2

Exogenous radiative forcing Fexo to model residual factors (dynamics cal-

ibrated on the IPCCs’ RCPs (2013)?)

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Modeling set-up – The climate module

Temperature change (1/2)

Upper Layer

Atmosphere Biosphere Upper part of the oceans

Lower Layer

Lower part of the oceans

Two-Layer Model of Mean Temperature Radiatjve Forcing Real Output Damage Figure: Dynamics of temperature.

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Modeling set-up – The climate module

Temperature change (2/2) Temperature’s evolution is inspired by Geoffroy et al. (2013):

Energy Balanced A two-layer model:

◮ Atmosphere, biosphere, ocean upper level (temperature anomaly):

C ˙ T = F − ρT − γ∗(T − T0)

◮ Deep ocean (temperature anomaly T0):

C0 ˙ T0 = γ∗(T − T0)

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Modeling set-up – The climate module

CO2 accumulation (1/2)

Layer AT

Atmosphere

Layer UP

Biosphere Upper part of the oceans

Layer LO

Lower part of the oceans

Three-Layer Model of CO2 Accumulation CO2 Emissions Radiative Forcing Figure: CO2 accumulation in a three-layer model.

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Modeling set-up – The climate module

CO2 accumulation (2/2) Modeling through the following system:    ˙ CO2

AT

˙ CO2

COUP

2

COLO

2

 

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Modeling set-up – The climate module

Radiative forcing Radiative forcing is the sum of two contributions: F := Find + Fexo

CO2 accumulation in the atmospheric layer:

Find := F2×CO2 log(2) log

COAT

2

COATinit

2

Exogenous radiative forcing Fexo to model residual factors (dynamics cal-

ibrated on the IPCCs’ RCPs (2013)?)

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Modeling set-up – The climate module

Temperature change (1/2)

Upper Layer

Atmosphere Biosphere Upper part of the oceans

Lower Layer

Lower part of the oceans

Two-Layer Model of Mean Temperature Radiatjve Forcing Real Output Damage Figure: Dynamics of temperature.

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Modeling set-up – The climate module

Temperature change Temperature’s evolution is inspired by Geoffroy et al. (2013):

Energy Balanced A two-layer model:

◮ Atmosphere, biosphere, ocean upper level (temperature anomaly):

C ˙ T = F − ρT − γ∗(T − T0)

◮ Deep ocean (temperature anomaly T0):

C0 ˙ T0 = γ∗(T − T0)

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Modeling set-up – The climate module

Figure: Tol (2018)

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Modeling set-up – The climate module

Climate damage as a percentage of real GDP

0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 1 2 3 4 5 6 Damages (fraction of output) Temperature increase (°C) Nordhaus Weitzman Dietz and Stern

Figure: Shape of various damage functions

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Outline

1 Introduction and motivation 2 The stock flow consistent approach 3 Coping with Collapse Introduction Related Literature Modeling set-up

The macroeconomic core The climate module

Target achievements Modeling uncertainty Climate prospective

Scope of analysis Scenario analysis

Concluding remarks

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Target achievements –

Which temperature targets can be reached?

Nordhaus (2016) following a recalibration of the climate module of DICE:2

The study confirms past estimates of likely rapid climate change over the next century if there are not major climate-change policies. It suggests that it will be extremely difficult to achieve the 2◦C target of international agree- ments even if ambitious policies are introduced in the near term. The required carbon price needed to achieve current targets has risen over time as policies have been delayed.

Illustration:

1 2 3 4 1.5 3.1 4.5 6.0

Figure: Temperature increase in 2100 as a function of the climate sensitivity whenever zero net emission is reached in 2016 (blue line) or 2018 (red line).

2❤tt♣✿✴✴❝♦✇❧❡s✳②❛❧❡✳❡❞✉✴s✐t❡s✴❞❡❢❛✉❧t✴❢✐❧❡s✴❢✐❧❡s✴♣✉❜✴❞✷✵✴❞✷✵✺✼✳♣❞❢

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Target achievements –

Which shape for the carbon price?

0.1 0.2 0.3 0.4 0.5

1.0
0.5

0.0 0.5 1.0

γpC βpC

4.0 °C 3.5 °C 3.0 °C 2.5 °C 2.0 °C

Figure: Heatmap in 2100 depending on the carbon price path in the Type 3 scenario (exponential case in the white line).

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Outline

1 Introduction and motivation 2 The stock flow consistent approach 3 Coping with Collapse Introduction Related Literature Modeling set-up

The macroeconomic core The climate module

Target achievements Modeling uncertainty Climate prospective

Scope of analysis Scenario analysis

Concluding remarks

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Modeling uncertainty

Uncertainty parameters Uncertainty is divided into two classes:

1. On the macroeconomic parameters

◮ α, that drives the labor productivity growth, and consequently, the long-term

growth.

2. On the climate module parameters

◮ S, the climate sensitivity parameter. ◮ Cup, the biosphere and upper ocean’s ultimate load capacity of absorption

CO2.

◮ In all our simulations, we test various damage functions between Nordhaus’s

and the Dietz and Stern’s.

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Modeling uncertainty

Shapes of the probability density functions

−0.04 −0.02 0.00 0.02 0.04 0.06 0.08 5 15 25 35 α Normal density µ = 0.0206 σ = 0.0112 2 4 6 8 0.0 0.2 0.4 S log−Normal density µ = 1.107 σ = 0.264 200 400 600 800 1000 0.000 0.002 0.004 Cup log−Normal density µ = 5.8855763 σ = 0.2512867

Figure: Probability density function of the parameters

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Outline

1 Introduction and motivation 2 The stock flow consistent approach 3 Coping with Collapse Introduction Related Literature Modeling set-up

The macroeconomic core The climate module

Target achievements Modeling uncertainty Climate prospective

Scope of analysis Scenario analysis

Concluding remarks

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Climate prospective – Scope of analysis

Design of the prospective scenarios

Calibration over a reconstructed world economy (approx.

85% of the “real” world) with a panel of 36 countries over the period 2000 – 2015 (dataset from World Bank, Penn University, the Bureau of Economic Anal- ysis and the United Nations).

Prospective analysis through 4 classes of scenarios:3

Scenario No climate Baseline Low policy High policy Carbon tax (Weak pC)

Yes
Carbon tax (High pC)
Yes

Yes

Abat. subsidy (25%)
Yes

Damage Type

Stern

Stern Stern Table: Scenarios considered for the prospective analysis

Where the public policies are

1. Weak pC represents non-constraining carbon price starting at approx. 2 in

2016 and growing at a 2% rate per year

2. High pC represents a carbon price at 80 in 2020 and 100 in 2030
3. A x% of abatement subsidy in equivalent to reducing abatements costs by

x%

3The scenarios listed in the presentation are drawn from a broader range assessed in this study.

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Climate prospective – Scenario analysis

The no-climate scenario

2020 2040 2060 2080 2100 100 300 500

Output

2020 2040 2060 2080 2100

4
2

2 4 6

Real growth

2020 2040 2060 2080 2100 1 2 3 4 5 6

Nominal interest rate (in %)

2020 2040 2060 2080 2100

4
2

2 4 6

Inflation rate

2020 2040 2060 2080 2100 1 2 3 4 5

Debt-to-output ratio

2020 2040 2060 2080 2100 0.0 0.2 0.4 0.6 0.8 1.0

Employment rate

Figure: [0.25; 0.75] probability interval of the No climate scenario with a damage-to-capital ratio of 0% in black shades (medians in solid lines)

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Climate prospective – Scenario analysis

Policy scenarios – Trajectories and narratives

2020 2040 2060 2080 2100 100 200 300 400 500 600

Output

2020 2040 2060 2080 2100

4
2

2 4 6

Real growth

2020 2040 2060 2080 2100 1 2 3 4 5 6

Nominal interest rate (in %)

2020 2040 2060 2080 2100

4
2

2 4 6

Inflation rate

2020 2040 2060 2080 2100 1 2 3 4 5

Debt-to-output ratio

2020 2040 2060 2080 2100

4
2

2 4

Public deficit-to-ouput ratio

2020 2040 2060 2080 2100 50 100 150 200

Emissions

2020 2040 2060 2080 2100 1 2 3 4 5 6

Temperature

Figure: [0.25; 0.75] probability interval of the Baseline, Low policy and High policy scenarios with a damage-to-capital ratio of 0% in red, purple and blue shades (medians in solid lines)

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Climate prospective – Scenario analysis

Policy scenarios – Staying under the temperature and debt thresholds

Baseline Low policy High policy 2 4 6 0% 33% 0% 33% 0% 33%

Share of damages to capital 0% 33% Year 2050 2100

Figure: Probability density function of the temperature anomaly ratio in 2050 (dark blue) and 2100 (light blue).

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Climate prospective – Scenario analysis

Policy scenarios – Basin of attraction

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Outline

1 Introduction and motivation 2 The stock flow consistent approach 3 Coping with Collapse Introduction Related Literature Modeling set-up

The macroeconomic core The climate module

Target achievements Modeling uncertainty Climate prospective

Scope of analysis Scenario analysis

Concluding remarks

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Concluding remarks

Main results

We have developed of a stock-flow consistent monetary integrated as-

sessment model calibrated at the world level

Inaction would most likely lead to a global collapse of the economic sys-

tem

Limited action (carbon price only) allows to avoid most climate damages

but remains insufficient to preclude financial instability

Wider public involvement (carbon price and subsidies) is more likely to

meet both objectives, in line with the recommendations of the Stern- Stiglitz report (2017)

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Concluding remarks

Some possible ways forward

Develop more robust multivariate estimation and calibration methods (Thorn-

ton and Chambers 2014, Grasselli and Maheshwari 2018, Mc Isaac 2018).

Estimate behavioral functions by taking into consideration dataset with

various frequencies–Financial vs. Economics (Thornton and Chambers 2017).

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#WorldInCommon

AGENCE FRANÇAISE DE DÉVELOPPEMENT | FRENCH DEVELOPMENT AGENCY

Thanks for your attention mcisaacf@afd.fr Shiny application of the model online

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