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# Default Risk and Income Fluctuations¶

Contents

## Overview¶

This lecture computes versions of Arellano’s [Are08] model of sovereign default

The model describes interactions among default risk, output, and an equilibrium interest rate that includes a premium for endogenous default risk

The decision maker is a government of a small open economy that borrows from risk-neutral foreign creditors

The foreign lenders must be compensated for default risk

The government borrows and lends abroad in order to smooth the consumption of its citizens

The government repays its debt only if it wants to, but declining to pay has adverse consequences

The interest rate on government debt adjusts in response to the state-dependent default probability chosen by government

The model yields outcomes that help interpret sovereign default experiences, including

- countercyclical interest rates on sovereign debt
- countercyclical trade balances
- high volatility of consumption relative to output

Notably, long recessions caused by bad draws in the income process increase the government’s incentive to default

This can lead to

- spikes in interest rates
- temporary losses of access to international credit markets
- large drops in output, consumption, and welfare
- large capital outflows during recessions

Such dynamics are consistent with experiences of many countries

## Structure¶

In this section we describe the main features of the model

### Output, Consumption and Debt¶

A small open economy is endowed with an exogenous stochastically fluctuating potential output stream \(\{y_t\}\)

Potential output is realized only in periods in which the government honors its sovereign debt

The output good can be traded or consumed

The sequence \(\{y_t\}\) is described by a Markov process with stochastic density kernel \(p(y, y')\)

Households within the country are identical and rank stochastic consumption streams according to

Here

- \(0 < \beta < 1\) is a time discount factor
- \(u\) is an increasing and strictly concave utility function

Consumption sequences enjoyed by households are affected by the government’s decision to borrow or lend internationally

The government is benevolent in the sense that its aim is to maximize (1)

The government is the only domestic actor with access to foreign credit

Because household are averse to consumption fluctuations, the government will try to smooth consumption by borrowing from (and lending to) foreign creditors

### Asset Markets¶

The only credit instrument available to the government is a one-period bond traded in international credit markets

The bond market has the following features

The bond matures in one period and is not state contingent

A purchase of a bond with face value \(B'\) is a claim to \(B'\) units of the consumption good next period

To purchase \(B'\) next period costs \(q B'\) now, or, what is equivalent

For selling \(-B'\) units of next period goods the seller earns \(- q B'\) of today’s goods

- if \(B' < 0\), then \(-q B'\) units of the good are received in the current period, for a promise to repay \(-B'\) units next period
- there is an equilibrium price function \(q(B', y)\) that makes \(q\) depend on both \(B'\) and \(y\)

Earnings on the government portfolio are distributed (or, if negative, taxed) lump sum to households

When the government is not excluded from financial markets, the one-period national budget constraint is

Here and below, a prime denotes a next period value or a claim maturing next period

To rule out Ponzi schemes, we also require that \(B \geq -Z\) in every period

- \(Z\) is chosen to be sufficiently large that the constraint never binds in equilibrium

### Financial Markets¶

Foreign creditors

- are risk neutral
- know the domestic output stochastic process \(\{y_t\}\) and observe \(y_t, y_{t-1}, \ldots,\) at time \(t\)
- can borrow or lend without limit in an international credit market at a constant international interest rate \(r\)
- receive full payment if the government chooses to pay
- receive zero if the government defaults on its one-period debt due

When a government is expected to default next period with probability \(\delta\), the expected value of a promise to pay one unit of consumption next period is \(1 - \delta\).

Therefore, the discounted expected value of a promise to pay \(B\) next period is

Next we turn to how the government in effect chooses the default probability \(\delta\)

### Government’s decisions¶

At each point in time \(t\), the government chooses between

- defaulting
- meeting its current obligations and purchasing or selling an optimal quantity of one-period sovereign debt

Defaulting means declining to repay all of its current obligations

If the government defaults in the current period, then consumption equals current output

But a sovereign default has two consequences:

Output immediately falls from \(y\) to \(h(y)\), where \(0 \leq h(y) \leq y\)

- it returns to \(y\) only after the country regains access to international credit markets

The country loses access to foreign credit markets

### Reentering international credit market¶

While in a state of default, the economy regains access to foreign credit in each subsequent period with probability \(\theta\)

## Equilibrium¶

Informally, an equilibrium is a sequence of interest rates on its sovereign debt, a stochastic sequence of government default decisions and an implied flow of household consumption such that

- Consumption and assets satisfy the national budget constraint
- The government maximizes household utility taking into account
- the resource constraint
- the effect of its choices on the price of bonds
- consequences of defaulting now for future net output and future borrowing and lending opportunities

- The interest rate on the government’s debt includes a risk-premium sufficient to make foreign creditors expect on average to earn the constant risk-free international interest rate

To express these ideas more precisely, consider first the choices of the government, which

enters a period with initial assets \(B\), or what is the same thing, initial debt to be repaid now of \(-B\)

observes current output \(y\), and

chooses either

- to default, or
- to pay \(-B\) and set next period’s debt due to \(-B'\)

In a recursive formulation,

- state variables for the government comprise the pair \((B, y)\)
- \(v(B, y)\) is the optimum value of the government’s problem when at the beginning of a period it faces the choice of whether to honor or default
- \(v_c(B, y)\) is the value of choosing to pay obligations falling due
- \(v_d(y)\) is the value of choosing to default

\(v_d(y)\) does not depend on \(B\) because, when access to credit is eventually regained, net foreign assets equal \(0\)

Expressed recursively, the value of defaulting is

The value of paying is

The three value functions are linked by

The government chooses to default when

and hence given \(B'\) the probability of default next period is

Given zero profits for foreign creditors in equilibrium, we can combine (3) and (4) to pin down the bond price function:

### Definition of equilibrium¶

An *equilibrium* is

- a pricing function \(q(B',y)\),
- a triple of value functions \((v_c(B, y), v_d(y), v(B,y))\),
- a decision rule telling the government when to default and when to pay as a function of the state \((B, y)\), and
- an asset accumulation rule that, conditional on choosing not to default, maps \((B,y)\) into \(B'\)

such that

- The three Bellman equations for \((v_c(B, y), v_d(y), v(B,y))\) are satisfied
- Given the price function \(q(B',y)\), the default decision rule and the asset accumulation decsion rule attain the optimal value function \(v(B,y)\), and
- The price function \(q(B',y)\) satisfies equation (5)

## Computation¶

Let’s now compute an equilibrium of Arellano’s model

The equilibrium objects are the value function \(v(B, y)\), the associated default decision rule, and the pricing function \(q(B', y)\)

We’ll use our code to replicate Arellano’s results

After that we’ll perform some additional simulations

The majority of the code below was written by Chase Coleman

It uses a slightly modified version of the algorithm recommended by Arellano

- The appendix to [Are08] recommends value function iteration until convergence, updating the price, and then repeating
- Instead, we update the bond price at every value function iteration step

The second approach is faster and the two different procedures deliver very similar results

Here is a more detailed description of our algorithm:

- Guess a value function \(v(B, y)\) and price function \(q(B', y)\)
- At each pair \((B, y)\),
- update the value of defaulting \(v_d(y)\)
- update the value of continuing \(v_c(B, y)\)

- Update the value function v(B, y), the default rule, the implied ex ante default probability, and the price function
- Check for convergence. If converged, stop. If not, go to step 2.

We use simple discretization on a grid of asset holdings and income levels

The output process is discretized using Tauchen’s quadrature method

The code can be found in the file arellano_vfi.jl but we repeat it here for convenience

(Results and discussion follow the code)

```
using QuantEcon: tauchen, MarkovChain, simulate
# ------------------------------------------------------------------- #
# Define the main Arellano Economy type
# ------------------------------------------------------------------- #
"""
Arellano 2008 deals with a small open economy whose government
invests in foreign assets in order to smooth the consumption of
domestic households. Domestic households receive a stochastic
path of income.
##### Fields
* `β::AbstractFloat`: Time discounting parameter
* `γ::AbstractFloat`: Risk aversion parameter
* `r::AbstractFloat`: World interest rate
* `ρ::AbstractFloat`: Autoregressive coefficient on income process
* `η::AbstractFloat`: Standard deviation of noise in income process
* `θ::AbstractFloat`: Probability of re-entering the world financial sector after default
* `ny::Integer`: Number of points to use in approximation of income process
* `nB::Integer`: Number of points to use in approximation of asset holdings
* `ygrid::Vector{AbstractFloat}`: This is the grid used to approximate income process
* `ydefgrid::Vector{AbstractFloat}`: When in default get less income than process
would otherwise dictate
* `Bgrid::Vector{AbstractFloat}`: This is grid used to approximate choices of asset
holdings
* `Π::Matrix{AbstractFloat}`: Transition probabilities between income levels
* `vf::Matrix{AbstractFloat}`: Place to hold value function
* `vd::Matrix{AbstractFloat}`: Place to hold value function when in default
* `vc::Matrix{AbstractFloat}`: Place to hold value function when choosing to
continue
* `policy::Matrix{AbstractFloat}`: Place to hold asset policy function
* `q::Matrix{AbstractFloat}`: Place to hold prices at different pairs of (y, B')
* `defprob::Matrix{AbstractFloat}`: Place to hold the default probabilities for
pairs of (y, B')
"""
struct ArellanoEconomy{TF<:AbstractFloat, TI<:Integer}
# Model Parameters
β::TF
γ::TF
r::TF
ρ::TF
η::TF
θ::TF
# Grid Parameters
ny::TI
nB::TI
ygrid::Vector{TF}
ydefgrid::Vector{TF}
Bgrid::Vector{TF}
Π::Matrix{TF}
# Value function
vf::Matrix{TF}
vd::Matrix{TF}
vc::Matrix{TF}
policy::Matrix{TF}
q::Matrix{TF}
defprob::Matrix{TF}
end
"""
This is the default constructor for building an economy as presented
in Arellano 2008.
##### Arguments
* `;β::AbstractFloat(0.953)`: Time discounting parameter
* `;γ::AbstractFloat(2.0)`: Risk aversion parameter
* `;r::AbstractFloat(0.017)`: World interest rate
* `;ρ::AbstractFloat(0.945)`: Autoregressive coefficient on income process
* `;η::AbstractFloat(0.025)`: Standard deviation of noise in income process
* `;θ::AbstractFloat(0.282)`: Probability of re-entering the world financial sector
after default
* `;ny::Integer(21)`: Number of points to use in approximation of income process
* `;nB::Integer(251)`: Number of points to use in approximation of asset holdings
"""
function ArellanoEconomy{TF<:AbstractFloat}(;β::TF=.953, γ::TF=2., r::TF=0.017,
ρ::TF=0.945, η::TF=0.025, θ::TF=0.282,
ny::Integer=21, nB::Integer=251)
# Create grids
Bgrid = collect(linspace(-.4, .4, nB))
mc = tauchen(ny, ρ, η)
Π = mc.p
ygrid = exp.(mc.state_values)
ydefgrid = min.(.969 * mean(ygrid), ygrid)
# Define value functions (Notice ordered different than Python to take
# advantage of column major layout of Julia)
vf = zeros(nB, ny)
vd = zeros(1, ny)
vc = zeros(nB, ny)
policy = Array{TF}(nB, ny)
q = ones(nB, ny) .* (1 / (1 + r))
defprob = Array{TF}(nB, ny)
return ArellanoEconomy(β, γ, r, ρ, η, θ, ny, nB, ygrid, ydefgrid, Bgrid, Π,
vf, vd, vc, policy, q, defprob)
end
u(ae::ArellanoEconomy, c) = c^(1 - ae.γ) / (1 - ae.γ)
_unpack(ae::ArellanoEconomy) =
ae.β, ae.γ, ae.r, ae.ρ, ae.η, ae.θ, ae.ny, ae.nB
_unpackgrids(ae::ArellanoEconomy) =
ae.ygrid, ae.ydefgrid, ae.Bgrid, ae.Π, ae.vf, ae.vd, ae.vc, ae.policy, ae.q, ae.defprob
# ------------------------------------------------------------------- #
# Write the value function iteration
# ------------------------------------------------------------------- #
"""
This function performs the one step update of the value function for the
Arellano model-- Using current value functions and their expected value,
it updates the value function at every state by solving for the optimal
choice of savings
##### Arguments
* `ae::ArellanoEconomy`: This is the economy we would like to update the
value functions for
* `EV::Matrix{TF}`: Expected value function at each state
* `EVd::Matrix{TF}`: Expected value function of default at each state
* `EVc::Matrix{TF}`: Expected value function of continuing at each state
##### Notes
* This function updates value functions and policy functions in place.
"""
function one_step_update!{TF<:AbstractFloat}(ae::ArellanoEconomy, EV::Matrix{TF},
EVd::Matrix{TF}, EVc::Matrix{TF})
# Unpack stuff
β, γ, r, ρ, η, θ, ny, nB = _unpack(ae)
ygrid, ydefgrid, Bgrid, Π, vf, vd, vc, policy, q, defprob = _unpackgrids(ae)
zero_ind = searchsortedfirst(Bgrid, 0.)
for iy=1:ny
y = ae.ygrid[iy]
ydef = ae.ydefgrid[iy]
# Value of being in default with income y
defval = u(ae, ydef) + β*(θ*EVc[zero_ind, iy] + (1-θ)*EVd[1, iy])
ae.vd[1, iy] = defval
for ib=1:nB
B = ae.Bgrid[ib]
current_max = -1e14
pol_ind = 0
for ib_next=1:nB
c = max(y - ae.q[ib_next, iy]*Bgrid[ib_next] + B, 1e-14)
m = u(ae, c) + β * EV[ib_next, iy]
if m > current_max
current_max = m
pol_ind = ib_next
end
end
# Update value and policy functions
ae.vc[ib, iy] = current_max
ae.policy[ib, iy] = pol_ind
ae.vf[ib, iy] = defval > current_max ? defval: current_max
end
end
Void
end
"""
This function takes the Arellano economy and its value functions and
policy functions and then updates the prices for each (y, B') pair
##### Arguments
* `ae::ArellanoEconomy`: This is the economy we would like to update the
prices for
##### Notes
* This function updates the prices and default probabilities in place
"""
function compute_prices!(ae::ArellanoEconomy)
# Unpack parameters
β, γ, r, ρ, η, θ, ny, nB = _unpack(ae)
# Create default values with a matching size
vd_compat = repmat(ae.vd, nB)
default_states = vd_compat .> ae.vc
# Update default probabilities and prices
copy!(ae.defprob, default_states * ae.Π')
copy!(ae.q, (1 - ae.defprob) / (1 + r))
Void
end
"""
This performs value function iteration and stores all of the data inside
the ArellanoEconomy type.
##### Arguments
* `ae::ArellanoEconomy`: This is the economy we would like to solve
* `;tol::Float64(1e-8)`: Level of tolerance we would like to achieve
* `;maxit::Int(10000)`: Maximum number of iterations
##### Notes
* This updates all value functions, policy functions, and prices in place.
"""
function vfi!(ae::ArellanoEconomy; tol=1e-8, maxit=10000)
# Unpack stuff
β, γ, r, ρ, η, θ, ny, nB = _unpack(ae)
ygrid, ydefgrid, Bgrid, Π, vf, vd, vc, policy, q, defprob = _unpackgrids(ae)
Πt = Π'
# Iteration stuff
it = 0
dist = 10.
# Allocate memory for update
V_upd = zeros(ae.vf)
while dist > tol && it < maxit
it += 1
# Compute expectations for this iterations
# (We need Π' because of order value function dimensions)
copy!(V_upd, ae.vf)
EV = ae.vf * Πt
EVd = ae.vd * Πt
EVc = ae.vc * Πt
# Update Value Function
one_step_update!(ae, EV, EVd, EVc)
# Update prices
compute_prices!(ae)
dist = maximum(abs, V_upd - ae.vf)
if it%25 == 0
println("Finished iteration $(it) with dist of $(dist)")
end
end
Void
end
"""
This function simulates the Arellano economy
##### Arguments
* `ae::ArellanoEconomy`: This is the economy we would like to solve
* `capT::Integer`: Number of periods to simulate
* `;y_init::AbstratFloat(mean(ae.ygrid)`: The level of income we would like to
start with
* `;B_init::AbstratFloat(mean(ae.Bgrid)`: The level of asset holdings we would like
to start with
##### Returns
* `B_sim_val::Vector{TI}`: Simulated values of assets
* `y_sim_val::Vector{TF}`: Simulated values of income
* `q_sim_val::Vector{TF}`: Simulated values of prices
* `default_status::Vector{Bool}`: Simulated default status
(true if in default)
##### Notes
* This updates all value functions, policy functions, and prices in place.
"""
function QuantEcon.simulate{TI<:Integer, TF<:AbstractFloat}(ae::ArellanoEconomy,
capT::TI=5000;
y_init::TF=mean(ae.ygrid),
B_init::TF=mean(ae.Bgrid))
# Get initial indices
zero_index = searchsortedfirst(ae.Bgrid, 0.)
y_init_ind = searchsortedfirst(ae.ygrid, y_init)
B_init_ind = searchsortedfirst(ae.Bgrid, B_init)
# Create a QE MarkovChain
mc = MarkovChain(ae.Π)
y_sim_indices = simulate(mc, capT+1; init=y_init_ind)
# Allocate and Fill output
y_sim_val = Vector{TF}(capT+1)
B_sim_val, q_sim_val = similar(y_sim_val), similar(y_sim_val)
B_sim_indices = Vector{TI}(capT+1)
default_status = fill(false, capT+1)
B_sim_indices[1], default_status[1] = B_init_ind, false
y_sim_val[1], B_sim_val[1] = ae.ygrid[y_init_ind], ae.Bgrid[B_init_ind]
for t=1:capT
# Get today's indexes
yi, Bi = y_sim_indices[t], B_sim_indices[t]
defstat = default_status[t]
# If you are not in default
if !defstat
default_today = ae.vc[Bi, yi] < ae.vd[yi] ? true: false
if default_today
# Default values
default_status[t] = true
default_status[t+1] = true
y_sim_val[t] = ae.ydefgrid[y_sim_indices[t]]
B_sim_indices[t+1] = zero_index
B_sim_val[t+1] = 0.
q_sim_val[t] = ae.q[zero_index, y_sim_indices[t]]
else
default_status[t] = false
y_sim_val[t] = ae.ygrid[y_sim_indices[t]]
B_sim_indices[t+1] = ae.policy[Bi, yi]
B_sim_val[t+1] = ae.Bgrid[B_sim_indices[t+1]]
q_sim_val[t] = ae.q[B_sim_indices[t+1], y_sim_indices[t]]
end
# If you are in default
else
B_sim_indices[t+1] = zero_index
B_sim_val[t+1] = 0.
y_sim_val[t] = ae.ydefgrid[y_sim_indices[t]]
q_sim_val[t] = ae.q[zero_index, y_sim_indices[t]]
# With probability θ exit default status
if rand() < ae.θ
default_status[t+1] = false
else
default_status[t+1] = true
end
end
end
return (y_sim_val[1:capT], B_sim_val[1:capT], q_sim_val[1:capT],
default_status[1:capT])
end
```

## Results¶

Let’s start by trying to replicate the results obtained in [Are08]

In what follows, all results are computed using Arellano’s parameter values

The values can be seen in the function ArellanoEconomy shown above

- For example, r=0.017 matches the average quarterly rate on a 5 year US treasury over the period 1983–2001

Details on how to compute the figures are reported as solutions to the exercises

The first figure shows the bond price schedule and replicates Figure 3 of Arellano, where \(y_L\) and \(Y_H\) are particular below average and above average values of output \(y\)

- \(y_L\) is 5% below the mean of the \(y\) grid values
- \(y_H\) is 5% above the mean of the \(y\) grid values

The grid used to compute this figure was relatively coarse (ny, nB = 21, 251) in order to match Arrelano’s findings

Here’s the same relationships computed on a finer grid (ny, nB = 51, 551)

In either case, the figure shows that

- Higher levels of debt (larger \(-B'\)) induce larger discounts on the face value, which correspond to higher interest rates
- Lower income also causes more discounting, as foreign creditors anticipate greater likelihood of default

The next figure plots value functions and replicates the right hand panel of Figure 4 of [Are08]

We can use the results of the computation to study the default probability \(\delta(B', y)\) defined in (4)

The next plot shows these default probabilities over \((B', y)\) as a heat map

As anticipated, the probability that the government chooses to default in the following period increases with indebtedness and falls with income

Next let’s run a time series simulation of \(\{y_t\}\), \(\{B_t\}\) and \(q(B_{t+1}, y_t)\)

The grey vertical bars correspond to periods when the economy is excluded from financial markets because of a past default

One notable feature of the simulated data is the nonlinear response of interest rates

Periods of relative stability are followed by sharp spikes in the discount rate on government debt

## Exercises¶

### Exercise 1¶

To the extent that you can, replicate the figures shown above

- Use the parameter values listed as defaults in the function ArellanoEconomy
- The time series will of course vary depending on the shock draws

## Solutions¶

```
using Gadfly, Compose, ColorTypes, DataFrames
```

Compute the value function, policy and equilibrium prices

```
ae = ArellanoEconomy(β=.953, # time discount rate
γ=2., # risk aversion
r=0.017, # international interest rate
ρ=.945, # persistence in output
η=0.025, # st dev of output shock
θ=0.282, # prob of regaining access
ny=21, # number of points in y grid
nB=251) # number of points in B grid
# now solve the model on the grid.
vfi!(ae)
```

```
Finished iteration 25 with dist of 0.3424484168091375
Finished iteration 50 with dist of 0.0982039407428843
Finished iteration 75 with dist of 0.02915866229151476
Finished iteration 100 with dist of 0.008729266837651295
Finished iteration 125 with dist of 0.0026184009381182705
Finished iteration 150 with dist of 0.0007857709211798181
Finished iteration 175 with dist of 0.00023583246008485048
Finished iteration 200 with dist of 7.078195654131036e-5
Finished iteration 225 with dist of 2.1244388765495614e-5
Finished iteration 250 with dist of 6.3762679332057814e-6
Finished iteration 275 with dist of 1.9137668516577833e-6
Finished iteration 300 with dist of 5.743961786208729e-7
Finished iteration 325 with dist of 1.723987352875156e-7
Finished iteration 350 with dist of 5.174360495630026e-8
Finished iteration 375 with dist of 1.5530286390230685e-8
```

Compute the bond price schedule as seen in figure 3 of Arellano (2008)

```
# Create "Y High" and "Y Low" values as 5% devs from mean
high, low = mean(ae.ygrid)*1.05, mean(ae.ygrid)*.95
iy_high, iy_low = map(x->searchsortedfirst(ae.ygrid, x), (high, low))
# Extract a suitable plot grid
x = Float64[]
q_low = Float64[]
q_high = Float64[]
for i=1:ae.nB
b = ae.Bgrid[i]
if -0.35 <= b <= 0 # To match fig 3 of Arellano
push!(x, b)
push!(q_low, ae.q[i, iy_low])
push!(q_high, ae.q[i, iy_high])
end
end
# generate plot
p1=plot(x=repeat(x, outer=[2]), y=[q_low; q_high],
color=repeat([:Low, :High], inner=[length(x)]),
Guide.title("Bond price schedule q(y, B')"),
Guide.xlabel("B'"), Guide.ylabel("q"),
Guide.colorkey("y"), Geom.line)
```

Draw a plot of the value functions

```
p2=plot(x=repeat(ae.Bgrid, outer=[2]),
y=vec(ae.vf[:, [iy_low, iy_high]]),
color=repeat([:Low, :High], inner=[length(ae.Bgrid)]),
Guide.title("Value functions"),
Guide.xlabel("B"), Guide.ylabel("V(y,B)"),
Guide.colorkey("y"), Geom.line)
```

Draw a heat map for default probability

```
p3=plot(x_min=repeat(ae.Bgrid[1:end-1], inner=[ae.ny-1]),
x_max=repeat(ae.Bgrid[2:end], inner=[ae.ny-1]),
y_min=repeat(ae.ygrid[1:end-1], outer=[ae.nB-1]),
y_max=repeat(ae.ygrid[2:end], outer=[ae.nB-1]),
x=(repeat(ae.Bgrid[1:end-1], inner=[ae.ny-1]) + repeat(ae.Bgrid[2:end], inner=[ae.ny-1]))/2,
y=(repeat(ae.ygrid[1:end-1], outer=[ae.nB-1]) + repeat(ae.ygrid[2:end], outer=[ae.nB-1]))/2,
color=clamp.(vec(ae.defprob[1:end-1, 1:end-1]'), 0, 1),
Geom.rectbin,
Guide.xlabel("B'"), Guide.ylabel("y"),
Guide.title("Probability of default"), Geom.rectbin,
Scale.y_continuous(minvalue=0.8, maxvalue=1.2),
Scale.x_continuous(minvalue=minimum(ae.Bgrid), maxvalue=0.0),
Scale.color_continuous(minvalue=0, maxvalue=1))
```

Plot a time series of major variables simulated from the model

```
# simulate
T = 250
y_vec, B_vec, q_vec, default_vec = simulate(ae, T)
# find starting and ending periods of recessions (if any)
if any(default_vec)
defs = find(default_vec)
def_breaks = diff(defs) .> 1
def_start = defs[[true; def_breaks]]
def_end = defs[[def_breaks; true]]
else
starts, ends = Int[], Int[]
end
# construct boxes that shade periods of default
def_box = Guide.annotation(compose(context(),
[rectangle(i[1], 0h, i[2]-i[1], 1h)
for i=zip(def_start, def_end)]...,
fill(RGBA(0.5, 0.5, 0.5, 0.2))))
# xy labels are common for all plots
xy_lab = [Guide.xlabel("time"), Guide.ylabel("")]
# now iterate over three variables and put them into an array
p4 = Gadfly.Plot[]
for (vec, name) in [(y_vec, "Output"), (B_vec, "Foreign assets"), (q_vec, "Bond price")]
push!(p4,
plot(x=1:T, y=vec, Geom.line, def_box, Guide.title(name), xy_lab...))
end
# set final plot height and vertically stack the above three plots
set_default_plot_size(6inch, 8inch)
vstack(p4...)
```