This lecture is part of a suite of lectures that use the quantecon DLE class to instantiate models within the [HS13] class of models described in detail in Recursive Models of Dynamic Linear Economies.

In addition to what’s included in Anaconda, this lecture uses the quantecon library.

In [ ]:

```
!pip install --upgrade quantecon
```

This lecture adds a third solution method for the linear-quadratic-Gaussian permanent income model with $ \beta R = 1 $, complementing the other two solution methods described in Optimal Savings I: The Permanent Income Model and Optimal Savings II: LQ Techniques and this Jupyter notebook.

The additional solution method uses the **DLE** class.

In this way, we map the permanent income model into the framework of Hansen & Sargent (2013) “Recursive Models of Dynamic Linear Economies” [HS13].

We’ll also require the following imports

In [ ]:

```
import quantecon as qe
import numpy as np
import scipy.linalg as la
import matplotlib.pyplot as plt
%matplotlib inline
from quantecon import DLE
np.set_printoptions(suppress=True, precision=4)
```

The LQ permanent income model is an example of a **savings problem**.

A consumer has preferences over consumption streams that are ordered by the utility functional

$$ E_0 \sum_{t=0}^\infty \beta^t u(c_t) \tag{22.1} $$

where $ E_t $ is the mathematical expectation conditioned on the consumer’s time $ t $ information, $ c_t $ is time $ t $ consumption, $ u(c) $ is a strictly concave one-period utility function, and $ \beta \in (0,1) $ is a discount factor.

The LQ model gets its name partly from assuming that the utility function $ u $ is quadratic:

$$ u(c) = -.5(c - \gamma)^2 $$where $ \gamma>0 $ is a bliss level of consumption.

The consumer maximizes the utility functional (22.1) by choosing a consumption, borrowing plan $ \{c_t, b_{t+1}\}_{t=0}^\infty $ subject to the sequence of budget constraints

$$ c_t + b_t = R^{-1} b_{t+1} + y_t, t \geq 0 \tag{22.2} $$

where $ y_t $ is an exogenous stationary endowment process, $ R $ is a constant gross risk-free interest rate, $ b_t $ is one-period risk-free debt maturing at $ t $, and $ b_0 $ is a given initial condition.

We shall assume that $ R^{-1} = \beta $.

Equation (22.2) is linear.

We use another set of linear equations to model the endowment process.

In particular, we assume that the endowment process has the state-space representation

$$ \begin{aligned} z_{t+1} & = A_{22} z_t + C_2 w_{t+1} \cr y_t & = U_y z_t \cr \end{aligned} \tag{22.3} $$

where $ w_{t+1} $ is an IID process with mean zero and identity contemporaneous covariance matrix, $ A_{22} $ is a stable matrix, its eigenvalues being strictly below unity in modulus, and $ U_y $ is a selection vector that identifies $ y $ with a particular linear combination of the $ z_t $.

We impose the following condition on the consumption, borrowing plan:

$$ E_0 \sum_{t=0}^\infty \beta^t b_t^2 < +\infty \tag{22.4} $$

This condition suffices to rule out Ponzi schemes.

(We impose this condition to rule out a borrow-more-and-more plan that would allow the household to enjoy bliss consumption forever)

The state vector confronting the household at $ t $ is

$$ x_t = \begin{bmatrix} z_t \\ b_t \end{bmatrix} $$where $ b_t $ is its one-period debt falling due at the beginning of period $ t $ and $ z_t $ contains all variables useful for forecasting its future endowment.

We assume that $ \{y_t\} $ follows a second order univariate autoregressive process:

$$ y_{t+1} = \alpha + \rho_1 y_t + \rho_2 y_{t-1} + \sigma w_{t+1} $$One way of solving this model is to map the problem into the framework outlined in Section 4.8 of [HS13] by setting up our technology, information and preference matrices as follows:

**Technology:**
$ \phi_c= \left[ {\begin{array}{c} 1 \\ 0 \end{array} } \right] $
,
$ \phi_g= \left[ {\begin{array}{c} 0 \\ 1 \end{array} } \right] $
,
$ \phi_i= \left[ {\begin{array}{c} -1 \\ -0.00001 \end{array} } \right] $,
$ \Gamma= \left[ {\begin{array}{c} -1 \\ 0 \end{array} } \right] $,
$ \Delta_k = 0 $, $ \Theta_k = R $.

**Information:**
$ A_{22} = \left[ {\begin{array}{ccc} 1 & 0 & 0 \\ \alpha & \rho_1 & \rho_2 \\ 0 & 1 & 0 \end{array} } \right] $,
$ C_{2} = \left[ {\begin{array}{c} 0 \\ \sigma \\ 0 \end{array} } \right] $,
$ U_b = \left[ {\begin{array}{ccc} \gamma & 0 & 0 \end{array} } \right] $,
$ U_d = \left[ {\begin{array}{ccc} 0 & 1 & 0 \\ 0 & 0 & 0 \end{array} } \right] $.

**Preferences:** $ \Lambda = 0 $, $ \Pi = 1 $,
$ \Delta_h = 0 $, $ \Theta_h = 0 $.

We set parameters

$ \alpha = 10, \beta = 0.95, \rho_1 = 0.9, \rho_2 = 0, \sigma = 1 $

(The value of $ \gamma $ does not affect the optimal decision rule)

The chosen matrices mean that the household’s technology is:

$$ c_t + k_{t-1} = i_t + y_t $$$$ \frac{k_t}{R} = i_t $$$$ l_t^2 = (0.00001)^2i_t $$Combining the first two of these gives the budget constraint of the permanent income model, where $ k_t = b_{t+1} $.

The third equation is a very small penalty on debt-accumulation to rule out Ponzi schemes.

We set up this instance of the DLE class below:

In [ ]:

```
α, β, ρ_1, ρ_2, σ = 10, 0.95, 0.9, 0, 1
γ = np.array([[-1], [0]])
ϕ_c = np.array([[1], [0]])
ϕ_g = np.array([[0], [1]])
ϕ_1 = 1e-5
ϕ_i = np.array([[-1], [-ϕ_1]])
δ_k = np.array([[0]])
θ_k = np.array([[1 / β]])
β = np.array([[β]])
l_λ = np.array([[0]])
π_h = np.array([[1]])
δ_h = np.array([[0]])
θ_h = np.array([[0]])
a22 = np.array([[1, 0, 0],
[α, ρ_1, ρ_2],
[0, 1, 0]])
c2 = np.array([[0], [σ], [0]])
ud = np.array([[0, 1, 0],
[0, 0, 0]])
ub = np.array([[100, 0, 0]])
x0 = np.array([[0], [0], [1], [0], [0]])
info1 = (a22, c2, ub, ud)
tech1 = (ϕ_c, ϕ_g, ϕ_i, γ, δ_k, θ_k)
pref1 = (β, l_λ, π_h, δ_h, θ_h)
econ1 = DLE(info1, tech1, pref1)
```

To check the solution of this model with that from the **LQ** problem,
we select the $ S_c $ matrix from the DLE class.

The solution to the DLE economy has:

$$ c_t = S_c x_t $$In [ ]:

```
econ1.Sc
```

The state vector in the DLE class is:

$$ x_t = \left[ {\begin{array}{c} h_{t-1} \\ k_{t-1} \\ z_t \end{array} } \right] $$where $ k_{t-1} $ = $ b_{t} $ is set up to be $ b_t $ in the permanent income model.

The state vector in the LQ problem is $ \begin{bmatrix} z_t \\ b_t \end{bmatrix} $.

Consequently, the relevant elements of econ1.Sc are the same as in $ -F $ occur when we apply other approaches to the same model in the lecture Optimal Savings II: LQ Techniques and this Jupyter notebook.

The plot below quickly replicates the first two figures of that lecture and that notebook to confirm that the solutions are the same

In [ ]:

```
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
for i in range(25):
econ1.compute_sequence(x0, ts_length=150)
ax1.plot(econ1.c[0], c='g')
ax1.plot(econ1.d[0], c='b')
ax1.plot(econ1.c[0], label='Consumption', c='g')
ax1.plot(econ1.d[0], label='Income', c='b')
ax1.legend()
for i in range(25):
econ1.compute_sequence(x0, ts_length=150)
ax2.plot(econ1.k[0], color='r')
ax2.plot(econ1.k[0], label='Debt', c='r')
ax2.legend()
plt.show()
```