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We'll want first and second moments of some key random variables below.
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@@ -136,7 +136,7 @@ def covariance(x, y, s):
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return x * y @ u.π[s] - mean(x, s) * mean(y, s)
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```
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## Long Simulation
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## Long simulation
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To generate a long simulation we use the following code.
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@@ -263,7 +263,7 @@ these early observations
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* early observations are more influenced by the initial value of the par value of government debt than by the ergodic mean of the par value of government debt
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* much later observations are more influenced by the ergodic mean and are independent of the par value of initial government debt
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## Asymptotic Mean and Rate of Convergence
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## Asymptotic mean and rate of convergence
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We apply the results of BEGS {cite}`BEGS1` to interpret
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@@ -310,7 +310,7 @@ BEGS interpret random variations in the right side of {eq}`eq_fiscal_risk_1` as
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${\mathcal R}_\tau(s, s_{-}) {\mathcal B}_{-}$, and
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- fluctuations in the effective government deficit ${\mathcal X}_t$
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### Asymptotic Mean
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### Asymptotic mean
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BEGS give conditions under which the ergodic mean of ${\mathcal B}_t$ is approximated by
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@@ -347,7 +347,7 @@ AMSS model should be approximately
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where mathematical expectations are taken with respect to the ergodic distribution.
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### Rate of Convergence
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### Rate of convergence
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BEGS also derive the following approximation to the rate of convergence to ${\mathcal B}^{*}$ from an arbitrary initial condition.
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@@ -361,13 +361,13 @@ BEGS also derive the following approximation to the rate of convergence to ${\m
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(See the equation above equation (47) in BEGS {cite}`BEGS1`)
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### More Advanced Topic
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### More advanced topic
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The remainder of this lecture is about technical material based on formulas from BEGS {cite}`BEGS1`.
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The topic involves interpreting and extending formula {eq}`eq_criterion_fiscal_1` for the ergodic mean ${\mathcal B}^*$.
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### Chicken and Egg
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### Chicken and egg
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Notice how attributes of the ergodic distribution for ${\mathcal B}_t$ appear
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on the right side of formula {eq}`eq_criterion_fiscal_1` for approximating the ergodic mean via ${\mathcal B}^*$.
@@ -385,7 +385,7 @@ As an example, notice how we used the formula for the mean of ${\mathcal B}$ in
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* **first** we computed the ergodic distribution using a reverse-engineering construction
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* **then** we verified that ${\mathcal B}^*$ agrees with the mean of that distribution
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### Approximating the Ergodic Mean
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### Approximating the ergodic mean
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BEGS also {cite}`BEGS1` propose an approximation to ${\mathcal B}^*$ that can be computed **without** first approximating the
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ergodic distribution.
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Here is a step-by-step description of the BEGS {cite}`BEGS1` approximation procedure.
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### Step by Step
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### Step by step
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**Step 1:** For a given $\tau$ we compute a vector of
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values $c_\tau(s), s= 1, 2, \ldots, S$ that satisfy
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