Empirical evidence on the monetary-fiscal policy mix
and macroeconomic (in)stability in the US

Asociación Argentina de Economía Política

Emiliano Carlevaro

University of Adelaide

Qazi Haque

University of Adelaide

Leandro Magnusson

University of Western Australia

2025-11-28

THE GREAT INFLATION

Inflation analogies by Cochrane (2025)

THE GREAT INFLATION(S)?

Inflation analogies by Cochrane (2025)

WHO DETERMINES INFLATION?

Inflation is always and everywhere a monetary phenomenon.
Milton Friedman

Persistent high inflation is always and everywhere a fiscal phenomenon, in which the central bank is a monetary accomplice.
Thomas J. Sargent

The policy rules

Monetary policy rule

\[ R_t \,=\, \color{blue}{\phi_{\pi}} \pi_t + g_m(x_t ; \rho_R, \color{blue}{\phi_{x}} ) + \epsilon_{R, t} \]

\[ \pi_t \equiv \frac{P_t}{P_{t-1}} \]

Fiscal policy rule

\[ \tau_{t}= \color{blue}{\psi_{b}} b_{t-1} + g_f(x_t ; \rho_{\tau}, \color{blue}{\psi_{x}} ) + \epsilon_{\tau, t} \]

\[ \begin{align} \tau \equiv \frac{T_t}{P_t Y_t} & \,\,\,\,\,\,\,\ b_{t} \equiv \frac{B_t}{P_t Y_t} \\ \end{align} \]

The government budget constraint

\[ \begin{align} R_t \,&=\, \color{blue}{\phi_{\pi}} \pi_t + g_m(x_t ; \rho_R, \color{blue}{\phi_{x}} ) + \epsilon_{R, t} \\ \tau_{t} &= \color{blue}{\psi_{b}} b_{t-1} + g_f(x_t ; \rho_{\tau}, \color{blue}{\psi_{x}} ) + \epsilon_{\tau, t} \end{align} \]

The govt budget constraint is

\[ b_{t} \approx (1 - \color{blue}{\psi_{b}}) b_{t-1} + (\mathbb{E}_{t-1} \pi_{t} - \pi_t) - \epsilon_{\tau, t} + \ldots \]

  • If \(\psi_b > 0\), debt level is stable

  • If \(\psi_b < 0\), \(\pi_t\) has to “accomodate”

    • Budget constraints are always satisfied!

Equilibrium depends primarily upon \(\phi_{\pi}\) and \(\psi_{b}\).

  • 4 possible cases (regimes).

Why do we care about regimes?

\[ b_{t} \approx (1 - \color{blue}{\psi_{b}}) b_{t-1} + (\mathbb{E}_{t-1} \pi_{t} - \pi_t) - \epsilon_{\tau, t} + \ldots \]

EMPIRICAL ISSUES: Lack of consensus

Endogeneity and weak identification

\[ R_t \,=\, \color{blue}{\phi_{\pi}} \pi_t + \epsilon_{R, t} \]

  • Instrumental variable estimator:
    • Instrument: \(\pi_{t-1}\).
    • Weak identification: \(\pi_{t}\) moves little
      • For example: \(\pi_t \rightarrow \pi^*\)

An additional instrument

\[ \begin{align} R_t \,&=\, \color{blue}{\phi_{\pi}} \pi_t + \epsilon_{R, t} \\ \pi_t &= \rho \pi_{t-1} + \gamma R_t + \varepsilon_{\pi, t} \end{align} \]

An additional instrument

\[ \begin{align} R_t \,&=\, \color{blue}{\phi_{\pi}} \pi_t + \epsilon_{R, t} \\ \pi_t &= \rho \pi_{t-1} + \gamma R_t + \varepsilon_{\pi, t} \end{align} \]

An additional instrument

\[ \begin{align} R_t \,&=\, \color{blue}{\phi_{\pi}} \pi_t + \epsilon_{R, t} \\ \pi_t &= \color{red}{\rho}_t \pi_{t-1} + \gamma R_t + \varepsilon_{\pi, t} \end{align} \]

Structural breaks as instruments

\[ \begin{align} R_t \,&=\, \color{blue}{\phi_{\pi}} \pi_t + \epsilon_{R, t} \\ \pi_t &= \color{red}{\rho}_t \pi_{t-1} + \gamma R_t + \varepsilon_{\pi, t} \end{align} \]

  • \(\pi_{t-1}\)
  • \(\mathcal{1}_{\{t>50\}} \pi_{t-1}\)

Structural breaks free up information

What if we don’t know the break dates?

  • Do hypothesis testing.
    • Fix \(H_0: \color{blue}{\phi_{\pi, 0}}\) and test whether \(\hat{\epsilon}_{R, t}\) is sufficiently stable.
    • The test has power if \(\rho\) shifts
    • If \(\rho\) does not shift, you will know it!

Identification by stability restrictions

Exploit instabilities in subsamples for identification (Magnusson and Mavroeidis 2014).

  1. Let \(\color{blue}{\mathbf{\theta_0}} \equiv \left[ \phi_{\pi}, \phi_x, \psi_{b}, \psi_{x}\right]\) be possible candidate parameter values.
  2. Fix the parameters under the null \[ H_0 : \color{blue}{\mathbf{\theta}} = \left[ \phi_{\pi, 0}, \phi_{x,0}; \psi_{b, 0}, \psi_{x, 0} \right] \]
  3. Compute “residuals” and the S and generalised-S test statistic.
  4. Keep \(\color{blue}{\mathbf{\theta_0}}\) if the test statistic is below the critical value.
  5. Rinse and repeat (grid search)
  • A confidence set comprises all \(\color{blue}{\mathbf{\theta_0}}\) that do not reject \(H_0\).

DATA

  • Before Volcker: 1961 Q1 to 1979 Q2 (74 observations)
  • After Volcker: 1984 Q1 to 2008 Q4 (100 observations)



\(R\) effective Federal Funds rate \(\tau\) current tax receipts + social insurance contributions
\(\pi\) CPI \(b\) market value of privately held gross federal debt
\(x\) Output gap as estimated by CBO \(Y\) Gross Domestic Product (GDP)

Source is FRED.

CONFIDENCE SETS: before Volcker

S set
img
90% confidence sets Indeterminacy, monetary, fiscal. 1961q1 - 1979q2.

CONFIDENCE SETS: before Volcker

genS-qLL set
Image1
90% confidence sets Indeterminacy, monetary, fiscal. 1961q1 - 1979q2.

CONFIDENCE SETS: after Volcker

S set
Image1
90% confidence sets Indeterminacy, monetary, fiscal. 1984q1 - 2008q4.

RESULTS: after Volcker

genS-qLL set
Image1
90% confidence sets Indeterminacy, monetary, fiscal. 1984q1 - 2008q4

ROBUSTNESS: Alternative policy rules

AR(2) monetary policy smoothing

Forward-looking monetary policy rule