Pandemic meltdown and economic recovery – A multi-phase dynamic model, empirics, and policy

In this paper a two-phase pandemic-economic model is proposed, with phase-specific modeling and policy variables – as suggested by the chronicle of pandemic and economic policy developments over the period 2020–2021. In a first phase, the spread of a pandemic disease is the primary concern of authorities, that still also pay attention to economic activity. A dynamic model is introduced, embedding a two-way interaction between an extended epidemic Susceptible-Infected-Recovered (SIR) model and output gap dynamics. In the second phase, posterior to lockdowns when waves fade away, monetary policy becomes the control variable, pursuing again a joint objective, of supporting a non-inflationary recovery without causing significant fatalities. We then use a standard stylized model for the macroeconomy with simplified infection dynamics, that also enter the policy objective. The two phases are thus studied in a regime change model where the control and state variables as well as the objective function are allowed to change across phases. We solve the model over a finite horizon and derive the optimal lockdown or monetary policy path that jointly minimizes pandemic and economic losses. The two-phase finite horizon decision model is empirically calibrated and numerically solved by discretization and optimization methods. In the first phase, albeit with lasting adverse effects on output, lockdown-based control can be effective in reducing infection rates, but less so when starting from a negative output gap. In the second phase, accommodative monetary policy appears to be effective on both fronts, with even an eventual need for a return to tightening as output gap closes and inflation resumes. © 2022 The Author(s)

Pandemic meltdown and economic recovery – A multi-phase dynamic model, empirics, and policy

In this paper a two-phase pandemic-economic model is proposed, with phase-specific modeling and policy variables – as suggested by the chronicle of pandemic and economic policy developments over the period 2020–2021. In a first phase, the spread of a pandemic disease is the primary concern of authorities, that still also pay attention to economic activity. A dynamic model is introduced, embedding a two-way interaction between an extended epidemic Susceptible-Infected-Recovered (SIR) model and output gap dynamics. In the second phase, posterior to lockdowns when waves fade away, monetary policy becomes the control variable, pursuing again a joint objective, of supporting a non-inflationary recovery without causing significant fatalities. We then use a standard stylized model for the macroeconomy with simplified infection dynamics, that also enter the policy objective. The two phases are thus studied in a regime change model where the control and state variables as well as the objective function are allowed to change across phases. We solve the model over a finite horizon and derive the optimal lockdown or monetary policy path that jointly minimizes pandemic and economic losses. The two-phase finite horizon decision model is empirically calibrated and numerically solved through AMPL, a new solution method for finite horizon dynamic models. In the first phase, albeit with lasting adverse effects on output, lockdown-based control can be effective in reducing infection rates, but less so when starting from a negative output gap. In the second phase, accommodative monetary policy appears to be effective on both fronts, with even an eventual need for a return to tightening as output gap closes and inflation resumes.

Outline of the Evolution of the Hungarian Monetary Policy from the Restoration of the Two-level Banking System to the Present Day

This study outlines the development of Hungary’s monetary policy, and the course and changes in its objectives and instruments since the beginning of the market economy transition in the late 1980s. The author’s basic thesis is that the period since the two-level banking system was reinstated after four decades of a planned economy system, in 1987, can be basically divided into three development phases with significantly different characteristics. The first phase was an ‘attempt to introduce’ an imported monetary mechanism, or perhaps an urge to comply with it, while the second phase was an approach of a monetary regime change launched in 2013 and supporting economic growth and financial stability strongly and directly, which lasted until the appearance of the traumatic elements of the Covid-19 pandemic crisis. The third phase is evolving today, under the circumstances of adapting to the conditions of the real essence of the twenty-first century, i.e. a new type of international competitiveness, which is pursued by the Central Bank of Hungary as stipulated by the Fundamental Law and the cardinal Central Bank Act of Hungary.

Finite mixture model of hidden Markov regression with covariate dependence

In recent days, a combination of finite mixture model (FMM) and hidden Markov model (HMM) is becoming popular for partitioning heterogeneous temporal data into homogeneous groups (clusters) with homogeneous time points (regimes). The regression mixtures commonly considered in this approach can also accommodate for covariates present in data. The classical fixed covariate approach, however, may not always serve as a reasonable assumption as it is incapable of accounting for the contribution of covariates in cluster formation. This paper introduces a novel approach for detecting clusters and regimes in time series data in the presence of random covariates. The computational challenges related to the proposed model has been discussed, and several simulation studies are performed. An application to United States COVID-19 data yields meaningful clusters and regimes.