Marko Mlikota
Hi!
I’m an Economics Ph.D. student at the University of Pennsylvania.
My research interests are in time series econometrics and (international) macro-finance. In particular, I analyze the implications of networks for cross-sectional dynamics (think of prices across industries linked by the supply chain network, or economic activity across countries linked via trade and capital flows).
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Contact: mlikota@sas.upenn.edu
Work in Progress
How Does a Dominant Currency Replace Another? Evidence from European Trade, with A. Mehl & I. van Robays (European Central Bank)
Abstract:
We assess why a dominant currency in international trade invoicing can be replaced with another by contrasting two hypotheses stressed in recent theory: increased trade and reduced exchange rate volatility vis-à-vis the emergent dominant currency area. Our study focuses on 13 European economies that saw marked increases in the use of the euro at the expense of the US dollar for trade invoicing. We show how theory maps itself into a network which links together invoicing currency decisions across countries and develop a fitting Panel-Vector autoregression to jointly model invoicing, trade and exchange rate volatility dynamics across countries, while allowing for cross-country effects emphasized in theory. We identify for each country a “trade shock” and an “exchange rate volatility shock”, finding significant evidence in support of the increased trade hypothesis. Our estimates suggest that in countries where trade with the Euro Area increased, the latter explains almost 40% of the rise in euro invoicing from 1999 to 2019. In contrast, the impact of greater exchange rate stability against the euro is found to be insignificant. Importantly, a country’s invoicing decision is significantly influenced by those of other countries within the regional trade network. This effect operates mainly via bilateral trade linkages rather than strategic complementarities in export price setting, which point to the relevance of changes to input-output linkages in making or breaking dominant currencies.
We assess why a dominant currency in international trade invoicing can be replaced with another by contrasting two hypotheses stressed in recent theory: increased trade and reduced exchange rate volatility vis-à-vis the emergent dominant currency area. Our study focuses on 13 European economies that saw marked increases in the use of the euro at the expense of the US dollar for trade invoicing. We show how theory maps itself into a network which links together invoicing currency decisions across countries and develop a fitting Panel-Vector autoregression to jointly model invoicing, trade and exchange rate volatility dynamics across countries, while allowing for cross-country effects emphasized in theory. We identify for each country a “trade shock” and an “exchange rate volatility shock”, finding significant evidence in support of the increased trade hypothesis. Our estimates suggest that in countries where trade with the Euro Area increased, the latter explains almost 40% of the rise in euro invoicing from 1999 to 2019. In contrast, the impact of greater exchange rate stability against the euro is found to be insignificant. Importantly, a country’s invoicing decision is significantly influenced by those of other countries within the regional trade network. This effect operates mainly via bilateral trade linkages rather than strategic complementarities in export price setting, which point to the relevance of changes to input-output linkages in making or breaking dominant currencies.
Working Papers
Cross-Sectional Dynamics Under Network Structure: Theory and Macroeconomic Applications [Job Market Paper]
Abstract:
Many environments in economics feature a cross-section of agents or units linked by a network of bilateral ties. I develop a framework to study dynamics in these cases. It consists of a vector autoregression in which innovations transmit cross-sectionally via bilateral links and which can accommodate general patterns of how network effects of higher order accumulate over time. In a first application, I take the supply chain network of the US economy as given and document how it drives the dynamics of sectoral prices. By estimating the time profile of network effects, the model allows me to go beyond steady state comparisons and study transition dynamics induced by granular shocks. As a result of different positions in the input-output network, sectors differ in both the strength and the timing of their impact on aggregates. In a second application, I discuss how to approximate cross-sectional processes by assuming that dynamics are driven by a network and in turn estimating the latter. The proposed framework offers a sparse, yet flexible and interpretable method for doing so, owing to networks’ ability to summarize complex relations among units by relatively few non-zero bilateral links. Modeling industrial production growth across 44 countries, I obtain reductions in out-of-sample mean squared errors of up to 20% relative to a principal components factor model.
Many environments in economics feature a cross-section of agents or units linked by a network of bilateral ties. I develop a framework to study dynamics in these cases. It consists of a vector autoregression in which innovations transmit cross-sectionally via bilateral links and which can accommodate general patterns of how network effects of higher order accumulate over time. In a first application, I take the supply chain network of the US economy as given and document how it drives the dynamics of sectoral prices. By estimating the time profile of network effects, the model allows me to go beyond steady state comparisons and study transition dynamics induced by granular shocks. As a result of different positions in the input-output network, sectors differ in both the strength and the timing of their impact on aggregates. In a second application, I discuss how to approximate cross-sectional processes by assuming that dynamics are driven by a network and in turn estimating the latter. The proposed framework offers a sparse, yet flexible and interpretable method for doing so, owing to networks’ ability to summarize complex relations among units by relatively few non-zero bilateral links. Modeling industrial production growth across 44 countries, I obtain reductions in out-of-sample mean squared errors of up to 20% relative to a principal components factor model.
Sequential Monte Carlo with Model Tempering, with F. Schorfheide (University of Pennsylvania), conditionally accepted, Studies in Nonlinear Dynamics & Econometrics
Abstract:
Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and two nonlinear dynamic stochastic general equilibrium models. The runtime reductions we obtain range from 27% to 88%.
Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and two nonlinear dynamic stochastic general equilibrium models. The runtime reductions we obtain range from 27% to 88%.
Publications
SVARs with Occasionally-Binding Constraints, Aruoba, S.B., Mlikota, M., Schorfheide, F., Villalvazo, S. (2022), Journal of Econometrics , 231(2), 477-499
Abstract:
We develop a structural VAR in which an occasionally-binding constraint generates censoring of one of the dependent variables. Once the censoring mechanism is triggered, we allow some of the coefficients for the remaining variables to change. We show that a necessary condition for a unique reduced form is that regression functions for the non-censored variables are continuous at the censoring point and that parameters satisfy some mild restrictions. In our application the censored variable is a nominal interest rate constrained by an effective lower bound (ELB). According to our estimates based on U.S. data, once the ELB becomes binding, the coefficients in the inflation equation change significantly, which translates into a change of the inflation responses to (unconventional) monetary policy and demand shocks. Our results suggest that the presence of the ELB is indeed empirically relevant for the propagation of shocks. We also obtain a shadow interest rate that shows a significant accommodation in the early phase of the Great Recession, followed by a mild and steady accommodation until liftoff in 2016.
We develop a structural VAR in which an occasionally-binding constraint generates censoring of one of the dependent variables. Once the censoring mechanism is triggered, we allow some of the coefficients for the remaining variables to change. We show that a necessary condition for a unique reduced form is that regression functions for the non-censored variables are continuous at the censoring point and that parameters satisfy some mild restrictions. In our application the censored variable is a nominal interest rate constrained by an effective lower bound (ELB). According to our estimates based on U.S. data, once the ELB becomes binding, the coefficients in the inflation equation change significantly, which translates into a change of the inflation responses to (unconventional) monetary policy and demand shocks. Our results suggest that the presence of the ELB is indeed empirically relevant for the propagation of shocks. We also obtain a shadow interest rate that shows a significant accommodation in the early phase of the Great Recession, followed by a mild and steady accommodation until liftoff in 2016.