I’m an applied macroeconomist, currently focusing on fiscal policy analysis and macroeconomic forecasting. My work often uses DSGE and VAR models with real-time data. 

Masaryk University, Department of Economics | Associate Professor
Lipova 41a | 60200 Brno | Czech Republic

Jan.Capek@econ.muni.cz 

Scholar | RePEc | ORCID | Masaryk U

with Jesús Crespo Cuaresma — Oxford Bulletin of Economics and Statistics 82(3), 483-502 (2020), lead article

We analyse the role played by data and specification choices as determinants of the size of the fiscal multipliers obtained using structural vector autoregressive models. The results, based on over twenty million fiscal multipliers estimated for European countries, indicate that many seemingly harmless modelling choices have a significant effect on the size and precision of fiscal multiplier estimates. In addition to the structural shock identification strategy, these modelling choices include the definition of spending and taxes, the national accounts system employed, the use of particular interest rates or inflation measures, or whether data are smoothed prior to estimation. The cumulative effects of such arguably innocuous methodological choices can lead to a change in the spending multipliers of as much as 0.4 points.

with Jesús Crespo Cuaresma, Niko Hauzenberger, Vlastimil Reichel — International Journal of Forecasting 39(4), 1820-1838 (2023)

We provide a comprehensive assessment of the predictive power of combinations of dynamic stochastic general equilibrium (DSGE) models for GDP growth, inflation, and the interest rate in the euro area. We employ a battery of static and dynamic pooling weights based on Bayesian model averaging principles, prediction pools, and dynamic factor representations, and entertain six different DSGE specifications and five prediction weighting schemes. Our results indicate that exploiting mixtures of DSGE models produces competitive forecasts compared to individual specifications for both point and density forecasts over the last three decades. Although these combinations do not tend to systematically achieve superior forecast performance, we find improvements for particular periods of time and variables when using prediction pooling, dynamic model averaging, and combinations of forecasts based on Bayesian predictive synthesis.

with Jakub Chalmovianský, Vlastimil Reichel — Economic Inquiry 64(3), 1055-1078 (2026)

This study systematically evaluates forecasting performance of 11 DSGE and 2 BVAR models during recessions and expansions in the US and the euro area. Results show that no single model dominates: parsimonious models perform well in stable periods and at short horizons, while richer DSGE specifications with financial frictions, flexible inflation targeting, or labor market dynamics improve forecasts during recessions. BVARs excel in interest rate forecasting, especially in expansions. Crisis-specific extensions, such as Covid-related shocks, yield temporary gains. Forecast accuracy depends on the economic state, variable, horizon, and evaluation metric, underscoring the need for a diversified, context-dependent modeling toolkit.

with Jesús Crespo Cuaresma, Jakub Chalmovianský, Vlastimil Reichel — Oxford Bulletin of Economics and Statistics 87(6), 1059-1080 (2025)

We evaluate the impact of real-time macroeconomic data and data revisions on the forecasting performance of DSGE models in the US and the euro area. We identify significant differences in data revisions between the two world regions: Negative revisions (due to overestimation in early data releases) are prevalent in the US, while the euro area data tends to be dominated by positive revisions. These biases are most significant in consumption growth, output growth, and hours worked. Parameter estimates in small-sized DSGE models are not strongly affected by the use of real-time data, while larger models exhibit substantial differences depending on the data used, especially during large economic downturns. Revisions significantly affect the predictive accuracy of the DSGE model for output growth in the US and for inflation and interest rates in the euro area. Our findings highlight the central role of data revisions as a determinant of predictive accuracy in macroeconomic models and thus of the quality of such specifications as an instrument to inform evidence-based policy-making.

with Jesús Crespo Cuaresma, Johannes Holler, Philip Schuster — Oxford Economic Papers 74(2), 412-430 (2022)

We estimate fiscal multipliers for Austria in a framework of model uncertainty emanating from the choice of a particular econometric model. We present a comprehensive framework which allows to assess the effects of different multiplier definitions and choices related to the data, the model employed, and further technical choices associated with the specification of the model exert on fiscal multiplier estimates. The mean present-value government spending multiplier over all models entertained, based on over one thousand estimates, is 0.94. Estimates of the peak spending multiplier tend to be larger than present-value spending multipliers, with a mean value of 1.08. The value of the mean presentvalue tax multiplier is -0.76 and the mean peak tax multiplier is -0.58 for all specifications used.

[Link to the authors' version (post-print) of a work that was accepted for publication in Oxford Economic Paper. Due to a technical problem, the published version did not include the last revisions carried out in the manuscript.]