Jakub Boratyński

(English) PDF


It is common to address the problem of uncertainty in computable general equilibrium modeling by sensitivity analysis. The relevant studies of the effects of parameter uncertainty usually focus on various elasticity parameters. In this paper we undertake sensitivity analysis with respect to the parameters derived from calibration to a benchmark data set, describing the structure of the economy. We use a time series of benchmark databases for the years 1996-2005 for Poland to sequentially calibrate a static CGE model, and examine the dispersion of endogenous variables’ responses in three distinct simulation experiments.
We find a part – though not the most – of the results to be significantly sensitive to the choice of calibration database (including ambiguities about the direction of response). The dispersion of the results and its sources clearly depend on the shock in question. Uncertainty is also quite diverse between variables. It is thus recommended that a thorough parametric sensitivity analysis be a conventional part of a simulation study. Also, the reliability of results would likely benefit even from simple, trend-based updates of the benchmark data, as the responses of endogenous variables exhibit systematic changes, observed when the model is calibrated to the data for consecutive years.


computable general equilibrium (CGE) modeling, sensitivity analysis, calibration


Arndt C., (1996), An Introduction to Systematic Sensitivity Analysis via Gaussian Quadrature, GTAP Technical Papers 02, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.

Böhringer C., Rutherford T. F., (2013), Transition Towards a Low Carbon Economy: A Computable General Equilibrium Analysis for Poland, Energy Policy, 55, 16–26.

Boratyński J., (2011), Zastosowanie systematycznej analizy wrażliwości w symulacjach na podstawie statycznego modelu równowagi ogólnej (CGE) (Systematic Sensitivity Analysis of Simulation Results in a Static Computable General Equilibrium Model), Bank i Kredyt, 42 (2), 67–96.

Boratyński J., Borowski J., (2012), The Long-Term Economic Impact of the Flat Tax in Poland. CGE simulation under alternative assumptions, Bank i Kredyt, 43 (3), 5–30.

Borowski J., Boratyński J., Czerniak A., Dykas P., Plich M., Rapacki R., Tokarski T., (2011), Długookresowy wpływ organizacji EURO 2012 na gospodarkę polską, Ekonomista, 4, 493–525.

Borowski J., Boratynski J., Czerniak A., Dykas P., Plich M., Rapacki R., Tokarski T., (2013), Assessing the Impact of the 2012 European Football Championships on the Polish Economy, International Journal of Sport Management and Marketing, 13 (1), 74–103.

Cardenete M. A., Sancho F., (2004), Sensitivity of CGE Simulations to Competing SAM Updates, The Review of Regional Studies, 34 (1), 37–56.

Dawkins C., (2005), Extended Sensitivity Analysis for Applied General Equilibrium Models, Revista de Economia del Rosario, 8 (2), 85–111.

DeVuyst E. A., Preckel P. V., (1997), Sensitivity Analysis Revisited: A Quadrature-Based Approach, Journal of Policy Modeling, 19 (2), 175–185.

Dixon P. B., Jorgenson D. W. (eds.), (2013), Handbook of Computable General Equilibrium Modeling, Vol. 1A and 1B, North-Holland.

Dixon P. B., Koopman R. B., Rimmer M. T., (2013), The MONASH Style of CGE Modeling, in: Dixon P. B., Jorgenson D. W., (eds.), Handbook of Computable General Equilibrium Modeling, North-Holland.

Dixon P. B., Parmenter B. R., (1996), Computable General Equilibrium Modeling for Policy Analysis and Forecasting. in: Amman H. M., Kendrick D. A., Rust J., (eds.), Handbook of Computational Economics, Vol. 1. North-Holland, NY, 3–85.

Dixon P. B., Parmenter B. R., Sutton J., Vincent D. P., (1982), ORANI: a Multisectoral Model of the Australian Economy, Contributions to Economic Analysis 142, North-Holland, Amsterdam.

Dixon P. B., Rimmer M. T., (2002), Dynamic General Equilibrium Modeling for Forecasting and Policy. A Practical Guide and Documentation of MONASH, North-Holland.

Domingues E. P., Haddad E. A., Hewings G., (2008), Sensitivity Analysis in Applied General Equilibrium Models: An Empirical Assessment for MERCOSUR Free Trade Areas Agreements, The Quarterly Review of Economics and Finance, 48 (2), 287–306.

Elliott J., Franklin M., Foster I., Munson T., Loudermilk M., (2012), Propagation of Data Error and Parametric Sensitivity in Computable General Equilibrium Models, Computational Economics, 39 (3), 219–241.

Gradzewicz M., Griffi n P., Żółkiewski Z., (2006), An Empirical Recursive-Dynamic General Equilibrium Model of Poland’s Economy, World Bank and the National Bank of Poland.

Hagemejer J., Michałek J. J., Michałek T., (2014), Liberalization of Services in Europe: Polish Perspective on Economic Implications of the Services Directive, Journal of Policy Modeling, 36 (2), 211–225.

Hagemejer J., Żółkiewski Z., (2013), Short-Run Impact of the Implementation of EU Climate and Energy Package for Poland: Computable General Equilibrium Model Simulations, Bank i Kredyt, 44 (3), 237–260.

Hagemejer J., Żółkiewski Z., Jędrzejowicz T., (2011), Fiscal Tightening after the Crisis, Bank i Kredyt, 42 (3), 33–59.

Hermeling C., Mennel T., (2008), Sensitivity Analysis in Economic Simulations: A Systematic Approach, ZEW Discussion Papers 08–068, Zentrum für Europische Wirtschaftsforschung / Center for European Economic Research.

Hertel T., Hummels D., Ivanic M., Keeney R., (2007), How Confident Can We Be of CGE-Based Assessments of Free Trade Agreements?, Economic Modelling, 24, 611–635.

Honkatukia J., Vaittinen R., Fudala-Poradzińska I., Janiak M., (2003), Poland’s EU Accession – Results from a Study Utilising the PolGem Model of the Polish Economy, Sixth Annual GTAP Conference, Scheveningen, The Hague, Netherlands, https://www.gtap.agecon.purdue.edu/resources/download/1469.pdf.

Horridge J. M., (2003), ORANI-G: A Generic Single-Country Computable General Equilibrium Model, Technical report, Monash University, Centre of Policy Studies/IMPACT Project.

Kiuila O., Peszko G., (2006), Sectoral and Macroeconomic Impacts of the Large Combustion Plants in Poland: A General Equilibrium Analysis, Energy economics, 28 (3), 288–307.

Narayanan B. G., Hertel T. W., Horridge J. M., (2010), Disaggregated Data and Trade Policy Analysis: The Value of Linking Partial and General Equilibrium Models, Economic Modelling, 27 (3), 755–766.

Roberts B. M., (1994), Calibration Procedure and the Robustness of CGE Models: Simulations with a Model for Poland, Economics of Planning, 27, 189–210.

Saltelli A., Ratto M., Andres T., Cariboni J., Gatelli D., Saisana M., Tarantola S., (2008), Global Sensitivity Analysis. The Primer, John Wiley & Sons, Ltd.

Timmer M. P., O’Mahony M., van Ark B., (2007), EU KLEMS Growth and Productivity Accounts: An Overview, Technical report, University of Groningen & University of Birmingham, www.euklems.net.

World Bank, (2011), Transition to a Low-Emissions Economy in Poland, Poverty Reduction and Economic Management Unit, Europe and Central Asia Region, Washington DC.

Zawalińska K., (2009), Evaluation of Rural Development Programs after Poland’s Accession to the EU: Regional CGE Approach, International Association of Agricultural Economists, conference paper, http://ageconsearch.umn.edu/bitstream/51342/2/IAAE_Zawalinska_445_AgEcon1.pdf.

Zawalińska K., Giesecke J., Horridge M., (2013), The Consequences of Less Favoured Area Support: a Multi-Regional CGE Analysis for Poland, Agricultural and Food Science, 22 (2), 272–287.

Back to top
© 2019–2022 Copyright by Statistics Poland, some rights reserved. Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) Creative Commons — Attribution-ShareAlike 4.0 International — CC BY-SA 4.0