Central European Journal of Economic Modelling and Econometrics

The United Nations General Assembly established the Sustainable Development Goals in 2015 to achieve an equitable and sustainable future for all by 2030. This study aims to model the relationship between government revenue per capita, quality of governance and the targets of several of these goals, including the coverage of the critical determinants of health; water, sanitation, healthcare, and education. We used government revenue because the policies and practices of international and multinational organisations - including corporations and banks - are more likely to influence revenue rather than government spending in countries in which they are engaged. Also, government revenue reflects a government's ability to spend across all sectors rather than just health or education. An unbalanced non-linear panel data model was employed, and annual data on 217 countries over the period 1960-2000 was used. The coverage of the Sustainable Development Goal variables was expressed as percentages and measures of the quality of governance included in the model. A linear relationship between revenue and the determinants of health would not be appropriate; therefore, we employ a logistic function. A standard panel logistic function would impose the same shape “S” curve on all countries, which is inappropriate. Therefore, we augment the parameters of the logistic function with measures of the quality of governance in each country, which allows each country to have a different “S” shape as the quality of its governance varies. Our study found that increased government revenue is associated with increased progress towards the Sustainable Development Goals. An improvement in the quality of governance could amplify this effect. This modelling and its accompanying visualisations can predict the potential of an increase in government revenue in an individual country regarding progress towards the Sustainable Development Goals.

This paper investigates the linkages between economic growth and fiscal policy under perfect capital mobility. The model incorporates wide range of fiscal policy instruments: the budget deficit, the structure of public debt, public expenditures on education, public consumption, and four tax rates. We prove that two tax rates - on consumption and interest on government bonds held by domestic lenders - are neutral for economic growth: both for the balanced growth path (BGP), and for transitory dynamics. All other parameters of fiscal policy are not neutral. Theoretical results are illustrated with an empirical analysis for Poland based on post-global financial crisis data for the Polish economy (2009-2018). Numerical simulations show that if fiscal policy remains unchanged, Polish economy will converge to the BGP with GDP growing at 2.3%. The best way to accelerate growth is to increase public investment in education. The other budgetary policy instruments are less effective in shaping economic growth.

We consider a monetary DSGE model featuring a borrowing constraint such that the amount of debt cannot be larger than a fraction - the debt-to-income (DTI) limit - of borrowers' labor income and the DTI limit is endogenous. The coexistence of financial amplification mechanisms warranted by this model provides a role for a specific macroprudential tool: a countercyclical DTI limit. Conditional on the pre-crisis sample and in a more recent out-of-sample period, our ex-post normative analysis shows that when this policy is implemented the cooperation between central bank and macroprudential authority in pursuing the “two instruments for two goals” strategy delivers an efficient performance in terms of macroeconomic stabilization, significantly outperforming the central bank's policy of “leaning against the wind”. This implies that a central bank should only be focused on its standard objectives (inflation and output stabilization) while financial stability be monitored by a macroprudential authority.

The purpose of this paper is to compare two approaches applied in property valuation: artificial neural networks and spatial regression. Despite the fact that artificial neural networks are often the first choice for modeling in the big data era, spatial econometrics methods offer incorporation of information on dependences between multiple objects in the studied space. Although this dependency structure can be incorporated into artificial neural network via feature engineering, this study is focused on abilities of reproducing it with machine learning method from crude coordinate data. The research is based on the database of 18,166 property sale transactions in Warsaw, Poland. According to this study, such volume of data does not allow artificial neural networks to compete in reflecting spatial dependence structure with spatial regression models.