2025. vol. 29. No. 1

This paper sheds light on the relationship between economic complexity and income inequality considering the role of institutions based on data over the period 1996–2020 across 52 developed and developing countries from Europe and Cent­ral Asia, and the Middle East and North Africa. Our contribution to the existing li­terature is twofold. First, we analyse the relationship between economic complexity and income inequality considering the institutional dimension and studying various components of institutions. Second, we take into account the non-linear form of relationship between economic complexity and income inequality, as well as het e­rogeneity of this relationship across groups of countries. We address endogeneity by employing a fixed effect two stage least squares model with instrumental variab­les. Our results demonstrate that for the overall sample of countries, an increase in a country’s economic complexity results in higher level of income inequality. However, the impact of economic complexity on income inequality is heterogeneous across groups of countries, with a U-inverted relationship in countries of Euro­pe and Central Asia. Moreover, economic complexity combined with the high level of institutional quality can reduce income inequality. Therefore, we conclude that the improvement of all components of institutional structure will facilitate a decrease in income disparities. Our analysis shows that better educational level leads to lower income inequality. Besides, our findings emphasise the need for policy ensuring more equal gains from economic development and international trade.

The article uses the example of an optimization problem of a household that makes a de­cision on the volumes of consumption and investment to show what difficulties arise in deterministic and stochastic formulations on a finite time interval. In order to make the problem solvableon a finite time interval, a special terminal condition on the agent's equity capital is added, gene­ralizing the standard versions of such conditions.

The article considers two settings. The first setting is a deterministic case, assuming that the household knows the trajectories of all exogenous variables over the entire time interval under consideration. An analytical solution to this problem is found and it is shown that by choosing the parameter of the terminal constraint in the problem on a finite time interval, it is always possible to obtain a consumption trajectory from the solution of a similar problem set for an infinite planning horizon. If the coefficient of the terminal condition is chosen so that the optimal consumption trajectory continues the previous value, then with a certain combination of initial conditions, the household's problem can either be solvable only up to a certain planning horizon, or be completely unsolvable.

The second statement is a stochastic case, when the household knows only the distribution law of exogenous variables. In this case, it is not possible to provide a complete analytical solution, but a sequential algorithm is proposed that allows one to obtain a step-by-step description of the calculation of such a solution. The study of the properties of the constructed model al­lows one to show how different the work with stochastic optimization problems for the analysis of deviations from a certain selected trajectory of states (balanced growth) in response to the implementation of other states (shocks) is from the problem of analyzing specific realized trajectories of the agent's variables.

A considerable amount of work has shown that a carbon tax combined with research subsidies may be regarded as effective policy for encouraging the spread of low-carbon technologies for the benefit of society. This paper exploits the macro­ economic approach of endogenous growth models with technological change in or­der to make a comparative assessment of the impact of such policy measures on economic growth in the US and Japan in the medium and long term. Our estimates with the micro and macro data reveal similarities among Japanese and US energy firms as regards the elasticity of the innovation production function in R&D expenditure and the probability of radical innovation. However, according to energy patent statistics, clean innovation is not as wide-spread in Japan as it is in the US. This may explain our quantitative findings of the need for a stronger reliance on a carbon tax in Japan as opposed to the US.

This article examines the task of multi-step forecasting of realized volatility. The paper introduces a modification of the loss function of the form quantile log-hyperbolic cosine (quantile log-cosh), and information extracted from options using the recovery theorem [Ross, 2015] is also used as exogenous factors in the context of predicting the realized volatility of exchange-Traded funds (ETF) SPY (SPDR S&P 500 ETF Trust) и QQQ (Invesco QQQ Trust). Two hypotheses are put forward: the first one assumes that the quantile log-cosh in neural networks will increase the accuracy of the predictive model on the test dataset compared to the same models trained on other target functions. The second hypothesis is to use information extracted from the recovery theorem. This theorem makes it possible to approximate the true distribution density of SPY and QQQ states in terms of Markov chains and get rid of the assumptions of a risk-neutral measure in financial models. Then, according to the second hypothesis, it is expected that the model with the factors extracted using the recovery theorem will show more accurate predictions on the test sample compared to the classical heterogeneous autoregression (HAR-RV) model. The following machine learning models are used to test hypotheses: LSTM, GRU, BiLSTM, BiGRU, FCNN and N-BEATS. The results show that the modification of the quantile log-cosh makes it possible to improve the accuracy of model predictions on the test dataset. Also, the inclusion of exogenous factors from the recovery theorem in the forecasting models of realized volatility makes it possible to significantly outperform the HAR-RV model, especially over the long-term horizon.

This study investigates changes in the expenditure on out-of-home food and alcohol during the COVID-19 pandemic. Our research specifically explores intergroup variations among households, taking into account the dissimilarity of Russian regions in light of the degree of quarantine restrictions enforced. To test the research hypotheses, we use microdata from the Household Budget Survey conducted by the Federal State Statistics Service. To compare the expenditures on out-of-home food and alcohol in regions with soft, medium, and hard restrictive measu­res, we employ t-test for comparing means and the Kolmogorov – Smirnov test forcomparing distributions. The Tobit model is applied to compare different social groups' household spending habits. The joint analysis of out-of-home food and al cohol expenditure enables the separation of involuntary savings from coping stra­tegies using models for censored data, thereby facilitating an in-depth assessment of household well-being in the face of shocks. Our findings show a reduction in out-of-home food expenditure across all social groups and all levels of quarantine restrictions. The share of alcohol expenditure decreased in almost all social groups in regions with soft measures but significantly increased in those with medium and hard restrictions.

The global financial crisis triggered a debate on the pros and cons of using macroprudential policy as a prudential control tool that includes capital reserves or requirements to address systemic risk, financial credit cycles, and macroeco­nomic stabilization objectives. The macroprudential policy has now been establi­shed as an area of financial policy to stop excessive risk-taking in the financial sec­tor and reduce its consequences to the real economy in response to the lessons learned from the global financial crisis. Controlling credit risk also requires a government-run fiscal sector, one of which is controlling corruption. Corruption sig­nificantly affects credit risk. This study aims to examine the effectiveness of macro­prudential policy instruments and the role of institutional instruments in controlling commercial bank credit risk in the Asia Pacific region from 2012 to 2023. This study uses the generalized method of moments (GMM) as an analytical tool. The results show that loan-to-value and corruption significantly affect credit risk in Asia Pacific.