Combining forecasts is considered the easiest way to improve the forecast quality compared to individual models. In this paper, we test the capabilities of the simplest methods of combination, such as simple averages and estimates based on the standard error of previous forecasts, to improve the performance of short-run forecasts of five resource price indicators (oil and metals). The basis of the work is the Gaidar Institute forecasts database, which provides the database of primary forecasts and allows you to calculate their combinations in real time. Based on the obtained results we conclude that even the simplest methods of combination are a way to improve the accuracy of forecasts. In addition, in the case of resource prices, one can even single out a group of methods (namely, combining with weights inversely proportional to the squared errors of individual forecasts) that provide the maximum gain in quality for the most periods.

We examine cross-country differences in the beneficent motive formation of aid to developing countries using data on 18 OECD donors for 2010–2019. The paper tested the theoretical determinants highlighted in the philosophy of collaboration on two subsamples of donors — those with a prevailing selfless motive and those with a mixed one. Our analysis shows that for the first group of countries, welfare and prosociality are significant factors of benefit-oriented aid, for the second — technological development. The components of civic culture, such as tolerance, trust, and public involvement in political decisions probably play a significant role regardless of the donor type . In addition, the paper explored key distinctions in the relationship between domestic and international aid. The results obtained expand the understanding of the cultural and historical contexts of untying aid, which remains one of the central tasks of the OECD.

In this paper we examine the relationship between regulatory bank capital and liquidity creation. We test the “financial fragility‐crowding out hypothesis” and the “risk absorption hypothesis” on the sample of 885 Russian banks over the 2010–2019 period. Inspired by Berger and Bouwmanʼs (2009) approach, we develop a liquidity creation measure that simultaneously considers the category and maturity of secondary accounts. After conducting several robustness checks, we report that bank capital is negatively related to liquidity creation which supports the “financial fragility‐crowding out hypothesis”. We also show that this relationship is independent of the bank size and the economic cycle. Our findings suggest that the Central Bank of Russia faces a trade‐off between financial stability and liquidity creation by the banking system, as tougher regulatory capital requirements decrease banks’ abilities to finance the real sector of the economy.

The research focuses on the investigation of birth cohort effects in post-month consumption of alcohol, cigarettes and marijuana. The authors utilize hierarchical and non-hierarchical multivariate probit models to examine the relationship between the consumption of addictive goods taking into account the endogeneity of subjective health evaluation. Based on the US National Survey on Drug Use and Health from 2002 to 2020 data the study provides evidence of significant age and gender differences in alcohol, cigarettes and marijuana consumption According to the findings, the consumption of each of the substances in question increases the propensity to use other addictive goods, and it differs significantly across birth cohorts and men and women. The correlation between alcohol, cigarettes and marijuana consumption increases from older to younger cohorts. There is also evidence of a closing gender gap in addictive goods consumption, particularly concerning the younger cohorts.

This paper proposes an approach for predicting migration statistics using Google Trends Index (GTI) search query data. We improved the existing methodology in two directions: firstly, we proposed an approach of aggregating key search queries based on various statistical criteria; secondly, we showed the importance of including in the migration model the time lag structure of search queries, depending on migration goals and the associated GTIs. We demonstrate the performance of the proposed approaches on monthly data from the German statistical office on migration volume from Russia to Germany from January 2011 to August 2022. The results show that distributed lag migration models with GTI are better predict migration than SARIMA models. Average lag estimates, i.e. the reaction time of migration statistics to search queries on the topics “embassy”, “work” and “study”, were 5.6, 6.5 and 8 months, respectively. We demonstrate that for forecasting migration from Russia to Germany, it is sufficient to consider only search queries related to the topic “embassy”.

The paper presents extensions of the popular difference‐in‐differences approach (DD) from 2×2 design on multiple time‐period, multiple groups, fuzzy DD, non‐staggered treatment and approaches to measure distributional treatment effect. The paper describes assumptions for consistent estimation of the treatment effect by two‐way fixed effects model (TWFE) and presents the problem leading to inconsistent estimates justifying the application of alternative estimators. The paper briefly introduces methods developing DD for multiple‐period multiple‐group cases based on TWFE and alternative approaches. The proposed techniques allow treatment evaluation in the frame of DD when canonical TWFE leads to inconsistent estimates. Some approaches allow replacement of the well‐known parallel trend assumption (PTA) for a conditional PTA or time randomisation. The paper refers to implementations of these methods in Stata and R. Simulation modelling demonstrates that the stated properties of the alternative estimators are not always reliable.

This paper attempts to construct hedonistic price indices for the computer game market from 2007 to 2023. The peculiarity of the work is that we use hedonistic regressions that consider additional factors reflecting the consumer's reaction to the object of study. In the previous study, we considered only the parameters of the objects set by the manufacturer. Previously, we modelled prices assigned by the manufacturer at the time of game release, and the study period did not cover 2022 and 2023. This paper uses final product prices recorded at the data collection time, and the period captures 2023. Hedonistic regression cleared the time‐dependence of a price from the number of game characteristics and some game demand factors. However, there are unobservable hard‐to‐specify sets of characteristics, such as genre features or game mode. Calculating the outcomes of the described variables directly in the model equation is complex. At the same time, if we assume that they uncorrelation with the variables explicitly specified in the model equation, we can treat them as random effects of various kinds. It allows us to obtain efficient and perhaps even reasonable price index estimates. For building indexes, we use a standard hedonistic approach with temporary dummy variables, modified to allow for the random effects described above. We evaluate the constructed models using OLS and the simulation likelihood method. This approach identifies factors whose outcomes on prices differ significantly depending on which genres, modes and user audience scale groups the objects under consideration fall into. The final part of the study analyzes monthly data from 2020 to 2023 for a deeper examination of the effects following February 24, 2022. The indices based on annual data in the period 2007–2023 reflect key global economic events, which, at first glance, contradicts the opinion of gaming industry experts about the weak sensitivity of the gaming market to crises. However, the analysis conducted on monthly data makes it possible to verify that the gaming industry has a very high rate of adaptation to economic and political upheavals.

This paper explores the effects of non‐interest income (NII) generating activities on banking sector efficiency in 152 countries from 1996 to 2017. Contrary to previous studies that examine the effects of diversification on banking performance at the micro‐level, this study seeks to provide new insights about the effects of diversification at the aggregate level on bank efficiency. This aspect offers a chance to capture the whole banking sector and provides a broader understanding of the effects of banking sector diversification. Our baseline results reveal that engaging in NII activities is positively associated with banking sector efficiency. Using the dynamic threshold regression method, we do not find a tipping point beyond which the benefits of NII activities have an adverse impact on banking sector efficiency. These results are insensitive to different groups of countries. Our findings generally suggest that banking liberalization contributes to the efficiency of the banking sector. In this sense, the findings of this study support banking sector diversification policies implemented in many countries since the 1980s and 1990s.

The research deals with the synthetic control group method application to assess the causal effect of economic sanctions on the Russian economy. The analysis of SCG method assumptions to the problem allowed us to establish that the most problematic issue is comparison group formation. We use the source data from the World Development Indicators database. GDP by PPP per capita was an outcome variable. The application of the method to assessing the impact of sanctions pressure shows that it is possible to select the weights in such a way as to ensure good closeness of the indicators of real and synthetic Russia in the pre-event period. Such states as Kazakhstan, Argentina, Brazil, and the USA were most in demand for the formation of a synthetic Russia. However, the estimates are unstable to changes in the set of countries used to form the synthetic control.

The paper exploits ordered choice logit models with latent classes to account for unobservable consumer heterogeneity in analyzing the preferences for cashless payments in Japan for purchases of different sizes. Using the data of the Survey of Household Finance (2007–2020), we discover that consumers separate into classes of more and less frequent users of cashless payments for each category of purchases. The probability of belonging to the former class is positively related to the fact of consumer taking measures for the protection of their financial assets. The results reveal a statistically different effect of consumer socio‐demographic characteristics (sex, age, income, employment, education, household size), the binary variable for residence in a large city and Kanto region, and the dummies for 2019 and 2020 on the choice of cashless payments in the two latent classes.