Vol 28, No 4 (2024)

This article for the first time addresses the issue of labour flows in the Russian labour market directed to and from the state of informal employment in 2007–2023, allowing to clarify the reaction of informal employment to the deterioration of the macroeconomic context of various types – global financial crisis in 2008–2010, the introduction of foreign sanctions in 2014 and 2022, as well as COVID-19 pandemic in 2020–2021. The results obtained indicate that informal workers do lose their jobs more often than formal workers during economic crises, but informality can also act as an important adjustment mechanism for Russian labour market. Neither the first “sanctions shock” of 2014–2015 nor the COVID-19 pandemic led to an increase in the rate of transition of informal workers to out of labour force state. The rates of transition from unemployment and out of labour force state to informal employment also fell much less in 2020 than corresponding flows from unemployment/out of labour force state to formal employment. At the same time, the second “sanctions shock” of 2022–2023 was accompanied by a reduction in the rates of transition to unemployment regardless of the type of previous employment, an increase in the rates of formal employment after unemployment, and a reduction in the rates of informal employment, which, apparently, reflects increased demand for labour from the corporate sector. The response of labour flows during the world financial crisis, on the contrary, demonstrates a significant reduction in the likelihood of employment in informal jobs. It corresponds with the findings of previous studies on the Russian labour market, which revealed significant drop in the hiring rates of informal workers in 2008–2010 in the non-tradable sectors of the economy.

This paper empirically estimates information asymmetry in cryptocurrency markets using the Probability of Informed Trading (PIN) and Adjusted PIN metrics. These markets, characterized by a high proportion of algorithmic trading and large volumes of high-frequency data, present a promising environment for analyzing informed trading behavior. We introduce a modified estimation procedure for Adjusted PIN, addressing floating-point errors and issues with local extrema, thereby improving its accuracy compared to the traditional naive approaches commonly used in the literature. Additionally, we propose an alternative trade aggregation method at higher frequencies than the conventional daily aggregation to enhance the efficiency of both PIN and Adjusted PIN models. Through analysis of both simulated and real data, we demonstrate that aggregating total buy and sell trades on a daily basis results in less meaningful estimates due to noisy input data, making it difficult to capture informed trader activity. The true optimal trade aggregation frequency is still to be further investigated, as increasing the frequency introduces heterogeneity in order imbalances, and the specific frequencies at which informed traders operate are still unknown. Finally, several empirical studies are conducted to evaluate the behavior of the metrics, revealing that illiquid cryptocurrencies exhibit relatively higher estimated probabilities of informed trading. This finding aligns with similar results observed in equity markets.

This review discusses methods of testing for a panel unit root. Modern approaches to testing in cross-sectionally correlated panels are discussed, preceding the analysis with an analysis of independent panels. In addition, the de-trending methods and corresponding asymptotic results are discussed. To account for cross-sectional correlation, the methods based on de-factorization and bootstrap are considered. In conclusion, links to existing packages that allow implementing some of the described methods are provided.

This review discusses methods for testing for unit roots in panel data. The investigation of several time series together instead of analyzing each one separately and the motivation for testing for panel unit roots are discussed. The review begins with a consideration of the simplest panel unit root tests with independent errors and two types of alternative hypothesis: homogeneous and heterogeneous. For the simplest tests, their asymptotic behavior is described under different types of convergence of the number of objects and the time horizon. Then, the issue of including a deterministic component and changing the asymptotic results are considered, as well as methods for accounting for a weak dependence of errors. The first section concludes with methods based on p-values.

The next section is devoted to the important issue of accounting for cross-sectional correlation in panels and its impact on classical panel unit root tests. Cross-sectional correlation takes place according to some macroeconomic theories, which state that there are some common factors (e.g., technological shocks) that affect not one, but some set of variables. Modifications of classical tests based on factorization are described, when cross-sectional correlation is approximated by (possibly non-stationary) common factors based on the principal component method and based on factor approximation using cross-sectional means. Alternative methods based on resampling are considered. The section ends with a comparative Monte Carlo simulations analysis of various tests described in the review. The problem of imbalanced panel is discussed. In conclusion, references are given to existing packages that allow implementing some of the described methods.