http://ej.hse.ru en-us Copyright 2024 Fri, 15 Mar 2024 11:45:48 +0300 https://ej.hse.ru/en/2024-28-1/905406001.html Human capital, which includes knowledge, skills and abilities of workers, is one of the key factors of economic growth. However, the quantitative estimates of its contribution to economic growth in Russia are few in number and have various limitations resulting from the ap proaches and data used. This paper presents new estimates of the contribution of human capital to economic growth, reflecting its impact on the productivity of workers. To measure the dynamics of human capital, I use an index that characterizes the education of workers and the health of adults. Inclusion of other human capital measures is limited by the availability and quality of data. To measure the human capital contribution, I use growth accounting. According to the results, in 2004–2017 the average contribution of human capital to the growth of Russian economy was about +0,6 p.p. The highest contribution was in the second half of the 2000s. During this period, a number of favorable factors facilitated the accumulation of human capital. These factors include the preceding increase in personal incomes and the large generations of young and educated workers entering the labor market. However, in the 2010s economic and demographic factors worsened, human capital accumulation slowed, and by 2018–2019 its contribution to economic growth became close to zero. The subsequent decline of population health during the coronavirus pandemic negatively affected the economic growth rates in 2020–2021. https://ej.hse.ru/en/2024-28-1/905412463.html This publication is a continuation of the co-authors’ article on the development of an OLG model with the higher education sector for a representative country where a unified state exam is taken. The key assumptions of the model that distinguish it from a number of others are the heterogeneity of individuals in terms of risk aversion, the discount factor, and the Unified State Exam scores. Based on the proposed model, it is possible to estimate the response of key macroeconomic indicators such as consumption, investment, government expenditure and out put in response to various government policies in the field of higher education. In this part of the work, numerical simulation is carried out based on the specification proposed by the authors in the previous article. The paper analyzes various government policy scenarios aimed at stimulating the accumulation of human capital. Scenarios with the state allocating additional funds for higher education, a different distribution of budget allocations, changes in tax rates and budget structure are considered. We also analyze the response of variables to changes in the variance of individuals' wages. The model is calibrated using Russian statistical data. Using the proposed model, we show the importance of taking microfoundations into account in educational policy analysis. Analysis of the stability of estimates using the example of calibration of distributions of heterogeneous parameters from alternative distributions showed a strong sensitivity of the research results to the choice of parameters of the distribution functions of preferences and abilities of individuals, which indicates the need to correctly take into account heterogeneity in the problems under consideration. Without correctly taking into account the heterogeneity in the problems under consideration, no estimation of scenarios for changes in educational policy can be adequate. https://ej.hse.ru/en/2024-28-1/905436203.html The paper reveals the forecasts for regional inflation based on the regions of the Privolzhskiy Federal District (PFD). The purpose of the study is to determine the model that most accurately predicts regional inflation. The paper compares the tools of machine learning – sup port vector machines, gradient boosting, and random forest – with econometric models of time series, autoregression and integrated autoregression-moving average – models that are often used to predict national inflation in Russia. The results of the study help to identify the key macroeconomic indicators that most significantly affect regional inflation. The top three of them for each region include the value of inflation within last month, the average inflation, and the count number of analyzed month. We show that machine learning models are not as bad at predicting regional inflation over long time periods (21 months and 24 months), while econometric models predict quite accurately on short time horizons. https://ej.hse.ru/en/2024-28-1/905441794.html The risk assessment of options for margining purposes is determined on exchanges using sensitivity coefficients or fixed scenarios of changes in risk parameters. Such methods cannot accurately estimate risk because they do not consider the dependence of option risk on the time to exercise. This dependence should be taken into account in modeling due to the variability of sensitivity coefficients over time to maturity and the time-structure of risk factors. This paper evaluates the effect of time to maturity and time-dependent risk parameters on option risk: implied volatility, implied volatility structure, and volatility risk premium. It was proved that there is a significant trend for the increase of risk assessment as the option approaches the exercise date. Moreover, not only the average risk estimate increases, but also its variance. For options with a strike different from the value of the underlying asset, the trend becomes less explicit, and the accuracy of the estimate decreases with distance from the central strike. But when the strike and the value of the underlying asset are equal, the trend describes the risk dynamics almost completely. It was found that there is a dependence of option risk on the structure of implied volatility: relative volatility bias significantly reduces the level of risk at the central strike, while the distance of bias increases the level of risk. It is important to note that implied volatility, although describing the volatility of option value, does not affect the level of option risk. The volatility risk premium is a relevant factor in describing option risk, but only for the paired regression cases. https://ej.hse.ru/en/2024-28-1/905443882.html The study investigates the predictive efficacy of various machine learningmet-hodologies, encompassing Random Forest (RF) regression, Gradient Boosting (GB), Xtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and a deep learning technique, specifically Long Short-Term Memory (LSTM). The benchmark method employed is the autoregressive (AR) model of order 1. With a focus on forecasting money demand for the Indian economy, a crucial component for achieving the Central Bank of India's inflation targeting objective, a comprehensive monthly dataset from 1997 to 2021 is utilized.The obtained results underline the robust predictive capabilities of the employed models concerning both narrow and broad money demand forecasts. By employing a range of evaluation metrics, the study rigorously compares the predictive performance of these models. Using the expanding window cross validation with time series split, the models are cross-validated to ensure accurate forecastsof monetary aggregates. Moreover, the Diebold – Mariano test is utilized to evaluate and compare the quality of forecasts.In particular, the research finds the superiority of LSTM and LASSO in predictive capabilities for narrow and broad money demand, respectively. These findings collectively contribute to enhancing the understanding of money demand prediction, thus facilitating informed decision-making within the realm of monetary policy. https://ej.hse.ru/en/2024-28-1/905450143.html In recent years, the Vietnamese economy has achieved significant accomplishments, with private investment playing a crucial role in these contributions. As the country entered the 21st century, the private sector's significance in Vietnam's economic development has been increasingly evident through the rising levels of investment, increased employment, greater contributions to the state budget, and overall GDP growth. This article aims to provide robust evidence of the short-term and long-term contributions of private investment to the remarkable economic growth of Vietnam from 2000 to 2022. The study collected data from all 63 provinces and municipalities of Vietnam during the period from 2000 to 2022. Using the collected data, the study employed the results of the Panel Mean Group (PMG) model, selected among three models (PMG, MG, DFE) through Hausman testing. Through the Dynamic Panel Threshold Model, the study accurately identified a maximum threshold ratio of Private investment/GDP at 32.2754%. Surpassing this ratio would lead to a situation of high inflation, an overheated economy, and exceeding production limits. Finally, causal inference from the panel data was utilized to analyze the relationship between private investment and other variables in the model. The study expanded the perspective on private investment's impact on economic growth. In the initial period, private investment activities encountered difficulties leading to inefficient investments and a negative impact on economic growth. However, with flexibility and good adaptability to the market, and efficient utilization of input factors, private investment made positive contributions to economic development over time. Furthermore, through causal inference testing, the study demonstrated a causal relationship between private investment and infrastructure investment, human capital, employment, government expenditure, and trade openness. Finally, the study proposed policy implications for the Vietnamese government to enhance the effectiveness of private investment and further contribute to economic growth.