Аспирантура
2024/2025
Моделирование гетерогенности: квантильные регрессии и анализ с латентными классами
Статус:
Курс по выбору
Направление:
00.00.00. Аспирантура
Кто читает:
Департамент прикладной экономики
Когда читается:
2-й курс, 2 семестр
Формат изучения:
без онлайн-курса
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Бесстремянная Галина Евгеньевна
Язык:
английский
Кредиты:
2
Контактные часы:
38
Course Syllabus
Abstract
The course is targeted at the study of advanced econometric methods for modelling observable and unobservable heterogeneity of various economic agents: individuals, households, firms, countries and regions. The methodology includes quantile regressions and models with a finite number of unobservable classes, which are incorporated in parametric efficiency analysis and policy evaluation techniques. A special attention will be given to applications in the corporate finance, industrial organization and public economics.
One method of studying unobservable heterogeneity is the use of latent class approach in various regression models. Here, the researcher assumes that an observation belongs to one of the unobservable groups, which may be associated, for instance, with unverifiable behavioral characteristics of individuals or with immeasurable managerial practices by firms.
Another method of tackling heterogeneity is the use of quantile regressions, in case of both cross-sectional and panel data models. Quantile regressions are widely used in evaluation of endogenous and exogenous policy reforms, as well as in the analysis of production and cost function.
The analysis is applied to the conditional -th quantile of the dependent variable. Instead of extrapolating the results of the mean regression to the tails of the distribution of the dependent variable, quantile regression enables obtaining independent estimates for the impact of covariates in each conditional quantile of the dependent variable. Different values of the estimated coefficients for the explanatory variable obtained in regressions with different values of are interpreted as the presence of heterogeneous effect of this explanatory variable. For instance, quantile regression may be used for studying heterogeneous effect of policy reforms and macroeconomic shocks on production, or for evaluating heterogeneous effects of socio-demographic characteristics of consumers on their expenditure. Another merit of quantile regression is the applicability for efficiency analysis. High values of quantile index (e.g. 0.8, 0.9) may be taken as an approximation of the production possibility frontier, while in case of conditional quantile regression applied to cost function low values of quantile index (e.g. 0.1, 0.2) may serve an approximation for the best cost minimization trajectory.
Sections will be devoted to the study of econometric packages, available for implementation of the analysis (R, Limdep, Stata) and to replications of the scientific papers/textbook chapters, using the datasets on firms and households.