|languages||English, German, Greek, Latin|
Experience: 0 years
Econometrics is the application of statistical methods to economic data and is described as the branch of economics that aims to give empirical content to economic relations. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference”. Lessons could be in the history of economics, schools of economics, mainstream economics, heterodox economics, economic methodology, economic theory, political economy, microeconomics, macroeconomics, international economics, applied economics, mathematical economics, or econometrics. Lessons can further explore concepts, theory and techniques of economic systems, economic growth, market, national accounting, experimental economics, computational economics, game theory, and operations research. By application lessons can explore agricultural, behavioral, business, cultural, demographic, development, digitalization, environmental, evolutionary, expeditionary, economic geography, financial, health, economic history, industrial organization, information, institiutional, knowledge, labor, law, managerial, monetary, natural resource, organizational, personnel, economic planning, economic policy, public economics, public/social choice, regional, rural, service, socioeconomics, economic sociology, economic statistics, urban, welfare, and welfare economics. Basic lessons have an introductory approach in the probability approach to econometrics, econometric terms and notation, observational data, standard data structures, sources for economic data, econometric software, data files for textbook, reading the manuscript, and common symbols. Lessons can progress to conditional expectation and projection, the algebra of least squares, least squares regression, normal regression and maximum likelihood, large sample asymptotic, asymptotic theory for least squares, restricted estimation, hypothesis testing, multivariate regression, instrumental variables, generalized method of moments, the bootstrap, univariate time series, panel data, non-parametric regression, series estimation, empirical likelihood, regression extensions, limited dependent variables, nonparametric density estimation, and matrix algebra. Students must advise what their tutoring in econometrics is for and what the content ought to be orientated as. I have three years in this kind of tutoring.