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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

This work introduces an R package for Bayesian indicator saturated models, enabling robust detection of outliers and structural breaks using non-local priors. Read more

This paper presents an efficient algorithm to detect maximally influencial in linear regression and proposes consistency criteria for this to hold in double/debiased ML. Read more

This paper develops a novel Bayesian prior distribution that approximates exact moment conditions while maintaining computational tractability. Read more

This paper analyzes macroeconomic risk by incorporating conditional skewness into growth forecasting models. Read more

This paper attempts to estimate counterfactual deforestation rates for evaluating REDD+ forest conservation programs using machine learning methods. Read more

We introduce a maximally flexible Bayesian method for detecting structural breaks in panel data with applications in climate policy. Read more

This paper develops a novel methodology for testing influential subsets in linear regression. Read more

This paper investigates the effects of subsidies in basic research by means of a general equilibrium model. Read more

This paper establishes the distribution of maximally influencial sets in linear models, allowing to test for excessive influence. Read more

This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown! Read more

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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field. Read more

This introductory undergraduate course in econometrics provides students with the fundamental tools for empirical economic analysis. The course covers the classical linear regression model, including simple and multiple regression, interpretation of coefficients, goodness-of-fit measures, and statistical inference. Students learn to estimate and interpret economic relationships, test hypotheses, and understand the assumptions underlying econometric models. Practical applications focus on real-world economic problems using statistical software, enabling students to conduct their own empirical research. The course emphasizes both theoretical foundations and hands-on data analysis skills essential for applied economics. Read more

This undergraduate course examines macroeconomic theory and policy in an international context. Students explore key topics including balance of payments, exchange rate determination, international capital flows, and monetary and fiscal policy in closed and open economies. The course covers foundational models such as the IS-LM model, the Mundell-Fleming framework, purchasing power parity, and interest rate parity conditions. Contemporary issues such as currency crises, international financial integration, global imbalances, and the role of international institutions (IMF, World Bank) are analyzed through both theoretical and empirical lenses. Students develop a theoretical understanding of how economies interact in the global system and how international linkages affect domestic macroeconomic outcomes and policy effectiveness. Read more

This advanced undergraduate econometrics course builds upon foundational knowledge to cover sophisticated econometric techniques for empirical research. Topics include uni- and multivariate time series analysis, panel data methods, instrumental variables estimation, limited dependent variable models, and generalized method of moments. Students explore econometric challenges such as endogeneity, heteroskedasticity, autocorrelation, and model specification issues. The course emphasizes modern econometric approaches used in applied economic research, with particular attention to causal inference and policy evaluation. Students engage with current empirical studies and develop the skills to critically evaluate and conduct advanced econometric analyses using software such as R, Matlab or Python. Read more

This graduate-level bridging course provides essential econometric foundations for incoming master’s students. The course covers fundamental statistical concepts, probability theory, and introductory econometric methods necessary for advanced graduate coursework. Topics include probability theory, asymptotic theory, parameter estimation and standard econometric models such as the linear model, binary choice, multinomial, etc. Read more