I am a PhD candidate in Economics from University of California Riverside. My research focuses in Econometrics with a particular interest in machine learning and forecasting methods.

I am from Mexico City, where I got my Bachelor in Applied Math and my Master in Economics. There, I also worked as an Economist at the Center for Monetary Latin American Studies (CEMLA) and as a consultant at the Inter-American Develpment Bank (IADB). Then, I moved to California when I started studying my PhD studies in Economics at University of California, Riverside. 

In my PhD studies, I have developed a huge interest for estimation methods in Econometrics. In particular, for time series forecasting while exploring the use of factor models and machine learning techniques.

The estimation methods I have proposed so far can be applied for forecasting the conditional mean, quantile or density of macroeconomic indicators at the Federal Reserve, Central Bank and Finance institutions, but also can be used in other studies such as climate change or health indicators. I am also looking to forecast time series variables while incorporating the presence of structural breaks (either continuous or discrete). In all these applications the presence of many correlated predictors or the construction of optimal aggregated indices becomes relevant.

I also have interest in working with a double debiased framework estimating the treatment effect with time series data which complicates the estimation since usually the information set is not that large.