I studied Economic Analysis at the University of Pablo de Olavide. I have a master's in quantitative economics from the University of Alicante, and now I am doing a PhD in economics at the University of Alicante. Apart from my teaching assistant position at the University of Alicante, I have also worked as PhD trainee in the Bussiness and Cycle Analysis department of the European Central Bank for one year. I describe my skills in the following list:

• Computer skills: Python, R, Stata, Matlab, Excel, Gretl, SQL, NoSQL (JSON, MongoDB,
Neo4J), ETL programs (Pentaho, Knime), Mathematika, SPSS, Eviews, Lingo, Lindo. Coding
for more than 5 years.

• Mathematics and statistics applied to economic analysis. Micro, macro and financial
econometrics. Forecasting, linear and nonlinear programming, seasonal adjustment,
mathematical modelling, probability and risk modelling, partial effect estimations, policy
evaluation, portfolio optimization, simulation exercises, identifying shocks and impulse
response analysis.

• Methodologies: Monte Carlo Simulations, ARIMAX, VAR, SVAR, VECM, Local Projections,
GARCH, Copulas, Mean-Variance Portfolio selection, Dynamic Factor Models (DFM),
Principal Component Analysis (PCA), OLS, GLS, IV estimations, Diff and Diff, Multinomial
Logit and probit, TBATS, STL, TRAMO-SEATS, X-11, X-13, Lasso, Support vector machine
(SVM), Neural Networks, Random forest, Decision Trees, mathematical optimization of
multivariable functions (Lagrange, Kuhn–Tucker, Simplex algorithm).

• Every step of the process: Define research question or identify business problem, collect/
query and process/clean data, analysis and visualization, modelling (build and validate
different models, evaluation to choose the best performing models), write and
communicate the results.

• Communication skills: Experience giving presentations and teaching.

• Researching skills: Ability to understand the scientific literature, apply the latest techniques
and write research papers.

• Experience working with every kind of data: Cross-sectional, time series, panel data,
textual data.

• Experience working with high frequency data.

• Experience working with data from the newspapers (Factiva) and social media (snscrape
python library).

• Experience performing sentiment analysis over textual data. Classify text as positive
or negative. I can keep track of people opinion over time about any topic, state, firm,
etc. Machine learning and rule-based models.

• Writing advanced reports and making professional presentations using latex.

• Driving license.