u/Ferran4

Good day, I'll go to the point. In Prague at least there seems to be this stereotype that VŠE is so easy it's basically "a very hard high school", while Charles University is extremely hard. Therefore my question is if these stereotypes have some merit or not:

• VŠE FIS faculty will be extremely easy/low quality?
• CERGE-EI/Charles University is as time consuming and stressful as it seems with tons of students dropping out?

Thank you in advance.

Greetings, I will briefly explain my situation:

I am currently heavily leaning towards getting a PhD (I already took part of a research scholarship -high level regarding the tasks-). Coming from an Spanish Economics undergrad, since the level of the math is... questionable, I thought of going for more of an stat masters before the PhD, and stumbled across this program:

https://preview.redd.it/z30surehe61h1.png?width=951&format=png&auto=webp&s=709a85ffd4324a473504fd3e0d04dd7e02b67d9b

Do you believe it would be inconvenient if I want to follow up with a PhD (probably in Europe as in: being an EU citizen, it is more likely that I end up staying in the continent)? Maybe it is too applied, it looks like it might focus too much on software, and be not so advanced?

AI is, admittedly, another worry, though I would much rather to stay in the public sector realm, which may be a bit less sensitive to it.

Alternatives are some research tracks (example: WU Wien master in economics, CERGE-EI/Charles University master in Economic Research), but these programs are extremely competitive to get in, at least some of them, apparently, stressful to the point of requiring approval to get a part-time job and being many reasons for expulsion -two failings in specific exams-, so I am really rethinking if it is worth to go through that, and, therefore, I would like to know if I really should discard the VSE EDA option or not.

Greetings, I will briefly explain my situation:

I am currently heavily leaning towards getting a PhD (I already took part of a research scholarship -high level regarding the tasks-). Coming from an Spanish Economics undergrad, since the level of the math is... questionable, I thought of going for more of an stat masters before the PhD, and stumbled across this program:

https://preview.redd.it/z30surehe61h1.png?width=951&format=png&auto=webp&s=709a85ffd4324a473504fd3e0d04dd7e02b67d9b

Do you believe it would be inconvenient if I want to follow up with a PhD (probably in Europe as in: being an EU citizen, it is more likely that I end up staying in the continent)? Maybe it is too applied, it looks like it might focus too much on software, and be not so advanced?

Just to put an example, the description of advanced econometrics:

Aims of the course: The course focuses on advanced econometric techniques with topics such as regression models based on time series, panel data models, linear and nonlinear simultaneous equations, models of vector autoregression, or econometric forecasts and policy evaluation. Software packages R / RStudio are used in classroom exercises and case studies.

Learning outcomes and competences: Upon successful completion of this course, students will be able to use single-equation regression models or multiple-equation models of simultaneous equations and vector autoregression in economic analysis, prediction and optimization of economic policies with use of econometric or statistical software (R and RStudio).

Course contents: 1. Introduction to the course, estimation methods (OLS, MM, GMM, MLE), predictions, k-fold cross validation. Variance-Bias tradeoff. 2. Nonlinear regression models (overview), quantile regression. 3. Regression models based on time series, stationarity, spurious regression, unit root tests. 4. Cointegrated time series (TS), testing for cointegration in linear regression models. Error correction model. 5. Testing stability in TS-based regression models (Chow tests), predictions and their evaluation. 6. Finite and infinite distributed lag models. Polynomially distributed lags (Almon type). Koyck transformation, rational distributed lags (RDL), partial adjustment model (PAM), adaptive expectations hypothesis (AEH), rational expectations. 7. Selected panel data methods for short panels (N >> T), assumptions and their tests, robust estimation. Dynamic models for panel data (Arellano-Bond estimator). 8. Selected panel data methods for long panels (T >> N), seemingly unrelated regression equations (SURE). 9. Selected panel data methods for T and N "large"; unit root series in panel data analysis, estimation methods, tests. 10. Simultaneous equations models (SEM), structural and reduced forms, identification of structural equations, estimation methods. 11. SEMs and panel data, non-linear SEM. 12. VAR models, their properties and use in predictions. Impulse-response functions (IRF) and IRF orthogonalization. 13. Advanced methods based on VAR models (SVAR, TVAR, IRF identification – Blanchard-Quah decomposition). Non-stationary time series, cointegration tests. Vector error-correction models (VECM), Johansen's method.

Alternatives are some research tracks in Germany and Austria (as "I may not get accepted options"), and consider also the Verona's "economics and data science" degree, but I would like to know if I really should discard the VSE EDA option or not.