Applied Economics tutorials: Stata, EViews, Python, Matlab and more!


Are you an economics student? If so, then you will be very interested in checking out my website. It is a comprehensive tutorial hub for those wishing to learn more about applied time series and forecasting. My tutorials provide students with detailed information on how to use Stata, EViews, Python, Matlab-Dynare and Latex software to create economic models and analyze data.

My website also provides useful tips and advice on how best to understand the techniques used in time series analysis, as well as examples of completed projects. Economics students will benefit from my tutorials as they have been written by someone who has completed advanced training in economics and statistics, making it easier for them to learn this complex area of study.

If you are an economics student looking for assistance with applied time series and forecasting, then I highly recommend visiting my website today! You won’t regret it!

EViews Course

Time series and forecasting are essential tools in understanding the future of a given market, industry, or sector. A well-crafted course on applied time series and forecasting can help equip participants with the knowledge needed to make informed decisions when it comes to predicting and planning for tomorrow.

EViews is an advanced platform that offers comprehensive courses on applied time series and forecasting with data analysis capabilities for experienced professionals as well as newcomers. This course covers topics ranging from ARIMA, VAR, SVAR, linear regression, cointegration, stationarity to forecasting using EViews modules. Additionally the course also provides hands-on exercises that allow participants to gain practical experience on topics such as data manipulation, estimation of models output results interpretation etc.

Stata Course

Time series analysis and forecasting have become indispensable tools for data analysts working with financial, economic, and social data. This course in Stata provides students with an understanding of key concepts in applied time series forecasting, as well as the knowledge to apply them through practical examples.

The course is designed to provide a comprehensive overview of the fundamentals of time series data analysis. It covers topics such as ARIMA models, Vector Auto Regression (VAR) models, Structural VAR (SVAR) models, linear regression techniques, cointegration methods and stationarity tests. Furthermore, it provides insight into forecasting using various approaches such as Bayesian inference and Markov chain Monte Carlo (MCMC).

DSGE Models in Dynare

Dynare is an open-source software for economic modelling, allowing users to write and solve dynamic stochastic general equilibrium (DSGE) models. This amazing course offers students a comprehensive introduction to DSGE modelling using the Dynare software in Matlab.

The detailed explanations provided by this course guide students through every step of the modelling process — from setting up the baseline model, solving and simulating it, interpreting results, and more. It covers all aspects of DSGE modelling by making use of helpful examples that are included with the lecture materials. Tutorials are provided on how to set up dynamic optimization problems in Dynare and further lectures explain how to investigate different kinds of policy issues arising from these models.

This course is ideal for anyone interested in learning about DSGE models and their implementation with Dynare.

DSGE Models in Stata

Are you a student looking for an introductory course to DSGE models in Stata? Look no further! This amazing course will introduce you to the fundamentals of DSGE models and provide step by step explanations on how to use them in Stata. Not only is this course detailed and comprehensive, but it is also designed with the student in mind who has no prior experience with DSGE models or the Stata software package.

The instructor of this course understands that learning new concepts can be challenging, which is why they have laid out each lesson so clearly, making sure all material is easy to understand and follow along. Furthermore, the instruction will cover not only building and estimating the model, but also interpreting results using various graphical displays. As a result, students gain confidence not only in their understanding of DSGE models but also when using Stata.

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