Welcome, this 2023-I semester I will be teaching Macroeconometrics in the UPC (Peruvian University of Applied Sciences). Macroeconometrics is a field that combines macroeconomics and econometrics. It uses methodologies, models, and techniques to analyze forces shaping national economies. Macroeconometric models are sets of equations designed to explain the economy or some part of it. These equations can be either stochastic (behavioral) or identities. Stochastic equations are estimated from historical data while identities hold by definition.

Some examples of macroeconometric models include time series models such as ARMA (Autoregressive Moving Average), GARCH (Generalized Autoregressive Conditional Heteroskedasticity), and VAR (Vector Autoregression) models. These models can be used to analyze and forecast economic variables:

• ARMA (Autoregressive Moving Average) models can be used to analyze and forecast time series data that is stationary. These models are useful when there is autocorrelation within the data.

• GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models can be used to model time series data with changing variances. These models are often used in finance to model volatility.

• VAR (Vector Autoregression) models can be used to analyze multiple time series data sets that influence each other. These models are often used in macroeconomics and finance for forecasting.

In this lecture series we are covering all of these models in detail, with applications for peruvian economy. We are using Eviews in class, but for the offline class I will be sharing Google Collab notebooks to put in practice our Python skills.

I hope you enjoy this course, se you in the next few weeks 😊🚀.