Check out our new publication on forecasting inflation using large datasets and statistical learning techniques.

**Real-time inflation forecasting with high-dimensional models: The case of Brazil**

Check out our new publication on forecasting inflation using large datasets and statistical learning techniques.

International Journal of Forecasting (2017)

Márcio Garcia, Marcelo C. Medeiros, Gabriel F. R. Vasconcelos

The idea of this post is to give an empirical example of how Principal Component Analysis (PCA) can be applied in Finance, especially in the Fixed Income Market. Principal components are very useful to reduce data dimensionality and give a joint interpretation to a group of variables. For example, one could use it to try to extract a common trend from several variables.

The book Advanced R, by Hadley Wickham, shows a very interesting statement:

“To understand R, two slogans are Helpful:

- Everything that exists in an object.
- Everything that happens is a function call.”

– John Chambers

We recently launched the R package ArCo. It is an implementation of the Artificial Counterfactual method proposed by Carvalho, Masini and Medeiros (2016). This post will review some of its features and show how simple it is to estimate “what would have happened” if something “had not happened”. Counterfactuals are useful when different groups for control and treatment are not feasible.

If you are close to the data science world you probably heard about LASSO. It stands for Least Absolute Shrinkage and Selection Operator. The LASSO is a model that uses a penalization on the size of the parameters in the objective function to try to exclude irrelevant variables from the model. It has two very natural uses, the first is variable selection, and the second is forecasting. Since normally the LASSO will select much less variables than Ordinary Least Squares (OLS), its forecast will have much less variance at the cost of a small amount of bias in sample.