Working Paper: CEPR ID: DP15446
Authors: Fabio Canova; Filippo Ferroni
Abstract: This paper describes a package which uses MATLAB functions and routines to estimate VARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density forecasts, and to trace out the causal effect of shocks using a number of identification schemes. The toolbox is equipped to handle missing observations, mixed frequencies and time series with large cross-section information (e.g. panels of VAR and FAVAR). It also contains a number of routines to extract cyclical information and to date business cycles. We describe the methodology employed and implementation of the functions with a number of practical examples.
Keywords: VARs; local projections; Bayesian inference; identification; forecasts; missing values; filters; cycles; MATLAB
JEL Codes: E52; E32; C10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| GDP (E20) | Prices (D49) |
| GDP (E20) | Interest Rates (E43) |
| Structural Disturbances (D59) | Macroeconomic Aggregates (E10) |