From 6a94ec30527948d43fc18d35cadfb76fd3d426cc Mon Sep 17 00:00:00 2001 From: Thore Kockerols Date: Sun, 24 Sep 2023 23:08:41 +0200 Subject: [PATCH] Update paper.md --- paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper.md b/paper.md index 951be865..8013be4a 100644 --- a/paper.md +++ b/paper.md @@ -24,7 +24,7 @@ bibliography: paper.bib The goal of this package is to reduce coding time and speed up model development by providing functions for working with discrete-time DSGE models. The user-friendly syntax, automatic variable declaration, and effective steady state solver facilitates fast prototyping of models. The package includes several pre-defined models from prominent economic papers, providing an immediate starting point for users. The target audience for the package includes central bankers, regulators, graduate students, and others working in academia with an interest in DSGE modelling. -The package supports programmatic model definition. Once the model is defined, the package finds the solution for the model dynamics knowing only the model equations and parameter values. The model dynamics can be solved for using first, (pruned) second, and (pruned) third-order perturbation solutions [@villemot2011solving; @levintal2017fifth, @andreasen2018pruning], leveraging symbolic and automatic differentiation. Furthermore, the package can be used to calibrate parameters, match model moments, and estimate the model on data using the Kalman filter [@durbin2012time]. The package is designed to be user-friendly and efficient. Once the functions are compiled and the non-stochastic steady state (NSSS) has been found, the users benefit from fast and convenient functions to generate outputs or change parameters. +The package supports programmatic model definition. Once the model is defined, the package finds the solution for the model dynamics knowing only the model equations and parameter values. The model dynamics can be solved for using first, (pruned) second, and (pruned) third-order perturbation solutions [@villemot2011solving; @levintal2017fifth; @andreasen2018pruning], leveraging symbolic and automatic differentiation. Furthermore, the package can be used to calibrate parameters, match model moments, and estimate the model on data using the Kalman filter [@durbin2012time]. The package is designed to be user-friendly and efficient. Once the functions are compiled and the non-stochastic steady state (NSSS) has been found, the users benefit from fast and convenient functions to generate outputs or change parameters. # Example