Metadata-Version: 2.1
Name: macrodemos
Version: 2021.3.13
Summary: Demos to learn macroeconomics and macro-econometrics concepts
Home-page: http://randall-romero.com/code/macrodemos
Author: Randall Romero-Aguilar
Author-email: randall.romero@outlook.com
License: MIT
Description: # MACRODEMOS

        

        ## Macroeconomics Demos: A Python package to teach macroeconomics and macroeconometrics

        

        The purpose of this package is to provide tools to teach concepts of macroeconomics and macroeconometrics.

        

        To date, the package provides these function:

        

        * [`ARMA_demo`](http://randall-romero.com/arma-demo/): Demo for learning about  ARMA processes. It creates a dash consisting of 7 plots to study the theoretical properties of ARMA(p, q) processes, as well as their estimated counterparts. The plots display

            1. a simulated sample

            2. autocorrelations

            3. partial autocorrelations

            4. impulse response function

            5. spectral density

            6. AR inverse roots

            7. MA inverse roots.

        *  Markov_demo: a demo to illustrate Markov chains. User sets the number of states, the transition matrix, and the initial distribution. The demo creates a dash consisting of 2 plots:

            1. a simulated sample

            2. the time evolution of the distribution of states

        *  Solow_demo: this demo illustrates the Solow-Swan model. Users can simulate the dynamic effect of a shock in a exogenous variable or a change in a model parameter. You will find 6 figures about the Solow-Swan model:

            1. Capital stock, per capita

            2. Output per capita,

            3. Savings per capita,

            4. Consumption per capita,

            5. Change in capital stock, and

            6. Output growth rate 

        *  filters_demo: to illustrate the use of the Hodrick-Prescott and the Baxter-King filters

        

        In a near future, I expect to add a few more demos:

        

        * `Bellman_demo`: to illustrate the solution of a Bellman equation by value function iteration

         

        ### Instructions

        To use the demos, just install this package `pip install macrodemos` and then

        

            from macrodemos import ARMA_demo

            ARMA_demo()

         

        This will open a new tab in your default Internet browser with a Plotly dash. 

         

        ### Disclaimer 

        This program illustrates basic concepts of time series filtering. It was developed for teaching purposes only.  If you have any suggestions, please send me an email at randall.romero@ucr.ac.cr

                                  

        **Copyright 2016-2021 Randall Romero-Aguilar**

        
Keywords: time series,ARMA,filters,Markov chain,Solow-Swan,Hodrick-Prescott,Baxter-King
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
