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gme

Gravity Modeling Environment by USITC

gptstudio

GPT RStudio addins that enable GPT assisted coding, writing & analysis

grad-io

Graduate Empirical Industrial Organization

gravity

R package that provides estimation methods for Gravity Models

gravity-estimation

Estimates parameters of EK(2002) or Waugh(2010) via STATA and computes bilateral trade flows via simmulation

gravity-model

gravity-model-of-trade

In this jupyter notebook was provided a new approach of building a gravity trade model. Linear models, KNN, Bagging and Boosting were used.

gravity-model-trade-vs-imports

One of the most widely used empirical models to estimate trade flows is the Gravity Model that is based on Newton’s “Law of Universal Gravitation”. This model predicts that the trade flows between two countries will be positively affected by their respective sizes, but negatively by distance. This model has recently come into the spotlight due to t

gravity_model

Data Analysis on world GDP data of 2016 to show the Gravitational Model for the UK.

gravitymodel

Trade models

gravitymodelinr

gravitymodelsoftrade

A play with some web scraping in R and an excuse to explore gravity trade models

gvc

Global Value Chain tools

ibm-hpe-data-modeling-simulation

Analyzing data to model (using the logistic function and its variants) the growth of two industrial organizations, IBM and Hewlett-Packard in MATLAB.

itnr

International Trade Network Analysis in R. This package can clean and transform international trade data from WITS (http://wits.worldbank.org/) into a international trade network and undertake descriptive analysis

link-weight-prediction-wtw

Reproducibility repository for paper with title: The World Trade Web: A Deep Learning Approach to Link Weight Prediction by Miguel Lozano. Abstract: Building on the Gravity Model of Trade, this paper uses a deep neural network with the purpose of link weight prediction in the world trade web (WTW), this is, predicting the magnitude of the trade interactions between countries. The inputs for prediction are characteristics of the interacting countries like trade openness and freedom, as well as their bilateral relationship traits, which include variables like distance between them and booleans such as whether they are in the same continent, share borders, language, ethnicity, trade agreements, legal system, among others. The results of using deep learning are favorable, improving performance obtained by multiple regression by as much as 8%

longrun-macroeconomic-models

Dynamic stochastic models of long run economic growth implemented in MATLAB

lshort-zh-cn

A Chi­nese edi­tion of the Not So Short Introduction to LaTeX2ε

master-school

study

monroe-dsge

The DSGE model with endogenous growth created by Tom Holden and Shifu Jiang as part of the MONROE project.

mooc

嵩天Python语言程序设计

numerical-python-second-ed

Source code for 'Numerical Python Second Edition' by Robert Johansson

oecd-trade-visualization

Data cleaning, manipulation, and visualization for U.S. and China reliance over time on foreign countries for computer electronics and electrical equipment inputs

panel_thresh

Panel_thresh_endogeneous/panel_thresh_endogenous.m extends the MATLAB proccedure IVTAR.M written by Bruce E. Hansen as described in Kremer, Bick and Nautz "Inflation and Growth: New Evidence From a Dynamic Panel Threshold Analysis" (Empirical Economics,44(2), April 2013, 861-878) to estimate a Dynamic Panel Threshold Model.

perlatonettiwaugh.jl

Code and algorithms for "Equilibrium Technology Diffusion, Trade, and Growth"

phdmacro2021

This is the course homepage of Advanced Macroeconomics II of Jinan University in Fall, 2021.

prca

Computations of probabaility revealed comparative advantage (pRCA), defined as probability of holding RCA >1 the following year, gibven current level of RCA, country and product export market size.

pyeconomics

Computational economics in Python

python-course

python-language-design-basics

python 语言程序设计基础（第二版） 嵩天 礼欣 黄天羽 著 书上代码