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It contains some useful resources in economics.
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The different fields can be found here
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The collections for codes and coding skills can be found here
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The collections for data and some replication codes can be found here
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Causal Inference - Author: Scott.
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The Effect: An Introduction to Research Design and Causality: Containing R, Stata, and Python codes.
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Applied Empirical Methods https://github.com/paulgp/applied-methods-phd
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Introduction to Computational Finance and Financial Econometrics with R
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Statistical Tools for Causal Inference other useful resource given by writter is here
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Econometrics (Master in Econ, 1st year): Jean-Marc Robin(Science Po)
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Probability and Statistics (Master in Econ, 1st year): Jean-Marc Robin(Science Po)
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Statistical Inference via Data Science https://moderndive.com/
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Doing Bayesian Data Analysis https://bookdown.org/content/3686/
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STAT545:Data wrangling, exploration, and analysis with R
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https://www.statlearning.com/ The classic book: An Introduction to Statistical Learning.
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Statistics Inference: Data Analyst Handbook
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Statistical Inference via Data Science https://moderndive.com/ using R and tidyverse to do statistical inference.
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Bayesian Stats: using Julia to apply Bayesian Statistics.
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Using Spatial Data with R: a quick introduction to spatial data with R.
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Spatial Data Science: Getting a deeper learning in Spatial Data Science.
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Computational and Inferential Thinking it is full of statistic methods to do data science.
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https://socviz.co/index.html#preface - Data Visualization
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https://tellingstorieswithdata.com/ Telling stories with data.
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Data Science for Economists and Other Animals introducing the core R usuage in Econ.
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Computational Economics for PhDs: using Julia in the field of economics is getting more and more popular. written by Florian Oswald.
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Introduction to Python for Econometrics, Statistics and Numerical Analysis
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Introduction to Computational Finance and Financial Econometrics with R - Author: Eric Zivot
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Economic Networks THEORY AND COMPUTATION - Authors: John Stachurski and Thomas J.Sargent.
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Openair A Guide to the Analysis of Air Pollution Data.
- https://www.parisschoolofeconomics.eu/en/news/from-may-24-to-june-4-watch-abhijit-banerjee-and-esther-duflo-s-online-course/#partie1: Instructors: Abhijit Banerjee and Esther Duflo
- Search and Matching in Macro and Finance Virtual Seminar Series https://sammf.com/
- Online Spatial and Urban Seminar https://osus.info/
- Advanced Econometric, Introduction to Nonparametric Analysis in Time Series Econometrics.
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Lectures in Recursive Economic Dynamics - author: Peter Galbács
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Advanced Macroeconomics I - Instructor: Professor Gertler
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Advanced Macroeconomics: Models with Heterogeneous Agents - Instructors: José Víctor Ríos Rull and Wei Cui
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Advanced macroeconomic analysis: JENNIFER LA'O(Columbia Business School)
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https://web.sas.upenn.edu/schorf/classes/ VAR estimation
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Forecasting: Principles and Practice: Classic book in time-series analysis.
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Graduate Macro Theory II - Instructor: Eric Sims(University of Notre Dame)
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Econometrics: First year graduate level econometrics notes with embedded examples using the Julia language.
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Advanced Topics in Trade - Instructor: Heiwai Tang.
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Quantitative Dynamic Model - Instructor: Daniel Xu.
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Empirical Methods for Industrial Organization - Instructor: Matthew Shum(Caltech).
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Organizational Economics II - Instructor: Daniel Barron(Northwestern).
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https://gregmankiw.blogspot.com/search?q=advice author: Greg Mankiw.
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https://blogs.ubc.ca/khead/research/research-advice: collected research advice by Keith Head.
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https://sites.google.com/site/mounirkaradja/resources: Tips and resources for research.
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https://sites.google.com/view/kleintob/ph-d-students: Academic writing, communicating and many other things.
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A practical guide of the first years (for outsiders) from insiders
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Impactful Research https://truan.github.io/resources/
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Productivity and work habit https://www.patrickbaylis.com/posts/2022-02-09-productivity/2022-02-09-productivity?continueFlag=5b129f8aac804b43a5524c23d36869e8
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Resources on Computation - author: Gabriel Mihalache