In recent years, breakthroughs have been made in the field of artificial intelligence, which has had a major impact on various fields of economy and society. Machine learning, which combines statistics, data science and computer science, is one of the mainstream directions of artificial intelligence, and it is also developing rapidly Integrate into econometric research. On the surface, machine learning usually uses big data, while econometrics usually uses smaller samples, but this distinction is becoming increasingly blurred, and machine learning has become increasingly prominent in the field of economics, especially the intersection of economics and other disciplines. R language is the mainstream computer language used for statistical modeling. It is very convenient for machine learning, and the learning curve is smoother than Python, so it is one of the first choices for machine learning. This time, starting from the actual needs of thesis writing, we will first briefly introduce the basic theories and research methods of economics, so that you can understand the topic selection method and writing framework of the thesis. Then focus on data collection and cleaning, comprehensive modeling evaluation, data analysis and visualization, data spatial effect, causal inference, etc., so that you can master the technology of using R language for economic research at the fastest speed. At the same time, it will also introduce the auxiliary software often used in thesis writing, so as to reduce the difficulty of thesis writing as much as possible.
Topic 1 Theoretical Basis and Software Introduction
1.1 Basic Principles of Economics
Main content: Economic thinking paradigm, resource allocation, efficiency and fairness (in the field of classical economics)
Gregory Mankiw, a popular introduction to the ten principles of economics
1.2 The basic idea of probability and statistics
1.2.1 Common concepts of probability and statistics
1.2.2 Evaluation (single index evaluation and composite index evaluation)
1.2.3
The generation of the concept of causal inference: causal inference is the process of describing the causal relationship according to the conditions of a certain result , the most effective way to infer causality is to conduct a randomized controlled trial, but this approach is time-consuming and expensive, and cannot account for and characterize individual differences; therefore consider causal inference from observational data, such frameworks include potential outcomes frameworks and structures Causal models, the causal inference methods of structural causal models are reviewed below.
1.3 Machine Learning for Evaluation and Causal Inference (Introduction to Algorithms)
1.3.1 KNN and Kmeans
1.3.2 Delphi and AHP
1.3.3 Entropy Weight Method
1.3.4 Random Forest Algorithm
1.3.5 Neural Network
1.4 Common software introduction
Topic 2 Data Acquisition and Arrangement
2.1 Introduction to Data Types
Quantitative data, categorical data, cross-sectional data, time series data, panel data
2.2 Data Acquisition
2.3 Data organization
Topic 3 Commonly used evaluation methods and detailed teaching of related software (case details)
3.1 Calculation of agricultural carbon emissions
3.2 Calculation of carbon emissions from energy consumption
3.3 Comprehensive evaluation methods
3.4 Data analysis and data visualization
3.5 Random forest regression modeling
3.6 Neural network regression modeling
Topic 4 Writing Key Points and Case Explanation
4.1 Overall Writing Points
4.1.1 A good start is half the success (Introduction) The source of the topic selection of the article
4.1.2 The writing method of the literature review
4.1.3 The selection of research methods and the editing of formulas
4.1.4 Data analysis and visualization (analysis)
4.1.5 Two 4.1.6 Writing
of conclusions and abstracts
4.1.7 Mindset building and journal selection and submission
4.2 Case explanation
4.2.1 Introduction to two common types of papers
Introduction to experimental papers Introduction to
model calculation papers
4.2.2 Case
Spatial-temporal characteristics and trend prediction of agricultural carbon emissions in Shanxi Province from 2000 to 2020 Evaluation of
agricultural carbon emissions in Xinjiang and analysis of driving factors based on machine learning algorithms
Research on driving factors and decoupling effects of carbon emissions in Northwest
China Regional differences and distribution dynamics of high-quality agricultural development in China evolution
