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Software and Packages for Empirical Research: Statistic Tests, Econometrics, and Machine Learning

Econ 211 Autumn 2021 Project 3

Published onMay 04, 2022
Software and Packages for Empirical Research: Statistic Tests, Econometrics, and Machine Learning
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About

In this instruction document, we introduce 11 useful tools for Economics: Neural Network Playground, TensorFlow, PyTorch, Eli5, Scipy, Statsmodel, Pingouin, SKlearn (scikit-learn), Keras, FinTA, and Kaggle Kernel. Basic information is provided for every tool, including introduction, license, required citation for this tool. Also, we provide examples for three of the tools: TensorFlow, SKlearn, and Kaggle Kernel. We hope this project would be helpful for those who want to conduct machine learning and statistical analysis in the field of economics.

Resources:

Github Repository: [URL]

Illustrator in Whimsical Folder: [URL]

PubPub Page: [URL]

Tables of Contents

Neural Network Playground

1. Introduction

2. License

3. Required Citation

4. Example

TensorFlow

1. Introduction

2. License

3. Required Citation

4. Example

PyTorch

1. Introduction

2. License

3. Required Citation

Eli5

1. Introduction

2. License

3. Required Citation

SciPy

1. Introduction

2. License

3. Required Citation

Statsmodel

1. Introduction

2. License

3. Required Citation

Pingouin

1. Introduction

2. License

3. Required Citation

SKlearn‌ ‌(scikit‌-learn)

1. Introduction

2. License

3. Required Citation

4. Example

Keras

1. Introduction

2. License

3. Required Citation

FinTA

1. Introduction

2. License

3. Required Citation

Kaggle Kernel

1. Introduction

2. Example

Case Studies [Colab]

Neural Network Playground

1. Introduction

[Neural Network Playground] is a web app written in JavaScript running in your browser. It lets you play with a real neural network and visualize it (Sato, 2016). Check its [Github repo] for more details

2. License

Neural Network Playground is a free and open-Source software, released under the Apache-2.0 License.

3. Required Citation

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man´e, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vi´egas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.

4. Example

Figure 1: Neural Network Playground Example

[Source: Created by whimsical]

TensorFlow

1. Introduction

[TensorFlow] is a free and open-source software library used for machine learning. It can be applied for solving various tasks but has a focus on training deep neural networks.

2. License

TensorFlow is a free and open Source software, released under the terms of the Apache License 2.0.

3. Required Citation

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man´e, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vi´egas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.

4. Example

Figure 2 presents how to classify images of clothing by TensorFlow. The example is provided by the [TensorFlow official website].

Figure 2: TensorFlow Example: Classify images of clothing

[Source: Created by Whimsical]

PyTorch

1. Introduction

[PyTorch] is an open-source machine learning library that is based on the Torch library. It can be used in fields such as computer vision and natural language processing (NLP).

2. License

PyTorch is a free and open Source software, released under the BSD License.

3. Required Citation

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Chintala, S. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32 (pp. 8024–8035). Curran Associates, Inc. 2019. Retrieved from http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

Eli5

1. Introduction

[ELI5] is a Python library which allows users to visualize and debug various Machine Learning models using the unified API. It has built-in support for several ML frameworks and provides a way to explain black-box models (ELI5, 2017).

2. License

Eli5 is a free and open Source software, released under the MIT License.

3. Required Citation

Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, Michael Auli. ELI5: Long Form Question Answering, Proceedings of ACL 2019.

SciPy

1. Introduction

[SciPy] is a free and open-source Python library and math toolkit. It can be used for scientific and technical computing.

2. License

SciPy is a free and open Source software, released under the BSD License.

3. Required Citation

Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17(3), 261-272.

Statsmodel

1. Introduction

[Statsmodels] is a Python package that allows users to browse, estimate statistical models, and perform statistical tests.

2. License

Statsmodel is a Free/Open Source software, released under the open-source Modified BSD (3-clause) license.

3. Required Citation

Seabold, S., & Perktold, J. statsmodels: Econometric and statistical modeling with python. 2010. In 9th Python in Science Conference.

Pingouin

1. Introduction

[Pingouin] is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy (Pingouin, 2021).

2. License

Pingouin is a Free/Open Source software, released under the GNU General Public License v3.0.

3. Required Citation

Vallat, R. (2018). Pingouin: statistics in Python. Journal of Open Source Software, 3(31), 1026, https://doi.org/10.21105/joss.01026

SKlearn‌ ‌(scikit‌-learn)

1. Introduction

[Scikit-learn] is a free software machine learning library for the Python programming language.

Figure 3: Neural Network Playground Example

[Source: Created by whimsical]

2. License

Scikit-learn is open source and free to use under the BSD License.

3. Required Citation

Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.

4. Example

Figure 4 shows how scikit-learn can be used to recognize images of hand-written digits, from 0-9. Check [Colab Notebook: scikit-learn hand-written digits recognition] for more details.

Figure 4: Neural Network Playground Example

[Source: Created by whimsical]

Keras

1. Introduction

[Keras] is an open-source neural network library written in Python that can be run on TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML.

2. License

Scikit-learn is open source and free to use under the MIT License.

3. Required Citation

Chollet, F., & others. (2015). Keras. GitHub. Retrieved from https://github.com/fchollet/keras

FinTA

1. Introduction

[FinTA (Financial Technical Analysis)] is a python package which provides common financial technical indicators implemented in Pandas (FinTA, 2018).

2. License

FinA is open source and free to use under the LGPL-3.0 license.

Kaggle Kernel

1. Introduction

[Kaggle] Kernels are essentially Jupyter notebooks in the browser, which means that users can save themselves the hassle of setting up a local environment and have a Jupyter notebook environment inside the browser (Yufeng, 2017).

2. Example

Figure 5 shows an example of Kaggle Kernel to do data processing and simple machine learning based on the dataset on Kaggle. Check [this example] for more details.

Figure 5: Kaggle Kernel Example

[Source: Created by whimsical]

Case Studies [Colab]

In this section, we provide three case studies for TensorFlow, Scikit‌-Learn, and Kaggle Kernel. [The case of TensorFlow] is provided by the TensorFlow official website, which trains a neural network model to classify images of clothing; [The case of Scikit-Learn] shows how scikit-learn can be used to recognize images of hand-written digits, from 0-9; [The case of Kaggle Kernel] presents how to do data processing and machine learning based on the Kaggle dataset related to Bitcoin. By time series algorithm, we can predict the Bitcoin prices according to the previous ones.

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2
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