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Digital Methods in Economic History: The Case of Computational Text Analysis
In the last two decades, there has been a considerable increase in the supply of digital resources available to economic historians. At the same...
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Empirical and computational approaches to collective choice: introduction to a special issue
This special issue examines empirical and computational approaches to collective choice, the aggregation of individual preferences to form a public...
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Determining Drivers of Private Equity Return with Computational Approaches
Private equity (PE) represents the acquisition of stakes in non-listed companies, often long-term, with the objective of improving the performance...
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The Spherical Parametrisation for Correlation Matrices and its Computational Advantages
In this paper, we analyse the computational advantages of the spherical parametrisation for correlation matrices in the context of Maximum Likelihood...
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Weighted Residuals Methods
This chapter introduces readers to global methods, which, unlike local perturbation methods, draw on information from various points in the state...
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Simulation-Based Methods
This chapter presents methods that combine stochastic simulation with other numerical tools to find approximate solutions on the model’s ergodic set....
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New Paradigm of Computational Sociology
“Data is one of the most important technical elements in the era of intelligent economy. Since the advent of the Internet, improving the efficiency...
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The life care annuity: enhancing product features and refining pricing methods
The state-of-the-art proposes life care annuities, that have been recently designed as variable annuity contracts with Long-Term Care payouts and...
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Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria
We test the performance of deep deterministic policy gradient—a deep reinforcement learning algorithm, able to handle continuous state and action...
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Decomposition Methods
This chapter uses the classic Oaxaca-Blinder (OB) decomposition for the mean as its point of departure and, then, focuses on the last 15 years of...
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Revisiting estimation methods for spatial econometric interaction models
This article develops improved calculation techniques for estimating the spatial econometric interaction model of LeSage and Pace (
2008 ) by maximum...
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Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy
Even though the literature on unregistered economic activity is growing at an increasing rate, we commonly encounter simple ordinary least squares...
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Forecasting Large Collections of Time Series: Feature-Based Methods
In economics and many other forecasting domains, the real world problems are too complex for a single model that assumes a specific data generation...
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Develo** Energy Demand Forecasting Methods
This chapter presents the basics of the load forecasting problem. Issues related to the modeling and its resolution, such as defining the scope that...
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Numerical Solution Methods
We start by considering the stochastic optimal growth model of Chap. 4 , without taxes, explaining the...
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An open-source implementation of geographic profiling methods for serial crime analysis
The rgeoprofile R package was developed to implement functions for the analysis of serial crime incidents. Geographic profiling is an investigative...
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Financial Fragility in Emerging Markets: Examining the Innovative Applications of Machine Learning Design Methods
Emerging economies, while exhibiting higher growth rates compared to developed countries, are susceptible to external shocks, leading to financial...
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Textual Machine Learning: An Application to Computational Economics Research
We demonstrate the benefit to economics of machine learning approaches for textual analysis. Our use case is a machine learning based structuring of...
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Bayesian Econometrics Methods
This chapter provides an introduction to the use of Bayesian methods in labor economics and related disciplines. Since the observed growth in...