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An Enterprise Multi-agent Model with Game Q-Learning Based on a Single Decision Factor
In recent years, the study of enterprise survival development and cooperation in the whole economic market has been rapidly developed. However, in...
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Random Effects Models
This chapter deals with the most relevant multi-dimensional random effects panel data models, where, unlike the case of fixed effects, the number of...
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OLG Models with Uncertainty
This chapter introduces both idiosyncratic and aggregate uncertainty into overlap** generations (OLG) models. The methods used for the computation...
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Models with Endogenous Regressors
This chapter examines various estimation and testing issues concerning models with endogenous regressors. The complexity of these issues increases as...
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Dynamic Models and Reciprocity
This chapter discusses the specification, estimation and testing of dynamic models with multi-dimensional data. The difficulties in estimating...
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Multi-Dimensional Models for Spatial Panels
Spatial econometric models are widely used to formalize interactions in geographical space or along networks. When applied to panel data, the...
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An Empirical Analysis of Asset Pricing Models
This paper presents a comparative analysis of three asset pricing models, namely, the Capital Asset Pricing Model (CAPM), the Fama-French Three...
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Random Coefficients Models
This chapter deals with specification, estimation, and inference within the framework of a random coefficients model for multi-dimensional panel...
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Comparative Analysis of Digital Business Models
This paper discusses the comparative analysis of different attributes of Google and Facebook business model and their novel features for handling...
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Evaluating Educational Performance of OECD Countries with Common-Weight DEA-Based Models
To establish more efficient and equitable education systems for students, increasing importance has been attached to the efficient usage of...
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Hybridization of ARIMA with Learning Models for Forecasting of Stock Market Time Series
This paper aims to highlight in a relevant way the interest of hybrid models (coupling of ARIMA processes and machine learning models) for economic...
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Transition to Circular Business Models
This chapter discusses the role of business models as a catalyst for circular transformation. It starts with the conceptualization of circular...
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Computing Longitudinal Moments for Heterogeneous Agent Models
Computing population moments for heterogeneous agent models is a necessary step for their estimation and evaluation. Computation based on Monte Carlo...
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Overlap** Generations Models with Perfect Foresight
This chapter studies overlap** generations (OLG) models as pioneered by Auerbach and Kotlikoff. Here, agents differ not only with regard to their...
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Statistical Evaluation of Deep Learning Models for Stock Return Forecasting
Artificial intelligence applications, including algorithmic training, portfolio allocation, and stock return forecasting in the financial industry,...
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Navigating the Digital Odyssey: AI-Driven Business Models in Industry 4.0
In the era of Industry 4.0, characterized by the convergence of digital technologies and physical systems, the transformation of business models is...
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Multi-Factor RFG-LSTM Algorithm for Stock Sequence Predicting
As has been demonstrated, the long short-term memory (LSTM) algorithm has the special ability to process sequenced data; however, LSTM suffers from...
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Flow count data-driven static traffic assignment models through network modularity partitioning
Accurate static traffic assignment models are important tools for the assessment of strategic transportation policies. In this article we present a...
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Short-run Johansen frontier-based industry models: methodological refinements and empirical illustration on fisheries
This contribution focuses on extending the current state of the art in a central resource allocation planning model known under the name of the...
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Machine Learning Method for Return Direction Forecast of Exchange Traded Funds (ETFs) Using Classification and Regression Models
This article aims to propose and apply a machine learning method to analyze the direction of returns from exchange traded funds using the historical...
