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Combining Feature Selection and Classification Using LASSO-Based MCO Classifier for Credit Risk Evaluation
Credit risk evaluation is a difficult task to predict default probabilities and deduce risk classification, and many classification methods and...
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DS-HECK: double-lasso estimation of Heckman selection model
We extend the Heckman (1979) sample selection model by allowing for a large number of controls that are selected using lasso under a sparsity...
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What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from the Lasso Regularization and Inferential Techniques
The question of what really drives economic growth in sub-Saharan Africa (SSA) has been debated for many decades now. However, there is still a lack...
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Indicator Selection of Index Construction by Adaptive Lasso with a Generic \(\varepsilon \)-Insensitive Loss
A successful index helps policy decision makers identify benchmark performances and trends and set policy priorities. Selecting representative...
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An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership
Ridership prediction at station level plays a critical role in subway transportation planning. Among various existing ridership prediction methods,...
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When do more police induce more crime?
We provide a necessary and sufficient condition on the equilibrium of a Walrasian economy for an increase in police expenditure to induce an increase...
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Macroeconomic attention and commodity market volatility
In this paper, we empirically examine the relationship between the novel macroeconomic attention indices (MAI) and commodity market volatility....
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Public Communication as a Mechanism for Collusion in the Broiler Industry
In this paper, we investigate whether the U.S. broiler companies used public information sharing mechanisms to collude to reduce output and fix...
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Institutions and carbon emissions: an investigation employing STIRPAT and machine learning methods
We employ an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model combined with the environmental...
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Forecasting House Prices: The Role of Fundamentals, Credit Conditions, and Supply Indicators
This paper evaluates the ability of various indicators related to macroeconomic fundamentals, credit conditions, and housing supply to predict house...
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Lights and GDP relationship: What does the computer tell us?
The relationship between nighttime lights and GDP varies from country to country. However, which factors drive variations in the lights–GDP...
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Huber Loss Meets Spatial Autoregressive Model: A Robust Variable Selection Method with Prior Information
In recent times, the significance of variable selection has amplified because of the advent of high-dimensional data. The regularization method is a...
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Impact of Health Indicators on Men and Women’s Wages in Russia
AbstractUsing data from the Russian Longitudinal Monitoring Survey, the article evaluates the impact of self-assessed health and morbidity on the...
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Understanding EU regional macroeconomic tip** points using panel threshold technique
The primary objective of the European Union has been to bring cohesion among its member states and their respective regions. This has led the...
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Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach
Because the U.S. is a major player in the international oil market, it is interesting to study whether aggregate and state-level economic conditions...
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FDI and onshore task composition: evidence from German firms with affiliates in the Czech Republic
How does a firm’s foreign direct investment (FDI) in a low-wage country change its onshore task demand in a high-wage country? Is the shift more...
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Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach
In this study, we investigate and apply the models from the machine learning (ML) paradigm to forecast the inflation rate. The models identified are...
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Indirect estimation of the monthly transport turnover indicator in Italy
The paper discusses the results of a selection of a set of monthly indicators to be used as predictors of the quarterly index of Italian service...
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Using Machine Learning to Understand Bargaining Experiments
We study dynamic unstructured bargaining with deadlines and one-sided private information about the amount available to share (the “pie size”)....
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Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth
We run a ‘horse race’ among popular forecasting methods, including machine learning (ML) and deep learning (DL) methods, that are employed to...
