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on Computational Economics |
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Issue of 2024‒01‒01
eleven papers chosen by |
| By: | Parth Daxesh Modi; Kamyar Arshi; Pertami J. Kunz; Abdelhak M. Zoubir |
| Abstract: | Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. Deep neural networks, on the other hand, has shown promising results recently; however, we require huge amount of high-quality data to leverage their power. There are some techniques such as augmentation that can help us with increasing the dataset size, but we cannot exploit them on historical bitcoin data. As a result, we propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06280&r=cmp |
| By: | Stempel, Daniel; Zahner, Johannes |
| JEL: | E58 C45 C53 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc23:277627&r=cmp |
| By: | Khaled AlAjmi; Jose Deodoro; Mr. Ashraf Khan; Kei Moriya |
| Abstract: | Using the 2010, 2015, and 2020/2021 datasets of the IMF’s Central Bank Legislation Database (CBLD), we explore artificial intelligence (AI) and machine learning (ML) approaches to analyzing patterns in central bank legislation. Our findings highlight that: (i) a simple Naïve Bayes algorithm can link CBLD search categories with a significant and increasing level of accuracy to specific articles and phrases in articles in laws (i.e., predict search classification); (ii) specific patterns or themes emerge across central bank legislation (most notably, on central bank governance, central bank policy and operations, and central bank stakeholders and transparency); and (iii) other AI/ML approaches yield interesting results, meriting further research. |
| Keywords: | central bank legislation; central banking; artificial intelligence; machine learning; Bayesian algorithm; Boolean algorithm; central bank governance; law and economics |
| Date: | 2023–11–17 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2023/241&r=cmp |
| By: | Holtemöller, Oliver; Kozyrev, Boris |
| JEL: | C22 C45 C53 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc23:277688&r=cmp |
| By: | Ksenia E. Chistyakova (National Research University Higher School of Economics); Tatiana B. Kazakova (National Research University Higher School of Economics) |
| Abstract: | The problem of language models’ interpretation is extensively inspected, but no universal answers have been found. Our study offers to combine widely accepted probing methods with a novel approach to a neural network under investigation. We propose to break grammatical forms on the pre-training step in order to get two "sibling" models, as it casts some light on how different linguistic features are encoded and distributed across the neural language architecture. |
| Keywords: | probing, language models, transformers, BERT. |
| JEL: | Z |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:hig:wpaper:115/lng/2023&r=cmp |
| By: | Chaohua Dong; Jiti Gao; Bin Peng; Yayi Yan |
| Abstract: | In this paper, we consider estimation and inference for both the multi-index parameters and the link function involved in a class of semiparametric multi–index models via deep neural networks (DNNs). We contribute to the design of DNN by i) providing more transparency for practical implementation, ii) defining different types of sparsity, iii) showing the differentiability, iv) pointing out the set of effective parameters, and v) offering a new variant of rectified linear activation function (ReLU), etc. Asymptotic properties for the joint estimates of both the index parameters and the link functions are established, and a feasible procedure for the purpose of inference is also proposed. We conduct extensive numerical studies to examine the finite-sample performance of the estimation methods, and we also evaluate the empirical relevance and applicability of the proposed models and estimation methods to real data. |
| Keywords: | asymptotic theory, multi-index model, ReLU, semiparametric regression |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2023-21&r=cmp |
| By: | Yuxi Heluo; Kexin Wang; Charles W. Robson |
| Abstract: | In this work, we contribute the first visual open-source empirical study on human behaviour during the COVID-19 pandemic, in order to investigate how compliant a general population is to mask-wearing-related public-health policy. Object-detection-based convolutional neural networks, regression analysis and multilayer perceptrons are combined to analyse visual data of the Viennese public during 2020. We find that mask-wearing-related government regulations and public-transport announcements encouraged correct mask-wearing-behaviours during the COVID-19 pandemic. Importantly, changes in announcement and regulation contents led to heterogeneous effects on people's behaviour. Comparing the predictive power of regression analysis and neural networks, we demonstrate that the latter produces more accurate predictions of population reactions during the COVID-19 pandemic. Our use of regression modelling also allows us to unearth possible causal pathways underlying societal behaviour. Since our findings highlight the importance of appropriate communication contents, our results will facilitate more effective non-pharmaceutical interventions to be developed in future. Adding to the literature, we demonstrate that regression modelling and neural networks are not mutually exclusive but instead complement each other. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.13046&r=cmp |
| By: | Jose E. Gomez-Gonzalez (City University of New York-Lehman College (USA). Visiting Professor - Universidad de la Sabana); Jorge M. Uribe (Universitat Oberta de Catalunya, Barcelona (Spain)); Oscar M. Valencia (Fiscal Management Division, Inter-American Development Bank, Washington (USA).) |
| Abstract: | We investigate how a country’s economic complexity influences its sovereign yield spread with respect to the US. We analyze various maturities across 28 countries, consisting of 16 emerging and 12 advanced economies. Notably, a one-unit increase in the economic complexity index is associated to a reduction of about 87 basis points in the 10-year yield spread (p |
| Keywords: | Sovereign Credit Risk, Convenience Yields, Yield Curve, Government Debt, Double-Machine-Learning, XGBoost. JEL classification: F34, G12, G15, H63, O40. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:ira:wpaper:202315&r=cmp |
| By: | Antoine Jacquier; Oleksiy Kondratyev; Gordon Lee; Mugad Oumgari |
| Abstract: | Quantum computing has recently appeared in the headlines of many scientific and popular publications. In the context of quantitative finance, we provide here an overview of its potential. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06621&r=cmp |
| By: | Thomas Chalaux; David Turner |
| Abstract: | This paper describes an algorithm, “DoomBot”, which selects parsimonious models to predict downturns over different quarterly horizons covering the ensuing two years for 20 OECD countries. The models are country- and horizon-specific and are automatically updated as the estimation sample period is extended, so facilitating out-of-sample evaluation of the algorithm. A limited combination of explanatory variables is chosen from a much larger pool of potential variables that include those that have been most useful in predicting downturns in previous OECD work. The most frequently selected variables are financial variables, especially those relating to credit and house prices, but also include equity prices and various measures of interest rates (such as the slope of the yield curve). Business cycle variables -- survey measure of capacity utilisation, industrial production, GDP and unemployment -- are also selected, but more frequently at very short horizons. The variables selected do not just relate to the domestic economy of the country being considered, but also international aggregates, consistent with findings from previous OECD work. The in-sample fit of the models is very good on standard performance metrics, although the out-of-sample performance is less impressive. The models do, however, provide a clear out-of-sample early warning of the Global Financial Crisis (GFC), especially when considered collectively, although they do generate ‘false alarms’ just ahead of the crisis. The models are less good at predicting the euro area crisis out-of-sample, but it is clear from the evolution of the choice of variables that the algorithm learns from this episode, for example through the more frequent selection of a variable measuring euro area sovereign bond spreads. The latest out-of-sample predictions made in mid-2023, suggest the probability of a downturn is at its greatest and most widespread since the GFC, with the largest contributions to such risks coming from house prices, interest rate developments (as measured by the slope of the yield curve and the rapidity of the change in short rates) and oil prices. On the other hand, warning signals from business cycle variables and equity prices, which are often good downturn predictors at short horizons, are conspicuously absent. |
| Keywords: | Downturn, forecast, GDP growth, recession, risk |
| JEL: | E01 E17 E65 E66 E58 |
| Date: | 2023–12–12 |
| URL: | http://d.repec.org/n?u=RePEc:oec:ecoaaa:1780-en&r=cmp |
| By: | Osman, Adam (University of Illinois at Urbana-Champaign); Speer, Jamin D. (University of Memphis) |
| Abstract: | Aversion to "stigma" - disutility associated with a program or activity due to beliefs about how it is perceived - may affect labor market choices and utilization of social programs, but empirical evidence of its importance is scarce. Using two randomized field experiments, we show that stigma can affect consequential labor market decisions. Treatments designed to alleviate stigma concerns about taking entry-level jobs - such as how those jobs are perceived by society - had small average effects on take-up of job assistance programs. However, using compositional analysis and machine learning methods, we document large heterogeneity in the responses to our treatments. Stigma significantly affects the composition of who takes up a program: the treatments were successful in overcoming stigma for older, wealthier, and working respondents. For other people, we show that our treatments merely increased the salience of the stigma without dispelling it. We conclude that social image concerns affect labor market decisions and that messaging surrounding programs can have important effects on program take-up and composition. |
| Keywords: | stigma, experiment, machine learning |
| JEL: | J22 C93 I38 Z13 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16599&r=cmp |