|
on Computational Economics |
|
Issue of 2022‒10‒10
fourteen papers chosen by |
| By: | du Plessis, Emile; Fritsche, Ulrich |
| Abstract: | A reflection on the lackluster growth over the decade since the Global Financial Crisis has renewed interest in preventative measures for a long-standing problem. Advances in machine learning algorithms during this period present promising forecasting solutions. In this context, the paper develops new forecasting methods for an old problem by employing 13 machine learning algorithms to study 147 year of systemic financial crises across 17 countries. It entails 12 leading indicators comprising real, banking and external sectors. Four modelling dimensions encompassing a contemporaneous pooled format through an expanding window, transformations with a lag structure and 20-year rolling window as well as individual format are implemented to assess performance through recursive out-of-sample forecasts. Findings suggest fixed capital formation is the most important variable. GDP per capita and consumer inflation have increased in prominence whereas debt-to-GDP, stock market and consumption were dominant at the turn of the 20th century. Through a lag structure, banking sector predictors on average describe 28 percent of the variation in crisis prevalence, real sector 64 percent and external sector 8 percent. A lag structure and rolling window both improve on optimised contemporaneous and individual country formats. Nearly half of all algorithms reach peak performance through a lag structure. As measured through AUC, F1 and Brier scores, top performing machine learning methods consistently produce high accuracy rates, with both random forests and gradient boosting in front with 77 percent correct forecasts. Top models contribute added value above 20 percentage points in most instances and deals with a high degree of complexity across several countries. |
| Keywords: | machine learning,systemic financial crises,leading indicators,forecasting,early warning signal |
| JEL: | C14 C15 C32 C35 C53 E37 E44 G21 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:uhhwps:67&r= |
| By: | Yiyan Huang; Cheuk Hang Leung; Xing Yan; Qi Wu; Shumin Ma; Zhiri Yuan; Dongdong Wang; Zhixiang Huang |
| Abstract: | Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate ATE in the observational study. However, the DML estimators can suffer an error-compounding issue and even give an extreme estimate when the propensity scores are misspecified or very close to 0 or 1. Previous studies have overcome this issue through some empirical tricks such as propensity score trimming, yet none of the existing literature solves this problem from a theoretical standpoint. In this paper, we propose a Robust Causal Learning (RCL) method to offset the deficiencies of the DML estimators. Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue. Empirically, the comprehensive experiments show that i) the RCL estimators give more stable estimations of the causal parameters than the DML estimators, and ii) the RCL estimators outperform the traditional estimators and their variants when applying different machine learning models on both simulation and benchmark datasets. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.01805&r= |
| By: | Berend Gort; Xiao-Yang Liu; Xinghang Sun; Jiechao Gao; Shuaiyu Chen; Christina Dan Wang |
| Abstract: | Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and optimistically reported increased profits in backtesting, which may suffer from the false positive issue due to overfitting. In this paper, we propose a practical approach to address backtest overfitting for cryptocurrency trading using deep reinforcement learning. First, we formulate the detection of backtest overfitting as a hypothesis test. Then, we train the DRL agents, estimate the probability of overfitting, and reject the overfitted agents, increasing the chance of good trading performance. Finally, on 10 cryptocurrencies over a testing period from 05/01/2022 to 06/27/2022 (during which the crypto market crashed two times), we show that the less overfitted deep reinforcement learning agents have a higher Sharpe ratio than that of more over-fitted agents, an equal weight strategy, and the S&P DBM Index (market benchmark), offering confidence in possible deployment to a real market. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.05559&r= |
| By: | Michelle Alexopoulos; Xinfen Han; Oleksiy Kryvtsov; Xu Zhang |
| Abstract: | We study soft information contained in congressional testimonies by the Federal Reserve Chairs and analyze its effects on financial markets. Using machine learning, we construct high-frequency measures of Fed Chairs' and Congress members' emotions expressed via their words, voice and face. Increases in the Chair's text-, voice-, or face-emotion indices during the testimony generally raise the S&P500 index and lower the VIX. Stock prices are particularly sensitive to both the members' questions and the Fed Chair's answers about issues directly related to monetary policy. These effects add up and propagate after the testimony, reaching magnitudes comparable to those after a policy rate cut. Our findings resonate with the view in psychology that communication is much more than words and underscore the need for a holistic approach to central bank communication. |
| Keywords: | Central bank communications, Financial markets, High-frequency identification, Facial emotion recognition, Vocal signal processing, Textual Analysis |
| JEL: | E52 E58 E71 |
| Date: | 2022–09–21 |
| URL: | http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-737&r= |
| By: | Jessica Birkholz |
| Abstract: | This study explores the regional innovation system characteristics that build the basis for the regional absorptive capacity of entrepreneurial knowledge. Regionalized patent data is combined with firm level and regional information for German regions over the period 1995 until 2015. Network analysis is applied to identify regional innovation system characteristics on three different layers: 1) cooperation between incumbent firms, 2) learning regimes, and 3) the technological knowledge base. Random forest analyses on basis of conditional inference classification trees are used to identify the most important characteristics for the regional absorption of entrepreneurial knowledge in general and on different efficiency levels. It is shown that characteristics on all three layers impact the regional absorption of entrepreneurial knowledge. Further, the direction and magnitude of the effect regional innovation system characteristics have on the regional knowledge absorption vary across different levels of absorption rates. It is concluded that for a successful implementation of policies to increase the impact of entrepreneurial knowledge on regional development, the regional innovation system needs to be monitored and adapted continuously. |
| Keywords: | Entrepreneurship, Regional absorptive capacity, Smart specialization |
| JEL: | L26 O33 D85 |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:atv:wpaper:2205&r= |
| By: | Lena Sasal; Tanujit Chakraborty; Abdenour Hadid |
| Abstract: | Deep learning utilizing transformers has recently achieved a lot of success in many vital areas such as natural language processing, computer vision, anomaly detection, and recommendation systems, among many others. Among several merits of transformers, the ability to capture long-range temporal dependencies and interactions is desirable for time series forecasting, leading to its progress in various time series applications. In this paper, we build a transformer model for non-stationary time series. The problem is challenging yet crucially important. We present a novel framework for univariate time series representation learning based on the wavelet-based transformer encoder architecture and call it W-Transformer. The proposed W-Transformers utilize a maximal overlap discrete wavelet transformation (MODWT) to the time series data and build local transformers on the decomposed datasets to vividly capture the nonstationarity and long-range nonlinear dependencies in the time series. Evaluating our framework on several publicly available benchmark time series datasets from various domains and with diverse characteristics, we demonstrate that it performs, on average, significantly better than the baseline forecasters for short-term and long-term forecasting, even for datasets that consist of only a few hundred training samples. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.03945&r= |
| By: | Shrub, Yuliya; Rieger, Jonas; Müller, Henrik; Jentsch, Carsten |
| Abstract: | The prompt availability of information on the current state of the economy in real-time is required for prediction purposes and crucial for timely policy adjustment and economic decision-making. While important macroeconomic indicators are reported only quarterly and also published with substantial delay, other related data are available more frequently, that is monthly, weekly, daily or even more often. In this regard, the goal of nowcasting methods is to make use of such more frequently collected variables to update predictions of less often reported variables such as e.g. GDP growth. In this paper, we propose a mixed-frequency model to investigate the potential of using text data in form of newspaper articles for nowcasting German GDP growth. Newspaper text data appears to be very helpful in this regard as it directly explains economic and social progress influencing GDP growth and as it is updated frequently without any substantial delay. We compare several setups based on commonly used macro variables with and without additionally included information from text data (extracted in an unsupervised manner) as well as a setup only based on such text data. To deal with the high dimensionality of the considered data, we make use of principal component regression, penalization techniques and random forest. Comparing our results leads to the conclusion that there are certain benefits achievable when text data are included for nowcasting, but the unsupervised extraction of information from text data tends to still contain too much irrelevant noise hampering the performance of the resulting nowcasting approach. |
| Keywords: | Topic model,latent Dirichlet allocation,text mining,econometrics,gross domestic product,prediction,forecast |
| JEL: | C52 C53 C55 E37 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:964&r= |
| By: | Ella Marley-Zagar (MRC Clinical Trials Unit at UCL, London, UK); Ian R. White (MRC Clinical Trials Unit at UCL, London, UK); Tim P. Morris (MRC Clinical Trials Unit at UCL, London, UK) |
| Abstract: | Simulation studies are used in a variety of disciplines to evaluate the properties of statistical methods. Simulation studies involve creating data by random sampling, typically from known probability distributions, with the aim of assessing the robustness and accuracy of new statistical techniques by comparing them to some known truth. We introduce the siman suite for the analysis of simulation results, a set of Stata programs that offer data manipulation, analysis and graphics to process, explore and visualise the results of simulation studies. siman expects a sensibly structured dataset of simulation study estimates, with input variables being in ‘long’ or ‘wide’ format, string or 1 numeric. The estimates data can be reshaped by siman reshape to enable data exploration. The key commands include siman analyse to estimate and tabulate performance; graphs to explore the estimates data (siman scatter, siman swarm, siman zipplot, siman blandaltman, siman comparemethodsscatter); and a variety of graphs to visualise the performance measures (siman nestloop, siman lollyplot, siman trellis) in the form of scatter plots, swarm plots, zip plots, Bland–Altman plots, nested-loop plots, lollyplots and trellis graphs (see Morris et al., 2019). |
| Date: | 2022–09–10 |
| URL: | http://d.repec.org/n?u=RePEc:boc:lsug22:02&r= |
| By: | Linda Zhang (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique); Gang D.U.; Jun W.U.; Yujie M.A. |
| Abstract: | In practice, it is of paramount importance that firms make joint decisions in production planning, pricing and retailer selection while considering emission regulation. This is because the joint decisions can ensure firms to obtain higher profits while contributing to sustainable environments. However, due to the problem complexity, no models facilitating such decision making are available. This study aims to develop a model to help firms make optimal joint decisions. To model the situations where a manufacturer is the leader and the retailers are followers, we adopt the Stackelberg game theory and develop a 0–1 mixed nonlinear bilevel program to maximize the profits of both the manufacturer and his retailers. We further develop a nested genetic algorithm to solve the game model. Numerical examples demonstrate (i) the applicability of the game model and the algorithm and (ii) the robustness of the algorithm. Managerial insights are obtained, suggesting that (i) manufacturers need to identify the capacity ranges (called capacity traps) where capacity increases result in reduced profits when making decisions to optimize profits; (ii) retailers should make suitable, e.g., pricing decisions so that the manufacturers can include them in the supply chains; (iii) both manufacturers and retailers may not need to consider the carbon emission buying (or selling) price when making decisions. |
| Keywords: | Stackelberg game,Nonlinear bilevel programming,Nested genetic algorithm,Emission control,Joint decision making |
| Date: | 2020–12–15 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03276837&r= |
| By: | Tae-Hwy Lee; Ekaterina Seregina |
| Abstract: | In this paper we develop a novel method of combining many forecasts based on a machine learning algorithm called Graphical LASSO. We visualize forecast errors from different forecasters as a network of interacting entities and generalize network inference in the presence of common factor structure and structural breaks. First, we note that forecasters often use common information and hence make common mistakes, which makes the forecast errors exhibit common factor structures. We propose the Factor Graphical LASSO (Factor GLASSO), which separates common forecast errors from the idiosyncratic errors and exploits sparsity of the precision matrix of the latter. Second, since the network of experts changes over time as a response to unstable environments such as recessions, it is unreasonable to assume constant forecast combination weights. Hence, we propose Regime-Dependent Factor Graphical LASSO (RD-Factor GLASSO) and develop its scalable implementation using the Alternating Direction Method of Multipliers (ADMM) to estimate regime-dependent forecast combination weights. The empirical application to forecasting macroeconomic series using the data of the European Central Bank's Survey of Professional Forecasters (ECB SPF) demonstrates superior performance of a combined forecast using Factor GLASSO and RD-Factor GLASSO. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.01697&r= |
| By: | Nora Lustig (Tulane University); Valentina Martinez Pabon (Yale University) |
| Abstract: | A Universal Basic Income (UBI) is often seen as an attractive policy option to replace existing targeted transfer and subsidy programs. However, in a budget-neutral switch to a UBI there is a trade-off between the generosity of the universal transfer, and hence its poverty impact, and the implied increase in tax burden. We summarize our results for fourteen low- and middle-income countries. We find that, with the exception of Russia, a poverty reducing, budget-neutral UBI would entail a significant increase in the net tax burden of top deciles. The efficiency cost and political resistance for such a policy would likely be too high. |
| Keywords: | universal basic income, microsimulation, inequality, poverty, tax incidence |
| JEL: | D31 D63 H22 I32 I38 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:tul:wpaper:2205&r= |
| By: | Lange, Kai-Robin; Reccius, Matthias; Schmidt, Tobias; Müller, Henrik; Roos, Michael W. M.; Jentsch, Carsten |
| Abstract: | Identifying narratives in texts is a challenging task, as not only narrative elements such as the factors and events have to be identified but their semantic relation has to be explained as well. Despite this complexity, an effective technique to extract narratives from texts can have a great impact on how we view political and economical developments. By analyzing narratives, one can get a better understanding of how such narratives spread across the media landscape and change our world views as a result. In this paper, we take a closer look into a recently proposed definition of a collective economic narrative that is characterized by containing a cause-effect relation which is used to explain a situation for a given world view. For the extraction of such collective economic narratives, we propose a novel pipeline that improves the RELATIO-method for statement detection. By filtering the corpus for causal articles and connecting statements by detecting causality between them, our augmented RELATIO approach adapts well to identify more complex narratives following our definition. Our approach also improves the consistency of the RELATIO-method by augmenting it with additional pre- and post-processing steps that enhance the statement detection by the means of Coreference Resolution and automatically filters out unwanted noise in the form of uninterpretable statements. We illustrate the performance of this new pipeline in detecting collective economic narratives by analyzing a Financial Times data set that we filtered for economic and inflation-related terms as well as causal indicators. |
| Keywords: | Econometrics,narrative,text mining,coreference resolution,named entity recognition,causal linking |
| JEL: | C18 C55 C87 E70 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:963&r= |
| By: | Krzysztof Makarski (Group for Research in Applied Economics (GRAPE); Warsaw School of Economics); Joanna Tyrowicz (Group for Research in Applied Economics (GRAPE); University of Warsaw; Institute of Labor Economics (IZA)) |
| Abstract: | We study the evolution of income and wealth inequality in an economy undergoing endogenous structural change with imperfect labor mobility. Our economy features two sectors: services and manufacturing. With faster TFP growth in manufacturing, labor reallocates from manufacturing to services. This reallocation is slower due to labor mobility frictions, which in turn, raises relative wages in services. As a result, income inequality is higher. Moreover, we study the impact of structural change on wealth inequality. Its economic intuition is more ambiguous. On the one hand, increased income dispersion implies increased dispersion in the ability to accumulate wealth across individuals. On the other hand, younger workers who hold the least assets are the most mobile across sectors. Their incomes are improved, which boosts their savings, which works towards equalizing wealth distribution. The consequence of these changes can by only verified with a computational model. To this end we construct and an overlapping generations model with two sectors: manufacturing and services. Our model also features heterogeneous individuals. With our model we are able to show how structural change affected the evolution of income and wealth inequality in Poland as of 1990. |
| Keywords: | structural change, inequality |
| JEL: | L16 E20 D63 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:fme:wpaper:68&r= |
| By: | Nora Lustig (Tulane University); Valentina Martinez Pabon (Yale University); Federico Sanz (Equity Institute and the World Bank); Stephen Younger (Equity Institute) |
| Abstract: | We present a methodological approach with relatively low information requirements to quantify the impact of large, unprecedented macroeconomic shocks like the COVID-19 pandemic on living standards across the income distribution. The approach can be produced quickly and, contrary to other "fast-delivery" exercises, does not assume that income losses are proportional across the income distribution, a feature that is critical to understanding the impact on poverty and inequality. Our method is sufficiently flexible to refine the projected effects of the shock as more information becomes available. We illustrate with data from the four largest countries in Latin America: Argentina, Brazil, Colombia, and Mexico, and discuss the estimated effect of COVID-19 on inequality and poverty. We also present the guidelines for adapting our framework to different countries and economic shocks. |
| Keywords: | COVID-19, inequality, poverty, mobility, microsimulations, Latin America |
| JEL: | C63 D31 E27 I32 I38 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:tul:wpaper:2206&r= |