Econometrics

29 pages, 3837 KiB

Open AccessArticle

Socio-Economic and Demographic Factors Associated with COVID-19 Mortality in European Regions: Spatial Econometric Analysis

by Mateusz Szysz and Andrzej Torój

Econometrics 2023, 11(2), 17; https://doi.org/10.3390/econometrics11020017 - 20 Jun 2023

Cited by 1 | Viewed by 1961

Abstract

In some NUTS 2 (Nomenclature of Territorial Units for Statistics) regions of Europe, the COVID-19 pandemic has triggered an increase in mortality by several dozen percent and only a few percent in others. Based on the data on 189 regions from 19 European [...] Read more.

In some NUTS 2 (Nomenclature of Territorial Units for Statistics) regions of Europe, the COVID-19 pandemic has triggered an increase in mortality by several dozen percent and only a few percent in others. Based on the data on 189 regions from 19 European countries, we identified factors responsible for these differences, both intra- and internationally. Due to the spatial nature of the virus diffusion and to account for unobservable country-level and sub-national characteristics, we used spatial econometric tools to estimate two types of models, explaining (i) the number of cases per 10,000 inhabitants and (ii) the percentage increase in the number of deaths compared to the 2016–2019 average in individual regions (mostly NUTS 2) in 2020. We used two weight matrices simultaneously, accounting for both types of spatial autocorrelation: linked to geographical proximity and adherence to the same country. For the feature selection, we used Bayesian Model Averaging. The number of reported cases is negatively correlated with the share of risk groups in the population (60+ years old, older people reporting chronic lower respiratory disease, and high blood pressure) and the level of society’s belief that the positive health effects of restrictions outweighed the economic losses. Furthermore, it positively correlated with GDP per capita (PPS) and the percentage of people employed in the industry. On the contrary, the mortality (per number of infections) has been limited through high-quality healthcare. Additionally, we noticed that the later the pandemic first hit a region, the lower the death toll there was, even controlling for the number of infections. Full article

(This article belongs to the Special Issue Health Econometrics)

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20 pages, 396 KiB

Open AccessFeature PaperArticle

Skill Mismatch, Nepotism, Job Satisfaction, and Young Females in the MENA Region

by Mahmoud Arayssi, Ali Fakih and Nathir Haimoun

Econometrics 2023, 11(2), 16; https://doi.org/10.3390/econometrics11020016 - 12 Jun 2023

Cited by 2 | Viewed by 2489

Abstract

Skills utilization is an important factor affecting labor productivity and job satisfaction. This paper examines the effects of skills mismatch, nepotism, and gender discrimination on wages and job satisfaction in MENA workplaces. Gender discrimination implies social costs for firms due to higher turnover [...] Read more.

Skills utilization is an important factor affecting labor productivity and job satisfaction. This paper examines the effects of skills mismatch, nepotism, and gender discrimination on wages and job satisfaction in MENA workplaces. Gender discrimination implies social costs for firms due to higher turnover rates and lower retention levels. Young females suffer disproportionality from this than their male counterparts, resulting in a wider gender gap in the labor market at multiple levels. Therefore, we find that the skill mismatch problem appears to be more significant among specific demographic groups, such as females, immigrants, and ethnic minorities; it is also negatively correlated with job satisfaction and wages. We bridge the literature gap on youth skill mismatch’s main determinants, including nepotism, by showing evidence from some developing countries. Given the implied social costs associated with these practices and their impact on the labor market, we have compiled a list of policy recommendations that the government and relevant stakeholders should take to reduce these problems in the workplace. Therefore, we provide a guide to address MENA’s skill mismatch and improve overall job satisfaction. Full article

26 pages, 1914 KiB

Open AccessArticle

Parameter Estimation of the Heston Volatility Model with Jumps in the Asset Prices

by Jarosław Gruszka and Janusz Szwabiński

Econometrics 2023, 11(2), 15; https://doi.org/10.3390/econometrics11020015 - 02 Jun 2023

Cited by 1 | Viewed by 2350

Abstract

The parametric estimation of stochastic differential equations (SDEs) has been the subject of intense studies already for several decades. The Heston model, for instance, is based on two coupled SDEs and is often used in financial mathematics for the dynamics of asset prices [...] Read more.

The parametric estimation of stochastic differential equations (SDEs) has been the subject of intense studies already for several decades. The Heston model, for instance, is based on two coupled SDEs and is often used in financial mathematics for the dynamics of asset prices and their volatility. Calibrating it to real data would be very useful in many practical scenarios. It is very challenging, however, since the volatility is not directly observable. In this paper, a complete estimation procedure of the Heston model without and with jumps in the asset prices is presented. Bayesian regression combined with the particle filtering method is used as the estimation framework. Within the framework, we propose a novel approach to handle jumps in order to neutralise their negative impact on the estimates of the key parameters of the model. An improvement in the sampling in the particle filtering method is discussed as well. Our analysis is supported by numerical simulations of the Heston model to investigate the performance of the estimators. In addition, a practical follow-along recipe is given to allow finding adequate estimates from any given data. Full article

(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)

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11 pages, 313 KiB

Open AccessArticle

Factorization of a Spectral Density with Smooth Eigenvalues of a Multidimensional Stationary Time Series

by Tamás Szabados

Econometrics 2023, 11(2), 14; https://doi.org/10.3390/econometrics11020014 - 31 May 2023

Viewed by 1073

Abstract

The aim of this paper to give a multidimensional version of the classical one-dimensional case of smooth spectral density. A spectral density with smooth eigenvalues and

H^{\infty}

eigenvectors gives an explicit method to factorize the spectral density and compute the Wold representation [...] Read more.

The aim of this paper to give a multidimensional version of the classical one-dimensional case of smooth spectral density. A spectral density with smooth eigenvalues and

H^{\infty}

eigenvectors gives an explicit method to factorize the spectral density and compute the Wold representation of a weakly stationary time series. A formula, similar to the Kolmogorov–Szeg

\overset{”}{o}

formula, is given for the covariance matrix of the innovations. These results are important to give the best linear predictions of the time series. The results are applicable when the rank of the process is smaller than the dimension of the process, which occurs frequently in many current applications, including econometrics. Full article

(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)

19 pages, 6901 KiB

Open AccessArticle

Online Hybrid Neural Network for Stock Price Prediction: A Case Study of High-Frequency Stock Trading in the Chinese Market

by Chengyu Li, Luyi Shen and Guoqi Qian

Econometrics 2023, 11(2), 13; https://doi.org/10.3390/econometrics11020013 - 18 May 2023

Cited by 1 | Viewed by 2457

Abstract

Time-series data, which exhibit a low signal-to-noise ratio, non-stationarity, and non-linearity, are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in [...] Read more.

Time-series data, which exhibit a low signal-to-noise ratio, non-stationarity, and non-linearity, are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in huge quantities. For this purpose, it is essential to apply a trading method that is capable of fast and accurate prediction from such time-series data. In this paper, we developed an online time series forecasting method for high-frequency trading (HFT) by integrating three neural network deep learning models, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), and transformer; and we abbreviate the new method to online LGT or O-LGT. The key innovation underlying our method is its efficient storage management, which enables super-fast computing. Specifically, when computing the forecast for the immediate future, we only use the output calculated from the previous trading data (rather than the previous trading data themselves) together with the current trading data. Thus, the computing only involves updating the current data into the process. We evaluated the performance of O-LGT by analyzing high-frequency limit order book (LOB) data from the Chinese market. It shows that, in most cases, our model achieves a similar speed with a much higher accuracy than the conventional fast supervised learning models for HFT. However, with a slight sacrifice in accuracy, O-LGT is approximately 12 to 64 times faster than the existing high-accuracy neural network models for LOB data from the Chinese market. Full article

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27 pages, 1943 KiB

Open AccessArticle

Local Gaussian Cross-Spectrum Analysis

by Lars Arne Jordanger and Dag Tjøstheim

Econometrics 2023, 11(2), 12; https://doi.org/10.3390/econometrics11020012 - 21 Apr 2023

Viewed by 1664

Abstract

The ordinary spectrum is restricted in its applications, since it is based on the second-order moments (auto- and cross-covariances). Alternative approaches to spectrum analysis have been investigated based on other measures of dependence. One such approach was developed for univariate time series by [...] Read more.

The ordinary spectrum is restricted in its applications, since it is based on the second-order moments (auto- and cross-covariances). Alternative approaches to spectrum analysis have been investigated based on other measures of dependence. One such approach was developed for univariate time series by the authors of this paper using the local Gaussian auto-spectrum based on the local Gaussian auto-correlations. This makes it possible to detect local structures in univariate time series that look similar to white noise when investigated by the ordinary auto-spectrum. In this paper, the local Gaussian approach is extended to a local Gaussian cross-spectrum for multivariate time series. The local Gaussian cross-spectrum has the desirable property that it coincides with the ordinary cross-spectrum for Gaussian time series, which implies that it can be used to detect non-Gaussian traits in the time series under investigation. In particular, if the ordinary spectrum is flat, then peaks and troughs of the local Gaussian spectrum can indicate nonlinear traits, which potentially might reveal local periodic phenomena that are undetected in an ordinary spectral analysis. Full article

(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)

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11 pages, 345 KiB

Open AccessArticle

Information-Criterion-Based Lag Length Selection in Vector Autoregressive Approximations for I(2) Processes

by Dietmar Bauer

Econometrics 2023, 11(2), 11; https://doi.org/10.3390/econometrics11020011 - 20 Apr 2023

Viewed by 1523

Abstract

When using vector autoregressive (VAR) models for approximating time series, a key step is the selection of the lag length. Often this is performed using information criteria, even if a theoretical justification is lacking in some cases. For stationary processes, the asymptotic properties [...] Read more.

When using vector autoregressive (VAR) models for approximating time series, a key step is the selection of the lag length. Often this is performed using information criteria, even if a theoretical justification is lacking in some cases. For stationary processes, the asymptotic properties of the corresponding estimators are well documented in great generality in the book Hannan and Deistler (1988). If the data-generating process is not a finite-order VAR, the selected lag length typically tends to infinity as a function of the sample size. For invertible vector autoregressive moving average (VARMA) processes, this typically happens roughly proportional to

log T

. The same approach for lag length selection is also followed in practice for more general processes, for example, unit root processes. In the I(1) case, the literature suggests that the behavior is analogous to the stationary case. For I(2) processes, no such results are currently known. This note closes this gap, concluding that information-criteria-based lag length selection for I(2) processes indeed shows similar properties to in the stationary case. Full article

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15 pages, 492 KiB

Open AccessArticle

Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

by Paul Haimerl and Tobias Hartl

Econometrics 2023, 11(2), 10; https://doi.org/10.3390/econometrics11020010 - 03 Apr 2023

Viewed by 2463

Abstract

The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the [...] Read more.

The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the prevalence of either an infection up- or down-turning regime for every day of the observational period. This method provides an intuitive real-time analysis of the state of the pandemic as well as a tool for identifying structural changes ex post. We find that when applied to U.S. data, the model closely tracks regime changes caused by viral mutations, policy interventions, and public behavior. Full article

(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)

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Econometrics, Volume 11, Issue 2 (June 2023) – 8 articles

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