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A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 33200

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Prof. Dr. Maria-Isabel Ayuda

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Guest Editor

Department of Economic Analysis, Universidad de Zaragoza, Gran Vía 2, 50005 Zaragoza, Spain
Interests: econometrics; cliometrics; international trade; anthropometrics; model selection

Prof. Dr. María Dolores Gadea Rivas

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Guest Editor

Department of Applied Economics, University of Zaragoza, Gran Vía 2, 50005 Zaragoza, Spain
Interests: applied econometrics; time series; macroeconomics; climate change

Special Issue Information

Dear Colleagues,

For many decades now, research in economics has mainly used statistical tools to validate its theoretical models or to obtain relevant empirical results on the main topics and issues it addresses. In applied economics, statistical and econometric methods have become the essential working tools.

This Special Issue aims to bring together recent developments on the statistical methods applied to economic research from a wide range of perspectives. Both methodological and empirical contributions are welcome, as well as international case studies at different geographical scales and time spans. This includes historical perspectives, theoretical discussions, policy design, micro and macro approaches, and international and regional studies, among others. The empirical articles in this Special Issue can be on a variety of topics, but we would like them to be oriented towards the main topics discussed today, such as the causes and dynamics of economic growth, the determinants of international trade, environmental economics and climate change, inequality in income distribution, globalization, migration, public expenditure and income, natural resources, and welfare economics.

Prof. Dr. Maria-Isabel Ayuda
Prof. Dr. María Dolores Gadea Rivas
Guest Editors

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Keywords

Econometrics
Statistical methods
Economic growth
International trade
Environmental economics
Climate change
Inequality in income distribution
Globalization
Natural resources
Business cycles
Health economics

Published Papers (13 papers)

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Research

16 pages, 349 KiB

Open AccessArticle

Nonlinear Contagion and Causality Nexus between Oil, Gold, VIX Investor Sentiment, Exchange Rate and Stock Market Returns: The MS-GARCH Copula Causality Method

by Melike E. Bildirici, Memet Salman and Özgür Ömer Ersin

Mathematics 2022, 10(21), 4035; https://doi.org/10.3390/math10214035 - 31 Oct 2022

Cited by 4 | Viewed by 1784

Abstract

The fluctuations in oil have strong implications on many financial assets not to mention its relationship with gold prices, exchange rates, stock markets, and investor sentiment. Recent evidence suggests nonlinear contagion among the factors stated above with bivariate or trivariate settings and a throughout investigation of contagion and causality links by taking especially nonlinearity into consideration deserves special importance for the relevant literature. For this purpose, the paper explores the Markov switching generalized autoregressive conditional heteroskedasticity copula (MS-GARCH—copula) and MS-GARCH-copula-causality method and its statistical properties. The methods incorporate regime switching and causality analyses in addition to modeling nonlinearity in conditional volatility. For a sample covering daily observations for 4 January 2000–13 March 2020, the empirical findings revealed that: i. the incorporation of MS type nonlinearity to copula analysis provides important information, ii. the new method helps in the determination of regime-dependent tail dependence among oil, VIX, gold, exchange rates, and BIST stock market returns, in addition to determining the direction of causality in those regimes, iii. important policy implications are derived with the proposed methods given the distinction between high and low volatility regimes leads to different solutions on the direction of causality. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

21 pages, 7749 KiB

Open AccessArticle

Modelling the Dynamic Interaction between Economic Policy Uncertainty and Commodity Prices in India: The Dynamic Autoregressive Distributed Lag Approach

by Rasool Dehghanzadeh Shahabad and Mehmet Balcilar

Mathematics 2022, 10(10), 1638; https://doi.org/10.3390/math10101638 - 11 May 2022

Cited by 6 | Viewed by 1716

Abstract

This study examines the dynamic interaction between oil, natural gas, and prices with Indian economic policy uncertainty (EPU). The study finds that gold prices and industrial production are fundamental drivers of Indian economic policy uncertainty in both the short and long runs, using a dynamic autoregressive distributed lag (ARDL) model with monthly data ranging from January 2003 to July 2020. Gold prices are positively related to the Indian EPU, while industrial production is negatively related to it. Thus, investors in the Indian economy should use gold as a hedge for portfolio diversification and as a safe haven during an economic crisis. We also find a significant positive interconnection between gold prices and crude oil prices in both the short run and the long run, while the significant positive impact of natural gas prices on crude oil prices manifests only in the long run. The evidence also indicates that the EPUs of the US and Europe positively affect the Indian EPU, while the EPU of China does not have a significant effect. Higher crude oil prices are associated with higher gas prices, whereas higher gold prices are negatively associated with the natural gas price and vice versa. Furthermore, the evidence shows that the Indian EPU does not have a significant effect on the changes in the prices of goods. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

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17 pages, 343 KiB

Open AccessArticle

Tourism Development and Economic Growth in Southeast Asian Countries under the Presence of Structural Break: Panel Kink with GME Estimator

by Paravee Maneejuk, Woraphon Yamaka and Wilawan Srichaikul

Mathematics 2022, 10(5), 723; https://doi.org/10.3390/math10050723 - 24 Feb 2022

Cited by 8 | Viewed by 2175

Abstract

This study examines the nonlinear impact of tourism development on economic growth in Southeast Asian countries using the panel kink regression model. Due to the paucity of Southeast Asian data, we may face the overparameterization problem in our model. To deal with this problem, this study proposes the Generalized Maximum Entropy (GME) estimator to estimate the unknown parameters in this nonlinear model. Several important tourism development indicators consisting of the total international arrivals, international tourism expenditure, and tourism receipts are considered. In addition, we also consider the gross capital formation and real effective exchange rate as a control variable in our nonlinear model. Our findings show that the effect of international tourist arrivals on economic growth should be separated into two regimes, while other factors do not exhibit a nonlinear relationship with Southeast Asian economic growth. Thus, we construct the empirical model with the kink effect in the variable of international tourist arrivals. To confirm the performance of the GME estimator, we compare it to the ordinary least squares and the fixed effect estimators. According to the mean squared and root means squared errors, we find that our GME estimator performs better than the ordinary least squares and the fixed effect estimators. This indicates that GME estimation is an applicable method for estimating the nonlinear effect of tourism growth on economic growth. Our empirical results show that there are positive impacts of tourism growth on economic growth for regime 1 (low tourism demand) and regime 2 (high tourism demand) with the effect of the low tourist arrivals regime being relatively larger. We also find a positive influence of gross capital formation, real effective exchange rate, international tourism expenditure, and tourism receipts on Southeast Asian economic growth. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

15 pages, 610 KiB

Open AccessArticle

Selection Criteria for Overlapping Binary Models—A Simulation Study

by Teresa Aparicio and Inmaculada Villanúa

Mathematics 2022, 10(3), 478; https://doi.org/10.3390/math10030478 - 02 Feb 2022

Cited by 1 | Viewed by 1020

Abstract

This paper deals with the problem of choosing the optimum criterion for selecting the best model out of a set of overlapping binary models. The criteria we studied were the well-known AIC and SBIC, and a third one called

C_{2}

. Special [...] Read more.

C_{2}

. Special attention was paid to the setting where neither of the competing models was correctly specified. This situation has not been studied very much but it is the most common case in empirical works. The theoretical study we carried out allowed us to conclude that, in general terms, all criteria perform well. A Monte Carlo exercise corroborated those results. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

22 pages, 696 KiB

Open AccessArticle

Latin American Agri-Food Exports, 1994–2019: A Gravity Model Approach

by María-Isabel Ayuda, Ignacio Belloc and Vicente Pinilla

Mathematics 2022, 10(3), 333; https://doi.org/10.3390/math10030333 - 21 Jan 2022

Cited by 6 | Viewed by 2770

Abstract

This study analyses the causes of the strong growth in the agri-food exports of Latin America between 1994 and 2019. To do this, a series of gravity models are estimated, using as a dependent variable the agri-food exports of 15 Latin American countries to their 185 principal trading partners. The empirical specification is based on the gravity theory of trade, according to which, trade between two countries is determined by the size of both of their markets and their transport costs. Other variables have also been included, considering the theoretical foundations proposed for the gravity model. We initially used the PPML estimator since it is the method that provides estimates with the best properties. We later compared these results with those obtained through OLS and the Heckman selection model. Our findings show that the growth in agri-food exports is explained by factors of both supply and demand, but that the latter plays a more important role since we have obtained evidence of a reverse home-market effect. Furthermore, we can conclude that the creation of regional trade agreements, such as NAFTA, MERCOSUR, CACM, APEC, and TPP, has significantly favoured agri-food exports in the region. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

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26 pages, 2513 KiB

Open AccessArticle

The Impact of Electronic Money on Monetary Policy: Based on DSGE Model Simulations

by Sumei Luo, Guangyou Zhou and Jinpeng Zhou

Mathematics 2021, 9(20), 2614; https://doi.org/10.3390/math9202614 - 17 Oct 2021

Cited by 9 | Viewed by 5532

Abstract

Starting with the interactive relationship between electronic money and household consumption stimuli, this paper deeply analyzes the changes in the behavior of each monetary subject under the impact of electronic money, and establishes a DSGE model based on the three economic sectors of family, commercial bank and central bank under the New Keynesian framework. On this basis, the impact of electronic money on savings, loans, output and the interest rate, and its impact on monetary policy, are described by numerical simulation. The simulation results show that: (1) electronic money has asymmetric effects on savings and loans, but an irrational deviation on households; (2) the influence of electronic money on the interest rate has a reverse effect, and the “inverse adjustment” of the interest rate increases the management difficulty of the micro subject to a certain extent, and affects the effectiveness of monetary policy; (3) the regulatory effect of price monetary policy is better than that of quantitative monetary policy, and electronic money has the effect of its risk restraining impact. Finally, based on the analysis, this paper gives policy recommendations. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

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23 pages, 2829 KiB

Open AccessArticle

The Role of Economic Contagion in the Inward Investment of Emerging Economies: The Dynamic Conditional Copula Approach

by Paravee Maneejuk and Woraphon Yamaka

Mathematics 2021, 9(20), 2540; https://doi.org/10.3390/math9202540 - 10 Oct 2021

Cited by 2 | Viewed by 1370

Abstract

Contagion has been one of the most widely studied and challenging problems in recent economic research. This paper aims at capturing the main impact of contagion risk of the U.S. on foreign direct investment inflows in 18 emerging countries. To quantify the degree of contagion, the time-varying tail dependence copula is employed. Then, the Granger causality test and time series regression analysis are used to investigate the temporal and contemporaneous effects of contagion risk on investment inflows, respectively. Overall, the results confirm the time-varying contagion effects of the U.S. economy on 18 emerging economies. The size of contagion effects gradually increases for all countries, except Thailand, the Philippines, Argentina, and Chile. Furthermore, the results of the Granger causality test and regression reveal that temporal and contemporaneous effects of contagion risk on investment inflows exist in 8 out of 18 countries. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

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16 pages, 322 KiB

Open AccessArticle

Geopolitical Uncertainties and Malaysian Stock Market Returns: Do Market Conditions Matter?

by Mohammad Enamul Hoque, Mohd Azlan Shah Zaidi and M. Kabir Hassan

Mathematics 2021, 9(19), 2393; https://doi.org/10.3390/math9192393 - 26 Sep 2021

Cited by 3 | Viewed by 3287

Abstract

Geopolitical uncertainties have been a concern for global economies and financial markets’ participants. By employing Markov switching regression and quantile regression, we investigated the effect of global and country-specific geopolitical uncertainties on Malaysian Conventional and Islamic stock returns in different market conditions. The estimated results of the Markov switching regression show that Malaysian conventional and Islamic stocks react differently to global and country-specific geopolitical uncertainties under different market volatility conditions, implying volatility dependent exposures and reactions to global and country-specific geopolitical uncertainties. The quantile regression results also reveal that Malaysian conventional and Islamic stocks respond differently to global and country-specific geopolitical uncertainties at different market stages. The empirical findings, therefore, indicate a heterogeneous and non-linear stock reaction to geopolitical uncertainties, providing new insights into geopolitical uncertainties and stock return relationships. Hence, the results will be valuable for asset pricing and investments in an emerging market such as the Malaysian market. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

17 pages, 1745 KiB

Open AccessArticle

An Automatic Algorithm to Date the Reference Cycle of the Spanish Economy

by Maximo Camacho, María Dolores Gadea and Ana Gómez-Loscos

Mathematics 2021, 9(18), 2241; https://doi.org/10.3390/math9182241 - 12 Sep 2021

Viewed by 1460

Abstract

This paper provides an accurate chronology of the Spanish reference business cycle adapting a multiple change-point model. In that approach, each combination of peaks and troughs dated in a set of economic indicators is assumed to be a realization of a mixture of bivariate Gaussian distributions, whose number of components is estimated from the data. The means of each of these components refer to the dates of the reference turning points. The transitions across the components of the mixture are governed by Markov chain that is restricted to force left-to-right transition dynamic. In the empirical application, seven recessions in the period from February 1970 to February 2020 are identified, which are in high concordance with the timing of the turning point dates established by the Spanish Business Cycle Dating Committee (SBCDC). Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

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36 pages, 52347 KiB

Open AccessArticle

Co-Movements between Eu Ets and the Energy Markets: A Var-Dcc-Garch Approach

by Pilar Gargallo, Luis Lample, Jesús A. Miguel and Manuel Salvador

Mathematics 2021, 9(15), 1787; https://doi.org/10.3390/math9151787 - 28 Jul 2021

Cited by 10 | Viewed by 2502

Abstract

This paper analyzes the co-movements of prices of fossil fuels, energy stock markets and EU allowances. This analysis is conducted in order to identify the spillover effect of volatility and correlation among these financial markets, and to provide a scientific basis that shows the interest of incorporating sustainable assets in the design of minimum risk strategies of investment. To achieve this goal, we have used a Vector Autoregressive-Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (VAR-DCC-GARCH) model that also incorporates a stock index of industrial companies as a leading indicator of the level of economic activity. In addition, the paper conducts an impulse response analysis to determine how unexpected shocks to prices are propagated along time, and, in particular, how they affect prices of the others, both in mean, variance and correlation. Therefore, the results of this one- and two-dimensional analysis allow for the study of short and long run dynamics of the relationship among those prices, thus, providing greater meaning and information for investors, which has implications for building their portfolios. The analyzed period was from January 2010 to February 2021, so that the data include half of phase II, full phase III and the onset of phase IV of the EU ETS, as well as the COVID-19 outbreak in the European context. We also analyzed whether the EUA price impulses the demand of clean energy stocks, which has important implications for the objective of triggering the investment in clean energy. Our results show the transmission mechanism of all of those prices, which are relevant not only for investors but also for policymakers to construct an early-warning system, revealing the most important transmission channels. Moreover, from an investment viewpoint, we observe a decline in dirty energies and a rise in the clean energy market, which might be an indication of the progress towards the energy transition to renewables sources within a circular economy perspective. Therefore, this shows that the EU ETS is achieving its goals, and that clean energy companies, aligned with their role towards socially responsible initiatives, are also gaining acceptance in terms of investments, which would be beneficial for the environment. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

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26 pages, 1778 KiB

Open AccessArticle

Collaborative Service Innovation: A Quantitative Analysis of Innovation Networks in a Multisectoral Setting

by Alberto Peralta and Luis Rubalcaba

Mathematics 2021, 9(11), 1270; https://doi.org/10.3390/math9111270 - 01 Jun 2021

Cited by 2 | Viewed by 2400

Abstract

Partial least squares structural equation modelling has proven very valuable to study the unexplored and complex public service innovation networks (PSINs) in the public sector, from a socio-economic stance. Web have modelled PSINs’ three structural variables—Social, Actors, and Functioning mode—using a sample of original data (n = 233). Our PSINs’ model confirms them as instruments that produce public service innovation—involving technological and nontechnological characteristics. Additionally, we set-up a novel and potentially fruitful methodology to study the intricate formation and impact of complex socio-economical structures that connect innovation and public services. Hence, our research supports a better and extended use of PSINs as a tool for policy and service co-design and co-implementation. And we open a promising line of studies involving multi-actor collaboration in the public sector. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

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17 pages, 2082 KiB

Open AccessFeature PaperArticle

Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time

by Juan Bógalo, Pilar Poncela and Eva Senra

Mathematics 2021, 9(11), 1169; https://doi.org/10.3390/math9111169 - 22 May 2021

Cited by 1 | Viewed by 1609

Abstract

Real-time monitoring of the economy is based on activity indicators that show regular patterns such as trends, seasonality and business cycles. However, parametric and non-parametric methods for signal extraction produce revisions at the end of the sample, and the arrival of new data makes it difficult to assess the state of the economy. In this paper, we compare two signal extraction procedures: Circulant Singular Spectral Analysis, CiSSA, a non-parametric technique in which we can extract components associated with desired frequencies, and a parametric method based on ARIMA modelling. Through a set of simulations, we show that the magnitude of the revisions produced by CiSSA converges to zero quicker, and it is smaller than that of the alternative procedure. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

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22 pages, 3723 KiB

Open AccessEditor’s ChoiceArticle

Stochastic Chebyshev Goal Programming Mixed Integer Linear Model for Sustainable Global Production Planning

by Chia-Nan Wang, Nhat-Luong Nhieu and Trang Thi Thu Tran

Mathematics 2021, 9(5), 483; https://doi.org/10.3390/math9050483 - 26 Feb 2021

Cited by 20 | Viewed by 2785

Abstract

Production planning is a necessary process that directly affects the efficiency of production systems in most industries. The complexity of the current production planning problem depends on increased options in production, uncertainties in demand and production resources. In this study, a stochastic multi-objective mixed-integer optimization model is developed to ensure production efficiency in uncertainty conditions and satisfy the requirements of sustainable development. The efficiency of the production system is ensured through objective functions that optimize backorder quantity, machine uptime and customer satisfaction. The other three objective functions of the proposed model are related to optimization of profits, emissions, and employment changing. The objective functions respectively represent the three elements of sustainable development: economy, environment, and sociality. The proposed model also assures the production manager’s discretion over whether or not to adopt production options such as backorder, overtime, and employment of temporary workers. At the same time, the resource limits of the above options can also be adjusted according to the situation of each production facility via the model’s parameters. The solutions that compromise the above objective functions are determined with the Chebyshev goal programming approach together with the weights of the goals. The model is applied to the multinational production system of a Southeast Asian supplier in the textile industry. The goal programming solution of the model shows an improvement in many aspects compared to this supplier’s manufacturing practices under the same production conditions. Last but not least, the study develops different scenarios based on different random distributions of uncertainty demand and different weights between the objective functions. The analysis and evaluation of these scenarios provide a reference basis for managers to adjust the production system in different situations. Analysis of uncertain demand with more complex random distributions as well as making predictions about the effectiveness of scenarios through the advantages of machine learning can be considered in future studies. Full article

(This article belongs to the Special Issue Statistical Methods in Economics)

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