Econometric Methods Research Articles

Toward sustainable climate action in advanced economies: Linking information communication technology, technological innovation, economic complexity, and ecological footprint

AbstractInformation and communication technology (ICT), technological innovation, and renewable energy (REN) consumption have been proffered as solutions to the recent environmental tragedies in developed countries. In recent times, ICT diffusion and technological innovation have improved in G7 countries, but the same cannot be said of REN consumption. As such, this study examines the link between ICT, economic complexity, technological innovation, REN, and ecological footprint (EF) for G7 countries over the period 1990–2020. We use three variables (fixed telephone subscriptions [FTS], mobile cellular subscriptions [MCS], and individuals using the internet [IUI]) to represent ICT. The presence of cross‐sectional dependence guides the use of second‐generation econometric methods for slope heterogeneity, unit root, cointegration, and parameter estimation. The augment mean group (AMG) estimator and panel OLS techniques are applied to complement the method of moment quantile regression (MM‐QR) approach. The MM‐QR results suggest that REN consumption and technological innovation impede the EF across all quantile levels (0.1–0.9), whereas economic growth and economic complexity augment the EF in G7 countries. The ICT variables have heterogeneous effects on the EF, suggesting that the impact of ICT on the EF depends on the estimation techniques and proxy for the variable. In line with these outcomes, public policies directed toward funding technological innovation projects are recommended. The funding should specifically focus on environmentally friendly technologies that can guarantee complementarity between reduced environmental damage and increased economic growth.

EXPORT DEMAND FOR THAILAND'S KING OF FRUIT

This paper aims to find the determinant of Thailand’s king of fruit exports based on the gravity model. Several primary econometrics methods apply to the gravity model applying to Thailand’s durian export. The result indicates that the country-pair and time-fixed effect PML with interaction regarding the landlock and time dummy variables give the most logical results. The empirical results suggest that enhancing labor productivity in durian cultivation is a strategy to increase durian cultivation and export. Durian exporters might use the lower price strategy to enhance their income. Thailand’s durian exporters should find a specific country in each income-level group where most people love durian.

Empirical Analysis of Factors Influencing China's Imports and Exports

Given the dynamic nature of the international trade landscape and China’s pivotal role in global trade, there is a compelling need to comprehensively investigate the myriad macro-factors influencing China's imports and exports. This study adopts econometric methods, based on a multiple linear model with fitness test and heteroskedasticity test for a comprehensive examination of diverse macro-factors affecting China's trade. Anticipated outcomes include a nuanced quantitative analysis of macro-factors impacting China's imports and exports, aiding understanding China's position in the global supply chain, offering decision-making insights for relevant industries, governments, and investors. The findings are expected to offer comprehensive and in-depth information for academia and practitioners alike, assisting financial decision-makers, business leaders, and investors in navigating global trade uncertainties and economic fluctuations. By delving into the quantitative impact of diverse macro-factors on China's imports and exports, this study aims to fill existing research gaps, providing substantial insights into the Chinese economy and international trade.

Factors Explaining Catastrophic Health Expenditure and HouseholdImpoverishment in Cameroon

The objective of this article is to study the exposure of Cameroonian households to the financial risk linked to catastrophic health expenditure. The study population is made up of households and the data used come from the fourth Cameroon Household Survey conducted in 2014. The statistical and econometric methods deployed are essentially descriptive statistics, hypothesis tests and logistic regression. Analyzes show that Cameroonian households spend an average of 5% of their total expenditure on health. Medication, used primarily by poor households, represents the first health service, followed by consultation and hospitalization, much more used by rich households. A significant proportion of Cameroonian households (10.33%) devote at least 10% of their income to direct payment for health care and services. The incidence of impoverishment due to these direct payments is 1.29%. The socioeconomic characteristics of Cameroonian households are the main determinants of their catastrophic health expenditures and their impoverishment due to direct payments for health care and services.

You Get What You Pay for: An Analysis of Public Contracts for Engineering Services

To understand the effects of bidder’s strategies on contractual modifications, this study utilized econometric methods and a database containing 5,434 engineering service contracts from Brazilian federal government. The results demonstrate the effects arising from the trade-off between the low prices presented in the contractor selection phase and the contractual performance measured by contract modifications. Competition among bidders in a reverse auction with multiple rounds of bid submissions can exacerbate this trade-off, potentially incentivizing risk-taking strategies by bidders. Subsequently, this may lead to the need for contract modifications to facilitate the ongoing execution of the contract.

The Causal Effects of Economic Policy Uncertainty on Changes in Exchange Rates and Volatility: Empirical Evidence from Türkiye

Applying a novel econometric method, nonparametric causality-in-quantiles approach, this paper investigates the causal effects of economic policy uncertainty (EPU) on Turkish changes in exchange rates and volatility with the monthly data spanning from February 1998 to December 2019. This approach of gives an opportunity to investigate the (non)causality in the θ-th quantile only in mean (first moment,i.e., m=1) or variance (second moment,i.e.,m=2) as well as the (non)causality in the mean and variance (m=1 and 2) successively. In sum, this approach calculates volatility by squaring returns. We use EPU indexes of the United States, Australia, European Union, Japan, Canada, and the United Kingdom and their currencies (USD, AUD, EUR, JPY, CAD, GBP, respectively) vis-à-vis Turkish Lira (TRY) and find that the EPUs of Australia, the European Union and Japan affect the returns of the AUD/TRY, EUR/TRY and JPY/TRY exchange rates, respectively. These results show that the EPU indices of these countries can give an idea about the returns and volatility of the relevant Turkish changes in exchange rates. The findings of this paper provide important implications for policymakers, investors, firms, exporters, and importers. Also, some studies can be carried out on the effects of the EPU index that will be created to Türkiye on the Turkish exchange rates or the other Turkish financial assets.

EVALUATING THE IMPACT OF INNOVATION ON BILATERAL TRADE IN NIGERIA

Purpose: the study examined the impact of innovation categorised into copyright, patents, and innovation index, on bilateral trade flows and trade costs on manufactured bilateral trade in Nigeria. Theoretical Framework: challenges facing the international trade such as high import and export tariff, taxes, deflation and inflation, unemployment and in one way or the other affect the economic growth of Nigeria. There is need to investigate the impact of innovation on bilateral trade flows and trade costs on manufactured bilateral trade in Nigeria. Methodology: the study adopted an ex post facto research design. Data were subjected to various diagnostic test ranging from descriptive statistics, unit root test, using Micro-soft excel and E-View 10 and econometric modelling. The researcher employed an econometric method of analysis which makes validation to become necessary. Because it outlines the summary statistics which will display the statistical summary and properties of the variable included in the model. Findings: Some research found a positive but non-significant relationship between technological innovation and economic growth and others showed just a positive relationship. Digital advancement was also shown to exhibit a significant effect on economic growth in Nigeria. Research, Practical & Social Implication: The most consensus recommendation, was an increase in budgetary allocation for the technological development.

Remittance Outflow and Economic Growth Linkage: The Role of Trade,ICT, Human, and Physical Capital

This study focuses on how remittance outflow shapes the economic growth (EG) performance in leading remittance-sending nations, considering the role of trade, ICT, and human and physical capital as control variables. It utilizes panel data from 1990 to 2021 and utilizes second-generation econometric methods. Our findings reveal a cointegration among variables and show that remittance outflow is growth-enhancing in leading remittance-paying countries. Trade, capital formation, and ICT deployment positively impact economic performance and appear to be a blessing for EG. While the role of HC is insignificant, indicating that it does not affect EG. The outcome suggests that remittance outflow is not an economic problem for the remittance-paying nations; instead, they must utilize the talent and skills of migrant labor to achieve EG. Our results suggest that policymakers should concentrate on using the talent and skills of migrant labor, consider trade as a source of growth, develop policies to improve the skills and competencies of the local workforce and deploy and utilize ICT facilities effectively to achieve sustainable EG.

Economic and institutional determinants of environmental health and sustainability: Spatial and nonlinear effects for a panel of worldwide countries

Research background: This study identifies the key factors influencing environmental health across a global panel of countries, focusing on protection from environmental hazards, as informed by the existing literature, while also shedding light on novel aspects of these causal relationships. Purpose of the article: This study aims to reveal, through a comprehensive review of the relevant literature, the underexplored phenomena of spatial diffusion and contagion of national environmental behaviors and the nonlinear dynamics between environmental performance and its determinants, acknowledging the significant diversity in the characteristics and behaviors of the countries studied. Methods: Spatial analysis and econometric methods, including spatial panel regression alongside dynamic panel models using threshold techniques, were employed to meet the study’s objectives. Findings & value added: This study’s major finding is that environmental performance across nations shows significant clustering influenced by economic and institutional factors. This clustering effect arises from spatial contagion and diffusion processes, as evidenced by spatial panel regression analysis. Furthermore, this study demonstrates that variations in environmental behavior can be attributed to differing levels of development and specific internal conditions within countries. Notably, a country’s gross domestic product and the proportion of industries in its economy have a substantial effect on its environmental health practices, establishing distinct impact thresholds. This research enriches academic dialogue by illustrating, through these thresholds, that in less developed countries, an increased industrial share leads to environmental degradation. Moreover, the influence of the other examined factors varied depending on the category of the country under review, highlighting the nuanced effects of economic and institutional variables on environmental outcomes.

GDP nowcasting: A machine learning and remote sensing data-based approach for Bolivia

This research introduces an innovative GDP nowcasting strategy tailored for developing countries, specifically addressing challenges related to limited data timeliness. The study centers on Bolivia, where the official monthly indicator of economic growth is released with a substantial delay of up to six months. The proposed nowcast estimates effectively narrow this gap from six to two months. This advancement is achieved through the integration of machine learning techniques with data comprising indicators from traditional sources and statistics derived from satellite imagery. The robustness of this approach is rigorously validated using various criteria, including performance comparisons with conventional econometric methods and sensitivity assessments to different feature sets. Beyond enhancing the understanding of Bolivia’s economic dynamics, this research establishes a framework for analogous analyses in regions grappling with information availability challenges.

Assessing the Effectiveness of Advertising Costs in the Banking Sector

In recent years, clients of Russian banks have learned to take into account changes in the financial market quickly. When the key rate changes, they change their strategy — the demand for annual deposits decreases, and the flow of funds for short-term deposits increases. It doesn't contradict the interests of the banking sector. At a high level of the refinancing rate on the interbank market, the credit institution is interested in increasing the money supply on deposits. Attracting customers is carried out by banks using a variety of marketing tools, the main of which remains advertising. None of the tools for the bank is free of charge and a traditional economic problem arises: to put in the budget minimal costs for advertising and get a large the effect. It is obvious that in such a formulation the problem is unsolvable. Therefore, currently the theory offers only a single approach to assessing the effectiveness of advertising costs, which the authors call «traditional». Its disadvantage refers to the fact that the generated effect is difficult to divide into impact components, since not only advertising affects the flow of new customers and the share of loyal ones. The approach proposed by the authors makes it possible to assert that the use of an econometric method in management analysis in addition to the traditional method of assessing the effectiveness forms an evidence base for justifying advertising costs by banks. The article reflects the results of the study using the example of JSC «Raiffeisenbank». The advantage of the proposed method is the availability of information: the study is based only on open data published on the website of the Bank of Russia. This will allow Russian commercial banks to assess cost effectiveness after advertising campaigns and during their planning as well as compare the level of effectiveness with competitors.

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities’ prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approach to symbolic regression, based on genetic programming, was also used as a benchmark tool. Secondly, various other econometric methods dealing with variable uncertainty were estimated including Bayesian Model Averaging, Dynamic Model Averaging, LASSO, ridge, elastic net, and least-angle regressions, etc. Therefore, this study reports a concise and uniform comparison of an application of several popular econometric models to forecasting the prices of numerous commodities. Robustness checks and statistical tests were performed to strengthen the obtained conclusions. Monthly data beginning from January 1988 and ending in August 2021 were analysed.

Analisis Ekonomi Pekerja Migran Indonesia dalam Memilih Jenis Pekerjaan pada BP3MI Provinsi Jawa Barat

This article presents the results of economic research on Indonesian migrant workers in choosing the type of work. Primary data were obtained through a survey of prospective migrant workers managed by the Indonesian Migrant Workers Protection Agency of West Java Province. The data explored represent the economic, social, demographic, and institutional aspects of migration, with mixed data properties, namely nominal, ordinal and multinomial. The qualitative limited dependent variable (QLDV) econometric method was used to estimate the multinomial logit equation. The results of the marginal effects estimation found that education level, wage level in the destination country, lack of job opportunities in the place of residence, experience working as a migrant worker, domestic wages, and the existence of income from household members other than husband and wife explained the decision to choose the type of job to work. The probability of choosing the operator/factory occupation is 0.3954, which is greater than other occupations. With a high school education level and an overseas wage of IDR 15.26 million per month.

Bitcoin is an Environmental Disaster. Is the Indictment Distorted?

The literature is inundated with the claim that Bitcoin pollutes the environment. While the assertion is irrefutable, the unsettled issue is whether the indictment of environmental disaster is disproportionate or distorted. This is an empirical question; this paper evaluates it via two econometric methods. First, accepting carbon dioxide (CO2) emission as a proxy for environmental pollution, the paper quantifies the elasticity of CO2 emission in relation to electricity consumption in Bitcoin production. The results reveal that, while Bitcoin production based on conventional electricity is inelastic, carbon emission responsiveness to fossil fuel is significant. A 1% increase in Bitcoin’s usage of coal-generated electricity leads to a 1.64% surge in CO2 emission. Second, the study applies the error correction model to show that some electricity consumption shocks emanate from global coal prices driven by economic factors beyond Bitcoin’s control. This raises the question of whether Bitcoin should shoulder the entire blame for the 1.64% pollution responsiveness. Therefore, the study makes two important contributions. First, Bitcoin’s pollution impact varies according to timespan and electricity source. Second, the intensity of carbon emission from electricity consumption is aggravated by external market factors beyond Bitcoin’s control. The findings should inform policymakers and enlighten environmental advocates.

On the relationships between tourism demand, carbon dioxide emissions and economic growth: a literature review

ABSTRACT Carbon dioxide (CO2) emissions and economic growth are fundamental determinants of international tourism demand. This study is a review of 105 studies on the relationships between tourism demand, CO2 emissions and economic growth that analyses the main results of this literature, detailing the proxies that are used to represent the main variables in question, the indicators used for their measurement, the geographical areas of analysis, the statistical and econometric methods used, and the main results obtained. The analysis shows the state of the art in this field of research, showing its limitations and suggesting several lines for future research at an empirical, conceptual, and methodological level. The study concluded that greater emphasis should be given to statistical and econometric modeling and to the investigation of other variables affecting the relationships between tourism demand, CO2 emissions and economic growth.

Exploring the spatialisation of the performance of residential REITs investment and direct investment in the housing market

ABSTRACT Despite numerous studies about the housing sector, the connection between the performance of residential real estate investment trusts (REITs) investment and direct investment in the housing market is still under-researched. To help fill this gap, we used quarterly data of the Australian Securities Exchange (ASX) residential REITs index and direct housing returns of Australia and its major cities, spanning March 1983 to June 2021, and adopted a suite of econometric methods to document the following findings. First, publicly listed housing (residential REITs) and private housing markets are not separated, but both are closely related in terms of their investment performance. Second, the performance of direct investment in the housing market of Adelaide, Brisbane, Melbourne and Sydney could lead to positive investment performances for residential REITs over time, while no comparable evidence is found for Canberra, Darwin, Hobart and Perth. Third, further analyses revealed a one-way direction of performance, flowing from the housing market of Brisbane, Melbourne and Sydney to residential REITs, while no similar evidence of performance flow was found for Adelaide. Our study has provided new evidence that could contribute to the discussion on the expansion of residential properties in the portfolio of institutional investors.

Industrial Development and Social Values: an Econometric Approach

The article delves into how original institutional economics elucidates processes of industrial development through the lenses of technological, institutional, and social factors. By employing econometric methods, we can further enhance theoretical research within the realm of original institutionalism by quantitatively analyzing social values. Fundamentally, social values form the basis for selecting behavioral patterns, institutions, and institutional frameworks to shape both individual and collective development trajectories. Research on social values yields crucial insights into how the public perceives specific contemporary challenges and issues. The Veblenian dichotomy aptly conceptualizes industrial and technological transformations alongside social values and institutions. Leveraging this dichotomy draws attention to the limitations of market mechanisms driven by contracts and profit motives, underscoring the need for a comprehensive analysis of modern industrial production in light of evolving social values. Through econometric modeling, specifically the use of the ordered logit model, we investigate the impact of industrial development levels and metrics assessing institutional progress in countries, as well as the significance of technology advancement in the future. While Russia exhibits relatively lower indices reflecting both personalized and institutional trust, the model validates a correlation between respondents' subjective evaluations of institutional quality in the country and their perceptions of future technology significance. Moreover, the model confirms a positive association between a country's industrial development level and respondents' views on the importance of technology progression in the future.

Comparing ARIMA, Neural Network and Hybrid Models for Forecasting Fish Production in Bangladesh

Time series forecasting is a commonly applied method for scientific predictions. There are several econometric methods for forecasting time series observations and predicting the systematic pattern of underlying data. ARIMA model is most renowned in this aspect for its linearity. Nowadays a machine learning model namely artificial neural network (ANN) is gaining popularity for its nonlinear characteristics. Inconsistent conclusions may frequently be drawn when evaluating whether ARIMA models or neural networks are better at forecasting future events. For this reason, a hybrid methodology has been established in this study to get advantage from both linear and nonlinear modeling. The annual dataset of fish production in Bangladesh from 1990 to 2020 has been evaluated in this case. Formulating three measurement errors, RMSE, MAE and MAPE it has been demonstrated that the hybrid approach has high level of forecasting accuracy than the other two models in forecasting fish production data. Dhaka Univ. J. Sci. 72(1): 71-76, 2024 (January)

Possibilities of using econometrical methods in studying of interregional interactions of migration in transportation

Thе article presents a set of methodological approaches to forecasting the spatial heterogeneity of transportation migration flows in Russia. The purpose of the study is to develop a methodological basis for forecasting the migration flows spatial heterogeneity. The scientific novelty of the article lies in the presentation and systematization of methodological approaches specific to studying socio-economic factors affecting migration flows considering spatial heterogeneity in the context of the Russia. The study has potential practical value for developing effective migration policies and strategies of spatial development in the country. The study uses general scientific methods of cognition and system-structural analysis and theoretical research method. In the course of the work, a literature review was conducted and forecasting methods applicable in studying migration flows in the ratio of transport accessibility, which are based on the real data analysis, have been analyzed. The results of the study showed that it is possible to study migration flows by spatial autocorrelation methods, and the main explanation of this process is socio-economic factors. Regression analysis revealed a significant correlation between migration flows and such factors as income level, development of transportation infrastructure, accessibility, education and other factors calculated in absolute units. The Moran index application to the migration flows analysis is a scientifically new method that allows us to study the migration spatial aspects in more depth and to identify their characteristics and interrelationships at a new level. The methods base obtained can be useful for calculations and further informed decision-making aimed at reducing spatial inequalities and stimulating sustainable spatial development.

Analyzing the nexus between environmental sustainability and clean energy for the USA

Environmental sustainability is a key target to achieve sustainable development goals (SDGs). However, achieving these targets needs tools to pave the way for achieving SDGs and COP28 targets. Therefore, the primary objective of the present study is to examine the significance of clean energy, research and development spending, technological innovation, income, and human capital in achieving environmental sustainability in the USA from 1990 to 2022. The study employed time series econometric methods to estimate the empirical results. The study confirmed the long-run cointegrating relationship among CO2 emissions, human capital, income, R&D, technological innovation, and clean energy. The results are statistically significant in the short run except for R&D expenditures. In the long run, the study found that income and human capital contribute to further aggravating the environment via increasing CO2 emissions. However, R&D expenditures, technological innovation, and clean energy help to promote environmental sustainability by limiting carbon emissions. The study recommends investment in technological innovation, clean energy, and increasing R&D expenditures to achieve environmental sustainability in the USA.