Econometric Applications Research Articles

Statistical Analysis of Poisson Bifurcated Autoregressive Modeling

This study evaluates the Poisson Bifurcated Autoregressive (PBAR) model for analyzing time-varying count data, focusing on exchange rate datasets from Anglophone countries. As an extension of the traditional Bifurcated Autoregressive (BAR) model, the PBAR model incorporates a Poisson-distributed error term, enabling it to capture non-Gaussian features and bifurcating structures common in degenerate economic data. Using historical exchange rates from the Central Bank of Nigeria’s Statistical Bulletin, the study compares the PBAR and BAR models based on Akaike Information Criterion (AIC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Results show the PBAR model consistently achieves better fit and accuracy. For Equatorial Guinea (XAF), PBAR recorded an AIC of 0.4077, MAE of 0.249, and RMSE of 0.875, while the BAR model had values of 0.4694, 0.847, and 1.075, respectively. Similarly, for Ghana (GHS), PBAR produced an AIC of 0.3500, MAE of 0.152, and RMSE of 1.026, outperforming BAR’s values of 0.4207, 1.080, and 1.086. Across all datasets, the PBAR model demonstrated improved accuracy and robustness. These findings highlight the PBAR model’s advantage in forecasting non-linear, bifurcating time series. The study concludes that PBAR offers a significant enhancement over traditional models, making it well-suited for financial econometrics applications where accurate modeling of count-based, irregular data is essential

A spatial Durbin model for interval-valued data with t-distribution

Interval-valued data have received much attention due to their wide applications in finance, econometrics, meteorology and medicine. However, most regression models developed for interval-valued data assume that the observations are mutually independent, which can not be adapted to scenarios where individuals are spatially correlated. We propose a new linear model to account for areal-type spatial dependencies present in interval-valued data. Specifically, two spatial Durbin models are separately built for the centers and log-ranges of the interval-valued response. To make the new model more robust to heavy-tailed noise, we assume the error term follows a t-distribution. The parameters are obtained using an expectation-maximization (EM) algorithm. Numerical experiments are designed to examine the performance of the proposed method. We also use a weather dataset to demonstrate the usefulness of our model.

Novel of statistical quality control development and econometric applications

Novel of statistical quality control development and econometric applications

Application of Econometrics in Agricultural Production

This Special Issue on “Applications of Econometrics in Agricultural Production” has aimed to rebuild and extend the approach to agricultural production analysis by including econometric methods for (a) developing a new paradigm for agricultural production analysis that acknowledges and models the relevance of the combined economic and agronomic aspects of the production processes; (b) defining output and input demand and supply in agricultural production from a technical perspective, with the use of production function/or production frontier models; and (c) understanding agricultural market exchange and market distortions and failures from a quantitative perspective [...]

The Fourier–Malliavin Volatility (FMVol) MATLAB® library

This paper presents the Fourier–Malliavin Volatility (FMVol) estimation library for MATLAB®. This library includes functions that implement Fourier–Malliavin estimators (see Malliavin and Mancino (2002, 2009)) of the volatility and co-volatility of continuous stochastic volatility processes and second-order quantities, like the quarticity (the squared volatility), the volatility of volatility and the leverage (the covariance between changes in the process and changes in its volatility). The Fourier–Malliavin method is fully non-parametric, does not require equally-spaced observations and is robust to measurement errors, or noise, without any preliminary bias correction or pre-treatment of the observations. Furthermore, in its multivariate version, it is intrinsically robust to irregular and asynchronous sampling. Although originally introduced for a specific application in financial econometrics, namely the estimation of asset volatilities, the Fourier–Malliavin method is a general method that can be applied whenever one is interested in reconstructing the latent volatility and second-order quantities of a continuous stochastic volatility process from discrete observations.

Sustainable Tourism in Romania: Integrating Economic, Social and Environmental Dimensions

Abstract Tourism and sustainable development are two interlinked areas, as tourism in its traditional form can bring significant economic and social benefits, but can also have a negative impact on the environment and local communities. Due to global changes that have occurred in recent years, such as population growth, accelerated urbanisation, climate change and threats to biodiversity, research in tourism and sustainable development is becoming essential to create a more responsible and balanced approach to these activities. In this paper we have focused on presenting the evolution of tourism over the period 1992-2021 and testing linear multiple regression analyses to establish the nature and extent of relationships between tourism, economic, social and environmental indicators, thereby demonstrating the impact of tourism on sustainable development. The econometric applications carried out in this work - a combined model for studying the intention of tourists to return to a tourist destination, a regression model taking into account spatial elements for evaluating the defining characteristics of accommodation units in Romanian tourism and a growth optimisation model taking into account the influence of the environment - allow the quantification of different categories of impact generated by tourism activities.

Empirical-Likelihood-Based Inference for Partially Linear Models

Partially linear models find extensive application in biometrics, econometrics, social sciences, and various other fields due to their versatility in accommodating both parametric and nonparametric elements. This study aims to establish statistical inference for the parametric component effects within these models, employing a nonparametric empirical likelihood approach. The proposed method involves a projection step to eliminate the nuisance nonparametric component and utilizes an empirical-likelihood-based technique, along with the Bartlett correction, to enhance the coverage probability of the confidence interval for the parameter of interest. This method demonstrates robustness in handling normally and non-normally distributed errors. The proposed empirical likelihood ratio statistic converges to a limiting chi-square distribution under certain regulations. Simulation studies demonstrate that this method provides better inference in terms of coverage probabilities compared to the conventional normal-approximation-based method. The proposed method is illustrated by analyzing the Boston housing data from a real study.

Application of Bayesian Econometrics in Policy Evaluation

Application of Bayesian Econometrics in Policy Evaluation

Organization of a Regional Computer Unit for Statistical Econometrics applications, Database Organization Access to International Bibliography

Organization of a Regional Computer Unit for Statistical Econometrics applications, Database Organization Access to International Bibliography

The impact of agriculture and industry sectors on environmental pollution: an econometric application in selected OECD member countries

İnsanoğlu yaradılışından bu yana çevre ile sürekli etkileşim halinde olmuştur. Tarım ve sanayi sektörlerinde yaşanan gelişmeler; yaşamı daha konforlu hale getirirken, ekonomik büyüme ve refah düzeyinde artışlar sağlanmış ancak, doğaya daha fazla müdahale edildiğinden, çevre üzerinde baskılar artmış, yaşam alanlarının bozulmasına yol açmıştır. Gelişmiş ve gelişmekte olan ülkelerde hızlı sanayileşme süreci ile enerji kullanımındaki artışlar CO2 salınımını artırarak çevresel tahribatlara neden olmuştur. Çevresel tahribattaki artışlara paralel çevre kalitesindeki düşüşler ülkelerin, endişelerini artırdığından bu konulardaki araştırmalar öncelenmiş, sürdürülebilir çevre dostu ekonomik büyüme hedefleri ortaya konulmuştur. Bu çalışmanın amacını tarım ve sanayi sektöründeki gelişmelerin çevre kirliliği üzerindeki etkisini seçilmiş OECD üyesi ülkelerinde (Fransa, Almanya, ABD, Birleşik Krallık, Danimarka, Hollanda, Belçika, Japonya, İtalya ve İspanya) araştırmak olmuştur. Çalışmada 2000-2019 dönemine ait veri setinden hareketle panel veri analizi uygulanmıştır. Çalışmada uygulanan FMOLS analizi bulgularına göre; uzun dönemde tarımsal katma değerdeki 1 birimlik artış CO2 emisyonlarını 0.12 birim; sanayileşmedeki 1 birimlik artış CO2 emisyonlarını 0.31 birim ve tarımsal arazi alanındaki 1 birimlik artış CO2 salınımlarını 0.92 birim artırdığı belirlenmiştir. Bununla birlikte çalışmada uygulanan panel DOLS analizinden elde edilen sonuçlara göre; uzun dönemde tarımsal katma değerdeki 1 birimlik artış CO2 salınımlarında 1.02 birim; tarımsal arazi alanındaki 1 birimlik artış ise CO2 emisyonlarında 1.41 birimlik artışa neden olduğu tespit edilmiştir.

Global Energy Transformation and the Impacts of Systematic Energy Change Policy on Climate Change Mitigation

This study aims to evaluate the effect of global energy transformation and systematic energy change on climate change. The model is constructed from dynamic panel data which comprises 26 world regions from the World Database Indicators (WDIs), International Energy Atomic (IEA), and International Monetary Fund (IMF), with a span from 2005 to 2022. The Generalized system Method of Moment (sys-GMM) and pooled OLS and random effect models have been used to empirically evaluate the linked effect of global transformation and systematic change on climate change. The sys-GMM approach is used to control the endogeneity of the lagged dependent variable when there is an association between the exogenous variable and the error term. Furthermore, it omits variable bias, measurement errors in the estimation, and unobserved panel heterogeneity. The econometric applications allow us to quantify the direct effect of global transformation and systematic change on climate change. The empirical analysis revealed that renewable energy, alternative energy, technology and innovation, and financial climate have a negative effect on climate change. It means that increasing consumption of the transformation energies leads to reducing the effect of climate change. However, fossil energy is statistically significant and positively affects climate change. Increasing the consumption of fossil energy raises the effect of climate change. There is a global need for massive decarbonization infrastructure that will help minimize the global warming that leads to climate change. Policies that take an endogenous approach through global transformation and systematic change should be implemented to reduce the effect of climate change. The policy should reduce the consumption of non-renewable energy and increase the consumption of renewable energy.

Application of econometrics in energy research—Empowerment from big data and machine learning

AbstractThis article expounds on the challenges of sustainable global energy development to conventional econometrics in energy research. It introduces energy big data's “4V” characteristics, that is, volume, variety, velocity, and veracity. Further, the article presents a series of challenges the “4V” feature brings to conventional econometrics research on energy issues. Then, by reviewing the existing literature, the article proposes the improvement and promotion of conventional econometrics in the three directions of data exploration and variable generation, prediction, and causal inference of machine learning in big data. Finally, the article concludes that the discipline of econometrics will never go out of fashion. Although big data and machine learning will increasingly challenge the conventional econometrics research paradigm, econometrics will be reborn in the way of “phoenix nirvana”.This article is categorized under: Emerging Technologies > Digitalization

Editorial announcement

I am delighted to welcome Sam Astill, James Duffy and Liudas Giraitis to the editorial board of the Journal of Time Series Analysis. All three join as Associate Editors with effect from 1 March 2023. At the same time, I would like to thank Professor Konstantinos Fokianos, who steps down as an Associate Editor with effect from 1 March 2023, for his work for the journal in this capacity since 2013. Sam Astill is a Senior Lecturer in Econometrics at Essex Business School. His research interests include theoretical and applied time series econometrics and financial econometrics, in particular asset price bubble detection and predictive regression. He has published his research in Journal of Financial Econometrics, Journal of Time Series Analysis, The Econometrics Journal, among others. James Duffy is an Associate Professor in Economics at the University of Oxford. His research is focused on strongly dependent time series, particularly with respect to: non-parametric estimation and inference; nonlinear generalizations of cointegration; and the robustness of inferences to varying levels of persistence. He is also interested in problems of identification and inference in structural macroeconomic models. His research has been published in the Annals of Statistics, the Journal of Econometrics, Econometric Theory, among others. Liudas Giraitis is Professor of Econometrics at Queen Mary, University of London. His research interests cover long memory and integrated I(d) models, non-parametric methods for time series models with time-varying parameters, ARCH modeling, asymptotic theory for dependent variables and their statistical and econometric applications. He has published his research in Annals of Statistics, Annals of Probability, Journal of Time Series Analysis, Econometric Theory, Journal of Econometrics, among others.

Proxying economic activity with daytime satellite imagery: Filling data gaps across time and space.

This paper develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. We demonstrate our measure's usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications.

Book Reviews

In Book Reviews, we review an extensive and diverse range of books. They cover theory and applications in operations research, statistics, management science, econometrics, mathematics, computers, and information systems. In addition, we include books in other fields that emphasize technical applications. The editor will be pleased to receive an email from those willing to review a book with an indication of specific areas of interest. If you are aware of a specific book that you would like to review or that you think should be reviewed, please contact the editor. The following books are reviewed in this issue of INFORMS Journal on Applied Analytics: The Spirit of Green: The Economics of Collisions and Contagions in a Crowded World (author: William D. Nordhaus; reviewer: Amitrajeet A. Batabyal) and Supply Chain Scheduling (authors: Zhi-Long Chen and Nicholas G. Hall; reviewer: Chelliah Sriskandarajah).

THE EFFECT OF SPECIAL AUTONOMY FUNDS, REGIONAL ORIGINAL REVENUE AND LOCAL GOVERNMENT EXPENDITURES ON WELFARE IN PAPUA PROVINCE

This study examines the effect of the Special Autonomy Fund, PAD and Government Expenditures on the Welfare of 19 Regencies/Cities in Papua Province with the Human Development Index as a reference for their welfare. This approach to research employs descriptive quantitative techniques based on correlation. Panel data from 2015-2021 and a cross-section of 19 regencies/cities in Papua Province gathered using document data collection techniques were used to compile the data for this study. To analyze the data in this study using multiple regression analysis with the help of the Econometric application Views (Eviews 10). The results of this study indicate that the Special Autonomy Fund variable significantly negatively affects the Human Development Index. Meanwhile, the Regional Original Income variable has a positive and insignificant effect on the Human Development Index. Next, the Regional Government Expenditure variable has a significant positive effect on the Human Development Index. Simultaneously, all variables positively and significantly affect the Human Development Index in Papua Province. For R2 in this study, the independent variable could explain the dependent variable of 97.27% and the remaining 2.73% was influenced by other factors

PENGARUH SUKU BUNGA, KURS, INFLASI, VOLUME TRANSAKSI, DAN PELANGGAN TERHADAP HARGA SAHAM

The Covid-19 pandemic has made many changes, especially in the world's economy and stock market conditions. So this study is intended to examine whether or not the influence of interest rates, exchange rates, inflation, transaction volume, and the number of customers on stock prices in operator companies in six countries with the most internet users in the world during pandemic. This research is quantitative research with a linear regression method which data collection is obtained through several pages needed in research. The collected data were then processed and tested through classical assumption tests (normality test, multicollinearity test, heteroscedasticity test, autocorrelation test) and hypothesis testing with linear regression analysis and coefficient of determination. The test in this research uses an econometric application program, E-views version 10. The results of this research showed that the exchange rate (X2) and transaction volume (X4) have no effect on stock prices (Y), and the interest rate variable (X1) has a negative effect and significant to the stock price (Y), while the inflation variable (X3) and the number of customers partially have a positive and significant effect on stock prices (Y). Simultaneously, the variable interest rate (X1), exchange rate (X2), inflation (X3), transaction volume (X4), and the number of customers (X5) affect stock prices (Y) by 97%, while the remaining 3% is affected by other variables outside the model adopted in this research.

Agricultural diversification, productivity, and food security across time and space

AbstractThis article presents an investigation of agricultural production risk over time and across space and its implications for food security. The econometric approach involves a Quantile Autoregressive (QAR) model and a copula to provide a flexible representation of the distribution of yield risk and its evolution over time and across space. The analysis relies on a two‐step estimation method to evaluate the multivariate yield distribution and its spatial and temporal evolution. Linkages between agricultural production risk and the economics of food security are explored, with implications for the welfare cost of food insecurity. The approach is illustrated in an econometric application to regional wheat and corn yields in Italy. The analysis provides new and useful information on the evolving linkages between agricultural production risk, productivity, and food security. Our integrated approach documents the role of regional diversification and of productivity growth along with their effects on food security.

On the Use of the Helmert Transformation, and its Applications in Panel Data Econometrics

AbstractWe revisit the Helmert transformation, and provide a useful and simple derivation of the joint distribution of the sample mean and the sample variance in samples from independently and identically distributed normal random variables. Our derivation is distinguished by concreteness, very little abstractness, and should be appealing to beginning students of statistics, and to both beginning and advanced students of econometrics. We also highlight one fruitful application of the Helmert transformation in panel data econometrics. The Helmert transformation can be used to eliminate the fixed effects in the estimation of fixed effects models, and we briefly review this application of the transformation in the panel data context.

The Effect of Socio-Economic Dimensions on Deforestation: Application of Spatial Econometrics

The Effect of Socio-Economic Dimensions on Deforestation: Application of Spatial Econometrics