Econometric Applications Research Articles

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 book is reviewed in this issue of INFORMS Journal on Applied Analytics, 52(3), May–June: Give Yourself a Nudge: Helping Smart People Make Smarter Personal and Business Decisions, Ralph Keeney.

Recent Advances of Dynamic Model Averaging Theory and Its Application in Econometrics

Dynamic Model Averaging (DMA) was first proposed by Raftery et al. (2010) when predicting the output strip thickness of cold rolling mills and is a recursive implementation of standard Bayesian model averaging, also known as recursive model averaging. Since Gary Koop and Dimitris Korobilis introduced DMA into the field of econometrics in 2012, dynamic model averaging has become a widely used estimation technique in macroeconomic applications because of its ability to adapt to the temporal change of parameters and the advantages of the specification of optimal prediction models, the method has good application prospects. This paper focuses on dynamic model averaging as a solution to model uncertainty problems, focusing on recent theoretical developments and their applications in econometrics. Discussions focused on uncertainties contained in covariates in regression models, such as normal linear regression and its extensions, and on advances in designing models to handle more challenging situations, such as time-dependent, spatially dependent, or endogenous data. The results show that the DMA method has good prediction accuracy, is a powerful tool for actual prediction, and provides important technical support for risk avoidance in management.

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 increasingly used 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 data on economic activity are unavailable for detailed regional levels and historical time series. This is especially true for areas of East Germany before reunification. Our procedure is generalizable to other settings, and yields great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications.

Financial Development and Economic Growth in BRICS-T Countries: An Econometric Application

In this study, the cointegration and causality relationships between economic growth and financial development were empirically analyzed using the annual data of BRICS-T countries for the period from 1991 to 2017. Access, depth, and effectiveness data, as well as the variables of Gross Fixed Capital Formation, labor force, and economic growth, representing the capital, are among the indicators representing IMF's financial development. In this empirical study, where three different models were used, the long-term relationship of capital, labor force, and one of the three indicators with economic growth was examined. In the results obtained, all of the three models were found to have cointegration relationships, and it was concluded that the variables would act together in the long term. In addition, causality relationships between access, depth and efficiency, and economic growth, which represent financial development, were also investigated in this empirical study. In the findings, it was found that economic growth was the causative of access; and that there was a two-way causality relationship between depth and economic growth, as well as between efficiency and economic growth. In light of these results, it was concluded that there was a two-way causality between financial development and economic growth.

Implicit Copulas: An Overview

Implicit copulas are the most common copula choice for modeling dependence in high dimensions. This broad class of copulas is introduced and surveyed, including elliptical copulas, skew t copulas, factor copulas, time series copulas and regression copulas. The common auxiliary representation of implicit copulas is outlined, and how this makes them both scalable and tractable for statistical modeling. Issues such as parameter identification, extended likelihoods for discrete or mixed data, parsimony in high dimensions, and simulation from the copula model are considered. Bayesian approaches to estimate the copula parameters, and predict from an implicit copula model, are outlined. Particular attention is given to implicit copula processes constructed from time series and regression models, which is at the forefront of current research. Two econometric applications—one from macroeconomic time series and the other from financial asset pricing—illustrate the advantages of implicit copula models.

COVID-19 PANDEMİSİNİN ULUSAL PARALAR VE FİNANSAL PİYASALAR ÜZERİNDEKİ ETKİLERİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÜZERİNE BİR ANALİZ

Coronavirus outbreak which started as an epidemic in Wuhan, China and soon transformed into a pandemic in the first quarter of 2020 is about to bring a profound stagnation to many national economies. In the study, to understand the effects of the covid-19 pandemic on national currency and financial markets from the perspective of developing and developing countries, daily data including the stock market closing prices, exchange rate and WTI gross oil prices of the effects of COVID-19 in the period of March 10, 2020 and May 9, 2020 for developing and developed economies were used. China, South Korea, Brazil, and Turkey are chosen to represent the developing world and Italy, France, Germany, Spain and England represent the developed world. Logarithms of all variables were taken and in the econometric application part, vector autoregression model was used. At the end of the study, it was determined that the number of Covid-19 cases did not affect exchange rates, but had an effect on stock prices in developing economies. As a result, It has been determined that developing economies affect more than developed economies from pandemic.

The Reform of Capstone Course of Financial Undergraduate Thesis based on the Application of Econometrics

In order to improve the quality of undergraduate thesis of the financial majors, the capstone course was introduced based on the application of Econometrics. In this paper the concept of capstone course was introduced and the reform of the undergraduate thesis based on the Capstone system was designed and implemented. A reasonable capstone system scheme according to the established curriculum objectives and the ability quality to be cultivated was designed, the whole process of the project implementation was managed. The teaching goal of process oriented and comprehensive ability training has been achieved and the teaching effect has been improved.

Satellite sensing night-time lights – A South African spatial econometric application

This study investigated how economic activity in one location may affect economic activities in adjacent locations. This study applies several statistical models to determine the economic activity effect of neighbouring locations within the South African context during the period 2012 to 2019, using data from satellite imagery. Decentralised gross domestic product (GDP) per location maps, i.e. to generate a gross domestic dataset at a very granular level was therefore developed. The level is a one km2 representation per location. The gross domestic product for each location is estimated using the night-time light remote sensing data derived from the Suomi National Polar Partnership (SNPP) infrared imaging satellite radiometer suite (VIIRS). This study illustrates how spatial correlations can be determined, and how to measure them using explanatory spatial data analysis. The essence of the spatial analysis revealed that “space does indeed matter”, as economic activity in one location is related to activity in neighbouring locations. Not only do spatial relationships exist, but economic activity seems to be fundamentally related to its own unique location.

Statistics and Genealogy: A Match Made in Heaven? The Forecasting and Inference Tool, an Application of the Central Limit Theorem in Genealogical Research

Applications of econometrics and statistics are widely used as tools in many disciplines as a means to provide quantitative analysis to investigate hypotheses. Examples include economics, sociology, and cliometrics. While so far in a genealogy such an approach has not been attempted, in this exploratory work we suggest some possibilities for the application of such quantitative tools to the subject. In doing so, we introduce and discuss the Forecasting and Inference Tool, an application of this framework, and a tool that can help genealogists to be more productive in their fieldwork by estimating years of death, marriage, and birth.

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, 51(4), July–August: Applications of Operations Research and Management Science for Military Decision Making, William P. Fox, Robert Burks; Behavioral Operational Research: A Capabilities Approach, Leroy White, Martin Kunc, Katharina Burger, Jonathan Malpass.

Exact simulation of Ornstein–Uhlenbeck tempered stable processes

AbstractThere are two types of tempered stable (TS) based Ornstein–Uhlenbeck (OU) processes: (i) the OU-TS process, the OU process driven by a TS subordinator, and (ii) the TS-OU process, the OU process with TS marginal law. They have various applications in financial engineering and econometrics. In the literature, only the second type under the stationary assumption has an exact simulation algorithm. In this paper we develop a unified approach to exactly simulate both types without the stationary assumption. It is mainly based on the distributional decomposition of stochastic processes with the aid of an acceptance–rejection scheme. As the inverse Gaussian distribution is an important special case of TS distribution, we also provide tailored algorithms for the corresponding OU processes. Numerical experiments and tests are reported to demonstrate the accuracy and effectiveness of our algorithms, and some further extensions are also discussed.

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, 51(3), May–June: Optimization and Control for Systems in the Big-Data Era: Theory and Applications, Tsan-Ming Choi, Jianjun Gao, James H. Lambert, Chi-Kong Ng, Jun Wang; Pricing Lives: Guideposts for a Safer Society, W. Kip Viscusi.

On Some Characterizations of Probability Distributions with Applications in Econometrics: A Centennial Tribute to CR Rao

CR Rao, the living legend in statistics, is celebrating his birth centenary year in 2020–21. One of the major areas in which Rao made significant contributions to statistics is characterization problems. The main objective of this paper is to connect the work of Rao and his followers on characterization to the underlying fundamental econometric problem. In doing so, the authors hope to draw the attention of the econometricians to the characterization method as a tool to further advance the less attempted problem of econometric modeling with errors in variables. The authors also hope that this paper would draw the attention of statisticians to the more interesting characterization problems arising in econometrics, there by providing fields of application to a class of characterization problems that otherwise remain mostly theoretical. Ragnar Frisch, the founder of econometrics, conceived the econometric model as a system of linear structural equations. Noting how one can use the reduced form equations to determine the joint distribution of the endogenous variables, given the distribution of the exogenous variables, Frisch asked the inverse question: under what distributional assumptions on the exogenous variables, does there exist a system of linear structural equations? Keeping this in the background, CR Rao answered that question in 1943 and 1947 using the characterization method and further advanced the characterization method in statistics. Rao and his followers, however, did not connect their work with the underlying econometric problem. The authors extend the econometric problem posed by Frisch to a two endogenous and two exogenous variables case and establish the existence of two linear structural equations and their identification, as an example of the potential benefits from this association between econometrics and statistics.

Construction of multiregion–multisector input–output tables: a spatial econometric approach for Poland

ABSTRACT This paper investigates the application of spatial econometrics in estimating the interregional flow matrix in multisector–multiregion input–output tables. With reasonable assumptions, it demonstrates that this application can be formulated as a modified multi-equation spatial Durbin model with missing parameters estimated via maximum likelihood. The statistical performance of the model is scrutinized and the method is illustrated using regional (NUTS-3-level) economic impact assessment simulations for Poland. The proposed non-survey method empirically built on sectoral–regional data on economic activity is widely replicable across countries with promising performance against widespread benchmarks.

Remote night-time lights sensing: Investigation and econometric application

Orientation: Some recent studies have been published that demonstrated the value of remote sensing night-time lights as descriptors and/or proxies for human activity. Research purpose: This article investigated the association between night-time light emissions and gross domestic product (GDP) estimates for South Africa. Motivation for the study: Satellite night-lights data seemed to be a useful proxy for economic activity at temporal and geographic scales for which traditional data are of poor quality, are unavailable or only available with a large time lag. Research approach/design and method: The article primarily used the remote sensing of night-time light emissions using satellite technologies. The methodology employed in this study involved estimating both a vector error correction modelling (VECM) and autoregressive distributed lag (ARDL) models that map light growth into a proxy for GDP growth. Main findings: Both the VECM and ARDL models confirmed a long-term co-integrating relationship between GDP (per capita) and night-time lights (total light intensity), a statistically significant short-term error correction term could, however, not be established through the VECM, but indeed through the ARDL model. Practical/managerial implications: The results of the study suggested that satellite remote sensing technologies held much promise and opportunities in terms of the field of Economics and Development. Contribution/value-add: This study contributes to our understanding of the spatial and temporal behaviour and trends in economic activity. It also suggested the use of satellite remote sensing technologies as part of official statistical frameworks and methodologies.

Assessment of the impact of export diversification on economic growth in Morocco: Econometric application

Resume Dans un contexte mondiale caracterise par l’instabilite et la fluctuation des recettes d’exportations, les taux de croissance economique enregistres recemment par le Royaume du Maroc mettent en question les dynamiques structurelles de l’economie marocaine. Une structure productive caracterisee essentiellement par une insuffisante diversification et un faible indice de sophistication des exportations. L’objectif principal de cet article est d’evaluer empiriquement l’impact de la diversification des exportations sur la croissance economique au Maroc a partir d’un modele autoregressive vectoriel (VAR). Les resultats de la presente etude montrent qu’une augmentation de 1% de la diversification des exportations marocaines influence positivement la croissance economique au Maroc de 0,27%. Au cours de cet article, nous avons presente, dans un premier temps, les outils de mesure de la diversification des exportations ainsi que notre approche methodologique, ce qui nous a permis, dans un deuxieme temps, d’analyser les resultats de notre etude econometrique. Mots cles : Economie, Structure, diversification, productivite, exportations, croissance Abstract In a global context marked by instability and fluctuating export revenues, the economic growth rates recorded recently by the Kingdom of Morocco call into question the structural dynamics of the Moroccan economy. The Moroccan productive structure is characterized mainly by insufficient diversification and a low index of export sophistication. The main objective of this paper is to empirically estimate the impact of export diversification on economic growth in Morocco using a vector auto regression model (VAR). The results of this study show that an increase in the diversification of Moroccan exports by 1% has a positive effect on economic growth in Morocco by 0.27%. In this paper, we first present the tools for measuring export diversification and our methodological approach, which allows us to analyze the results of our econometric study. Keywords: Economy, structure, diversification, productivity, exports, growth.

Extreme Conditional Quantiles for Panel Data Model with Individual Effects and Heteroscedastic Extremes

Panel quantile regression models play an important role in real applications of finance, econometrics, insurance and risk management. However, direct estimates of the extreme conditional quantiles may lead unstable results due to data sparsity on the tail regions. Moreover, the presence of individual effects complicates the inference for extreme quantiles and a study on their theoretical properties is necessary. This paper proposes a two-stage method to estimate/predict the extreme conditional quantiles where an intermediate quantile is first estimated according to panel regression models and the extrapolation of the intermediate quantile to an extreme quantile is carried out in the second stage. Under a set of second-order regular variation conditions of heteroscedastic extremes, we establish the asymptotic theories for the two-stage prediction while its finite sample performance is demonstrated and compared to the direct prediction by simulations. Finally, we apply the two-stage method to the macroeconomic and housing price data, and find strong evidence of housing bubbles and common economic factors as well as the cross country heterogeneity.

A robust-filtering method for noisy non-stationary multivariate time series with econometric applications

We investigate a new filtering method to estimate the hidden states of random variables for multiple non-stationary time series data. This helps in analyzing small sample non-stationary macro-economic time series in particular and it is based on the frequency domain application of the separating information maximum likelihood (SIML) method, developed by Kunitomo et al. (Separating Information Maximum Likelihood Estimation for High Frequency Financial Data. Springer, New York, 2018), and Kunitomo et al. (Japan J Statistics Data Sci 2:73–101, 2020), and Nishimura et al. (Asic-Pacific Financial Markets, 2019). We solve the filtering problem of hidden random variables of trend-cycle, seasonal and measurement-errors components, and propose a method to handle macro-economic time series. We develop the asymptotic theory based on the frequency domain analysis for non-stationary time series. We illustrate applications, including some properties of the method of Müller and Watson (Econometrica 86-3:775–804, 2018), and analyses of some macro-economic data in Japan.

Analysis of econometric research hotspots and application prospects —— visualization research based on citespace

By analyzing scholars’ research on econometrics, we can understand the hot topics of related research and, based on the changes of research hotspots in different years, we can derive the future application prospects. In this paper, the literature related to econometrics in cnki and web of science is selected as the sample source, and the literature between 2010 and 2020 is analyzed by the visualization software citespace for authors, keywords, etc. Through the study, it is found that the application of spatial econometrics and the solution to practical problems are the focus of current scholars’ research, and the application of econometrics is very wide, and the solution to practical problems is the new trend of econometric research in the future.

Моделирование взаимодействия регионов России и Республики Беларусь в целях прогнозирования дополнительных транспортных потоков

The article proposes a methodological approach using the spatial econometrics tool for development of transport and economic forecasting systems and reflection of major structural political decisions in its results on the example of the influence of economic integration of Russia and Belarus on transport flows in the intersectoral interterritorial systems of the Union State. To calculate the studied indicators, data from the official statistical websites of Russia and Belarus were used. The analysis of the spatial distribution of output in the processing sector of the regions of the Russian Federation and the Republic of Belarus is carried out to assess the possibility of interaction between these regions in this sector of the economy. When modeling, the Republic of Belarus is considered as a separate region within the framework of the Union State. Calculations of the global and local Moran indices were made and possible spatial autocorrelations were determined both between the subjects of the Russian Federation and between the regions of the Russian Federation and the Republic of Belarus. In the current study, a standard inverse distance weight matrix was used to assess the degree of interaction between regions. The problem is considered, first of all, from the transport side on the basis of the analysis of relations in heavy industry as the main cargo-forming sphere. The influence of economic and infrastructural factors on the indicator characterizing the degree of possible interaction between the regions of the Russian Federation and the Republic of Belarus in the field of industry and transport is studied. The results of the work make a step towards the systematic application of spatial econometrics in methodology and technology of transport and economic forecasting and can be used in preparation of strategies, programs and schemes for placement and development of industries, taking into account the potential of a new level of integration of the economies of Russia and Belarus.