Strong Cross-sectional Dependence Research Articles

지역 합계출산율에 영향을 미치는 요인 분석

Purpose - Low fertility rate is a serious problem, and this study analyzes factors affecting total fertility rate using panel data from 16 metropolitan cities and provinces in Korea from 2000 to 2022. Design/methodology/approach - Estimating the SAR model considering the weak cross-sectional dependence that exists in variables related to the regional total fertility rate, and using the DKSE estimation method considering the strong cross-sectional dependence. Findings - Estimation results considering weak and strong cross-sectional dependence were similar, confirming the robustness of the results. Female labor force participation rate has a positive effect on total fertility rate, and employment rate has no effect. However, the interaction term is a negative (-) sign. Crude marriage rate has a positive effect on total fertility rate, and apartment price has a slightly positive effect. Environmental factor has no effect, and policy factor has a negative effect. Research implications or Originality - In order for an increase in the female labor force participation rate to lead to an increase in the total fertility rate, qualitative improvements in female employment must be made. Financial investment policies for childbirth must increase their effectiveness. The problem of low fertility rate requires not only population policy but also social, economic, cultural, environmental, and policy conditions to be considered.

Equal predictive ability tests based on panel data with applications to OECD and IMF forecasts

We propose two types of equal predictive ability (EPA) tests with panels to compare the predictions made by two forecasters. The first type, S-statistics, focuses on the overall EPA hypothesis, which states that the EPA holds, on average, over all panel units and over time. The second type, C-statistics, focuses on the clustered EPA hypothesis where the EPA holds jointly for a fixed number of clusters of panel units. The asymptotic properties of the proposed tests are evaluated under weak and strong cross-sectional dependence. An extensive Monte Carlo simulation shows that the proposed tests have very good finite sample properties, even with little information about the cross-sectional dependence in the data. The proposed framework is applied to compare the economic growth forecasts of the OECD and the IMF, and to evaluate the performance of the consumer price inflation forecasts of the IMF.

Measuring financial soundness around the world: A machine learning approach

We use a fully data-driven approach and information provided by the IMF’s financial soundness indicators to measure the condition of a country’s financial system around the world. Given the nature of the measurement problem, we apply different versions of principal component analysis (PCA) to deal with the presence of strong cross-sectional and time dependence in the data due to unobserved common factors. Using this comprehensive sample and various statistical methods, we produce an alternative data-driven measure of financial soundness that provides policy makers and financial institutions with a monitoring and policy tool that is easy to implement and update. We validate our index by using alternative macroeconomic factors, confirming its predictive power. Our index captures important aspects of financial intermediation around the world.

News-implied linkages and local dependency in the equity market

This paper studies a heterogeneous coefficient spatial factor model that separately addresses both common factor risks (strong cross-sectional dependence) and local dependency (weak cross-sectional dependence) in equity returns. From the asset pricing perspective, we derive the theoretical implications of no asymptotic arbitrage for the heterogeneous spatial factor model, generalizing the work of Kou et al. (2018). We also provide the associated Wald tests for the APT restrictions in the general case when there are both traded and non-traded factors. On the empirical side, it is challenging to measure granular firm-to-firm connectivity for a high-dimensional panel of equity returns. We use extensive business news to construct firms’ links through which local shocks transmit, and we use those news-implied linkages as a proxy for the connectivity among firms. Empirically, we document a considerable degree of local dependency among S&P500 stocks, and the spatial component does a great job in capturing the remaining correlations in the de-factored returns. We find that adding spatial interaction terms to factor models reduces mispricing and boosts model fitting. By comparing the performance of the model estimated using different networks, we show that the news-implied linkages provide a comprehensive and integrated proxy for firm-to-firm connectivity.

High-dimensional VARs with common factors

This paper studies high-dimensional vector autoregressions (VARs) augmented with common factors that allow for strong cross-sectional dependence. Models of this type provide a convenient mechanism for accommodating the interconnectedness and temporal co-variability that are often present in large dimensional systems. We propose an ℓ1-nuclear-norm regularized estimator and derive the non-asymptotic upper bounds for the estimation errors as well as large sample asymptotics for the estimates. A singular value thresholding procedure is used to determine the correct number of factors with probability approaching one. Both the LASSO estimator and the conservative LASSO estimator are employed to improve estimation precision. The conservative LASSO estimates of the non-zero coefficients are shown to be asymptotically equivalent to the oracle least squares estimates. Simulations demonstrate that our estimators perform reasonably well in finite samples given the complex high-dimensional nature of the model. In an empirical illustration we apply the methodology to explore dynamic connectedness in the volatilities of financial asset prices and the transmission of ‘investor fear’. The findings reveal that a large proportion of connectedness is due to the common factors. Conditional on the presence of these common factors, the results still document remarkable connectedness due to the interactions between the individual variables, thereby supporting a common factor augmented VAR specification.

A data-driven approach to measuring epidemiological susceptibility risk around the world

Epidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010–2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

Pollution concern during globalization mode in financially resource-rich countries: Do financial development, natural resources, and renewable energy consumption matter?

The question still remains whether financial development, natural resources, and energy consumption can successfully alleviate environmental pollution during the mode of globalization. In this regard, this study presents empirical evidence of supporting this theoretical argument, exploring the influence of financial development, natural resources, globalization, non-renewable and renewable energy consumption on the ecological footprint in financially resource-rich countries from 1990 to 2018. The empirical findings confirm the strong cross-sectional dependence across cross-sections. Therefore, the second-generation panel data approach is applied to produce more robust and reliable results. The results explore that financial development, natural resources, and non-renewable energy positively affect the ecological footprint, while globalization and renewable energy reduce the ecological footprint in these countries. Similarly, the interaction between financial development integration with globalization and renewable energy usage with natural resources is confirmed to reduce environmental deterioration. Additionally, bidirectional causality discovers between financial development, non-renewable energy, renewable energy, and ecological footprint; however, unidirectional causality is significantly running from natural resources towards ecological footprint and ecological footprint towards globalization. Therefore, these countries require improving financial and natural resource structures and further upgrading of renewable energy consumption. Moreover, globalization plays a vital role in reducing ecological deterioration and requires instantaneous policy reactions in these countries.

The effect of technological innovation, FDI, and financial development on CO2 emission: evidence from the G8 countries.

The nexus of foreign direct investment and economic growth has been extensively investigated by the researchers of environmental economics; however, few studies have been conducted to find the impact of financial development and technological innovation in the backdrop of the environment. In the G8 countries (UK, USA, Canada, Germany, France, Italy Russia, Japan), the rapid increase in urbanization resulting from their speedy economic growth has brought about a huge increase in energy consumption that is in turn responsible for contemporary environmental degradation. This research intends to find the impact of technological innovation, financial development, foreign direct investment, energy use, and urbanization on carbon emission in G8 member countries, based on data from 1990 to 2019. The findings present strong cross-sectional dependence within the panel countries. According to the FMLOS estimator, a statistically significant long-run and negative association with CO2 has been found between foreign direct investment, financial development, and technological innovation in G8 countries. A long-run bidirectional causality has been found among economic growth, financial development, urbanization, trade openness, CO2 emission, and energy use; antithetically there is unidirectional causality between carbon emission and foreign direct investment. A quality foreign direct investment is the present demand for the development of industries, technological innovation, and financial development for G8 countries. Furthermore, urbanization plays a major role in environmental degradation, and more improved policies are needed for these countries.

Detecting common breaks in the means of high dimensional cross-dependent panels

Summary The problem of detecting change points in the mean of high dimensional panel data with potentially strong cross-sectional dependence is considered. Under the assumption that the cross-sectional dependence is captured by an unknown number of common factors, a new CUSUM-type statistic is proposed. We derive its asymptotic properties under three scenarios depending on to what extent the common factors are asymptotically dominant. With panel data consisting of N cross sectional time series of length T, the asymptotic results hold under the mild assumption that $\min \lbrace N,T\rbrace \rightarrow \infty$, with an otherwise arbitrary relationship between N and T, allowing the results to apply to most panel data examples. Bootstrap procedures are proposed to approximate the sampling distribution of the test statistics. A Monte Carlo simulation study showed that our test outperforms several other existing tests in finite samples in a number of cases, particularly when N is much larger than T. The practical application of the proposed results are demonstrated with real data applications to detecting and estimating change points in the high dimensional FRED-MD macroeconomic data set.

Estimating change-point latent factor models for high-dimensional time series

We consider estimating a factor model for high-dimensional time series that contains structural breaks in the factor loading space at unknown time points. We first study the case when there is one change point in factor loadings, and propose a consistent estimator for the structural break location, whose convergence rate is shown to depend on an interplay between the dimension of the observed time series and the strength of the underlying factor structure. Our results reveal that the asymptotic behavior of the proposed estimator can be asymmetric in the sense that a larger estimation error can occur toward the regime with weaker factor strength. Based on the proposed estimator for the structural break location, we also consider the problem of estimating the factor loading spaces before and after the structural break. We show that the proposed estimators for change-point location and loading spaces are consistent when the numbers of factors are correctly estimated or overestimated. The algorithm for multiple change-point detection is also developed in the paper. Compared with existing results on change-point factor analyses of high-dimensional time series, a distinguished feature of the current paper is that the noise process is not necessarily assumed to be idiosyncratic and as a result we allow the noise process with potentially strong cross-sectional dependence. Another advantage for the proposed method is that it is specifically designed for the changes in the factor loading space and the stationarity assumption is not imposed on either the factor or noise process, while most existing methods for change-point detection of high-dimensional time series with/without a factor structure require the data to be stationary or ’close’ to a stationary process between two change points, which is rather restrictive. Numerical experiments including a Monte Carlo simulation and a real data application are presented to illustrate the proposed estimators perform well.

Factor models with many assets: Strong factors, weak factors, and the two-pass procedure

This paper re-examines the problem of estimating risk premia in unconditional linear factor pricing models. Typically, the data used in the empirical literature are characterized by weakness of some pricing factors, strong cross-sectional dependence in the errors, and (moderately) high cross-sectional dimensionality. Using an asymptotic framework where the number of assets/portfolios grows with the time span of the data while the risk exposures of weak factors are local-to-zero, we show that the conventional two-pass estimation procedure delivers inconsistent estimates of the risk premia. We propose a new estimation procedure based on sample-splitting instrumental variables regression. The proposed estimator of risk premia is robust to weak included factors and to the presence of strong unaccounted cross-sectional error dependence. We prove the consistency of the new estimator, establish asymptotically valid inferences using Wald statistics, verify performance of the new procedure in simulations, and revisit some empirical studies.

Meteorological factors, COVID-19 cases, and deaths in top 10 most affected countries: an econometric investigation.

This paper examines the nexus between the Covid-19 confirmed cases, deaths, meteorological factors, including an air pollutant among the world’s top 10 infected countries, from 1 February 2020 through 30 June 2020, using advanced econometric techniques to address heterogeneity across the nations. The findings of the study suggest that there exists a strong cross-sectional dependence between Covid-19 cases, deaths, and all the meteorological factors for the countries under study. The findings also reveal that a long-term relationship exists between all the meteorological factors. There exists a bi-directional causality running between the Covid-19 cases and all the meteorological factors. With Covid-19 death cases as the dependent variable, there exists bi-directional causality running between the Covid-19 death cases and Covid-19 confirmed cases, air pressure, humidity, and temperature. Temperature and air pressure exhibit a statistically significant and negative impact on the Covid-19 confirmed cases. Air pollutant PM2.5 also exhibits a significant but positive impact on the Covid-19 confirmed cases. Temperature indicates a statistically significant and negative impact on the Covid-19 death cases. At the same time, Covid-19 confirmed cases and air pollutant PM2.5 exhibit a statistically significant and positive impact on the Covid-19 death cases across the ten countries under study. Hence, it is possible to postulate that cool and dry weather conditions with lower temperatures may promote indoor activities and human gatherings (assembling), leading to virus transmission. This study contributes both practically and theoretically to the concerned field of pandemic management. Our results assist in taking appropriate measures in implementing intersectoral policies and actions as necessary in a timely and efficient manner.Graphical abstract Causal relations of Meteorological factors and Covid-19 (2 models used in the study)Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-12668-5.

Econometric analysis of COVID-19 cases, deaths, and meteorological factors in South Asia.

The pandemic has affected almost 74 million people worldwide as of 17 December 2020. This is the first study that attempts to examine the nexus between the confirmed COVID-19 cases, deaths, meteorological factors, and the air pollutant namely PM2.5 in six South Asian countries, from 1 March 2020 to 30 June 2020, using the advanced econometric techniques that are robust to heterogeneity across nations. Our findings confirm (1) a strong cross-sectional dependence and significant correlation between COVID-19 cases, deaths, meteorological factors, and air pollutant; (2) long-term relationship between all the meteorological variables, air pollutant, and COVID-19 death cases; (3) temperature, air pressure, and humidity exhibit a significant impact on the COVID-19 confirmed cases, while COVID-19 confirmed cases and air pollutant PM2.5 have a statistically significant impact on the COVID-19 death cases. In this way, the conclusion that high temperature and high humidity increase the transmission of the COVID-19 infections can also be applied to the regions with greater transmission rates, where the minimum temperature is mostly over 21 °C and humidity ranges around 80% for months. From the findings, it is evident that majority of the meteorological factors and air pollutant PM2.5 exhibit significant negative and positive effects on the number of COVID-19 confirmed cases and death cases in the six countries under study. Air pollutant PM 2.5 provides more particle surface for the virus to stick and get transported longer distances. Hence, higher particulate pollution levels in the air increase COVID-19 transmission in these six South Asian countries. This information is vital for the government and public health authorities in formulating relevant policies. The study contributes both practically and theoretically to the concerned field of pandemic management.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-12613-6.

News-Implied Linkages and Local Dependency in the Equity Market

This paper studies a heterogeneous coefficient spatial factor model that separately addresses both common factor risks (strong cross-sectional dependence) and local dependency (weak cross-sectional dependence) in the equity returns. From the asset pricing perspective, we derive the theoretical implications of no asymptotic arbitrage for the heterogeneous spatial factor model. In empirical work, it is challenging to measure granular firm-to-firm connectivity for a high-dimensional panel of equity returns. We use extensive business news to construct firms’ links via which local shocks transmit, and we use those news-implied linkages as a proxy for the connectivity among firms. Empirically, we document a considerable degree of local dependency among S&P 500 stocks, and the spatial component does a great job in capturing the remaining correlations in the de-factored returns. We find that adding spatial interactions to factor models reduces mispricing and mean-squared errors. We also show that our news-implied linkages provide a comprehensive and integrated proxy for firm-to-firm connectivity, and it out-performs other existing networks in the literature.

Environmental expenditure interactions among OECD countries, 1995–2017

How do countries respond to other countries when setting the level of their environmental expenditures? Using data from 1995 to 2017 on a sample of 28 OECD countries, we examine the nature and extent of strategic interactions in environmental expenditures among OECD countries using a spatial Durbin model including economic and political control variables and both economic and spatial weight matrices reflecting several interaction mechanisms. The results show the existence of significant positive spatial dependence in environmental spending suggesting that OECD countries consider their neighbors’ behavior when making policy choices related to environmental expenditures. We also find that the most populous countries or those with high unemployment tend to spend less for environment while countries with a large urban population set higher levels of environmental expenditures. The results are robust to the inclusion of strong cross-sectional dependence under the form of common factors.

Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure

Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-section dependence through unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated effects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach needs to be modified in order to account for the hierarchical factor structure in 3D panels. Further, we provide asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is confirmed by Monte Carlo simulation studies. We demonstrate the empirical usefulness of the proposed framework through an application to a 3D panel gravity model of bilateral export flows.

The effects of FDI, technological innovation, and financial development on CO2 emissions: evidence from the BRICS countries.

The scholars of environmental economics have attempted the investigation of the impact of foreign direct investment-growth nexus, but they have missed the essential role played by technological innovation and financial development regarding the environmental costs. The notable economic growth and the consequent speedy process of urbanization in BRICS countries have brought about colossal escalation of energy needs leading to environmental degradation. The present study endeavors to explore the effect of foreign direct investment, technological innovation, and financial development on carbon emissions in BRICS member countries, with data from 1990 to 2017. The results verify a strong cross-sectional dependence within the panel countries. The Augmented Mean Group (AMG) estimator shows that foreign direct investment, technological innovation, and financial development in the BRICS countries possess a negative and statistically significant long-run association with CO2 emissions, while economic growth, trade openness, urbanization, and energy use are found to contribute statistically significant and positive with carbon emissions. The current study chose to employ the Dumitrescu and Hurlin panel causality test for examining the direction of causality. Findings reveal a bidirectional long-run causality running among financial development, economic growth, trade openness, urbanization, energy use, and CO2 emissions; on the contrary, unidirectional causality is found between foreign direct investment and carbon emissions. Consequently, for the BRICS member countries, the development of industries, financial institutions, and development of technological innovation are required to attract quality foreign direct investment. Moreover, urbanization contributes enormously to environmental degradation and necessitates urgent policy responses in these countries.

An alternative semiparametric model for spatial panel data

We propose a semiparametric P-Spline model to deal with spatial panel data. This model includes a non-parametric spatio-temporal trend, a spatial lag of the dependent variable, and a time series autoregressive noise. Specifically, we consider a spatio-temporal ANOVA model, disaggregating the trend into spatial and temporal main effects, as well as second- and third-order interactions between them. Algorithms based on spatial anisotropic penalties are used to estimate all the parameters in a closed form without the need for multidimensional optimization. Monte Carlo simulations and an empirical analysis of regional unemployment in Italy show that our model represents a valid alternative to parametric methods aimed at disentangling strong and weak cross-sectional dependence when both spatial and temporal heterogeneity are smoothly distributed.

Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure

Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-sectional dependence through unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated effects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach needs to be modified in order to account for the hierarchical factor structure in 3D panels. Further, we provide the associated asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is confirmed by Monte Carlo simulation studies. We also demonstrate the empirical usefulness of the proposed approach through an application to a 3D panel gravity model of bilateral export flows.

What drives environmental degradation? Evidence from 14 Sub-Saharan African countries

Although Sub-Saharan Africa (SSA) countries make less environmental pollution compared with other regions, the notably increasing economic growth and accelerating process of urbanization have resulted in the increase of energy needs and, thus, lead to environmental degradation. To empirically investigate the determinants of environmental degradation by accounting for the significant roles played by economic growth, non-renewable and renewable energy consumption, and urbanization, a balanced panel dataset of 14 SSA countries over 1990–2014 is utilized. Also, the ecological footprint (EF), considered a more comprehensive indicator, is used as a proxy of environmental degradation. The results confirm strong cross-sectional dependence within the SSA countries. The Augmented Mean Group (AMG) estimator indicates that economic growth, non-renewable energy consumption, and urbanization exert positive effects on the EF in the SSA countries, while renewable energy consumption plays a negative role in the EF. Moreover, bidirectional long-run causality runs among economic growth, non-renewable consumption, urbanization, and the EF; in contrast, unidirectional causality is found to run from renewable energy consumption to the EF. Therefore, for the SSA countries, the upgrading of industrial structure and further improvement of renewable energy are needed. In addition, urbanization plays a crucial role in contributing to environmental degradation and requires immediate policy response in the SSA countries.