Classical Panel Research Articles

Does Income Tax Competition Remain Relevant In Attracting FDI? The Case of ASEAN Countries

This study evaluates the impact of corporate income tax on foreign direct investment (FDI) in 6 Southeast Asian countries from 1999 to 2022, under the context of the preparation for the Global minimum tax application in many countries in and outside the region. Using classical panel data models (Fixed Effects and Random Effects model), the findings show no significant impact of corporate income tax on FDI inflows to ASEAN nations. Observing the effects of other factors on foreign investments, the study discovers the significant role of infrastructure in attracting FDI for the entire sample. Additionally, we find significant positive impacts of trade openness and significant negative impacts of political stability on FDI inflows for the developing nations in the area. Interestingly, we find positive relationships between economic shocks and FDI and the region's quick recovery of foreign investment flows. Based on the results, we provide several valuable academic and policy suggestions.

Consistent estimation of panel data sample selection models

The properties of classical panel data estimators including fixed effect, first-differences, random effects, and generalized method of moments-instrumental variables estimators in both static as well as dynamic panel data models are investigated under sample selection. The correlation of the unobserved errors is shown not to be sufficient for the inconsistency of these estimators. A necessary condition for this to arise is the presence of common (and/or non-independent) non-deterministic covariates in the selection and outcome equations. When both equations do not have covariates in common and independent of each other, the fixed effects, and random effects estimators in static models with exogenous covariates are consistent. Furthermore, the first-differenced generalized method of moments estimator uncorrected for sample selection as well as the instrumental variables estimator uncorrected for sample selection are both consistent for autoregressive models even with endogenous covariates. The same results hold when both equations have no covariates in common but are correlated once we account for such correlation. Under the same circumstances, the system generalized method of moments estimator adding more moments from the levels equation has moderate bias. Alternatively, when both equations have common covariates the appropriate correction method is suggested. Serial correlation of the errors being a key determinant for that choice. The finite sample properties of the proposed estimators are evaluated using a Monte Carlo study. Two empirical illustrations are provided.

Spatio-temporal modelling of housing satisfaction indicators based on statistical data – evidence from Poland

Motives: The research problem addressed in the paper is the identification of sources of the spatial differentiation of housing satisfaction under the influence of external factors (economic, demographic and social).Aim: The main research objective was to identify the determinants and spatial differentiation of housing satisfaction in Poland. Both classical panel models and spatial panel models were used for the analyses.Results: The results of the study confirmed the hypothesis that the inclusion of spatial dependencies significantly improved the quality of the panel model. The developed models supported the determination of statistical relationships between economic, demographic and social factors, the number of dwellings per 1000 inhabitants, and the average floor space per person. In addition, the spatial distribution of individual effects in spatial panel models revealed variation in housing satisfaction that resulted from different levels of socio-economic development.

Mechanism and Spatial Spillover Effect of New-Type Urbanization on Urban CO2 Emissions: Evidence from 250 Cities in China

Exploring the effect of new-type urbanization (NTU) on urban carbon abatement is of great practical significance for promoting urban green construction and coping with the challenge of global climate change. This study used data from 250 cities in China from 2008 to 2020 and constructed the NTU evaluation indicator system from five dimensions. We used classical panel regression models to examine the effects of NTU on urban CO2 emissions, and further used spatial econometric models of SEM, SAR, and SDM to identify the spatial spillover effects of NTU on urban CO2 emissions. The main results are that China’s NTU and CO2 emissions are generally rising, and NTU has a significantly negative effect on urban CO2 emissions, with an impact coefficient of −0.9339; the conclusions still hold after subsequent robustness tests. Heterogeneity analysis reveals that NTU’s carbon abatement effect is more pronounced in resource-based cities, old industrial areas, and cities with lower urbanization levels and higher innovation levels. Mechanism analysis shows that improving urban technological innovation and optimizing resource allocation are important paths for realizing urban CO2 emission reduction. NTU’s effect on urban CO2 emissions has a noticeable spatial spillover. Our findings provide policy makers with solid support for driving high-quality urban development and dual-carbon targets.

A Method for Predicting the Aeroelastic Response of Radial Turbomachines

AbstractAircraft intermittent combustion engines often incorporate turbochargers adapted from ground-based applications to improve their efficiency and performance. These turbochargers can operate at off-design conditions and experience blade failures brought on by aerodynamic-induced blade resonances. A reduced-order model of the aeroelastic response of general fluid–structural configurations is developed using the Euler–Lagrange equation informed by numerical data from uncoupled computational fluid dynamic (CFD) and computational structural dynamic calculations. The structural response is derived from a method of assumed-modes approach. The unsteady fluid response is described by a modified version of piston theory that approximates the local transient pressure fluctuation in conjunction with steady CFD solution data. The reduced-order model is first applied to a classical panel flutter scenario and found to predict a flutter boundary that compares favorably to the boundary identified by existing theory and experimental data. The model is then applied to the high-pressure turbine of a dual-stage turbocharger. The model predictions are shown to reliably determine the lack of turbine blade flutter and rudimentary damping comparisons are performed to assess the ability of the model to ascertain the susceptibility of the turbine to forced response. Obstacles associated with the current experimental state of the art that impinge upon further numerical validation are discussed.

Panel threshold model with covariate-dependent thresholds and its application to the cash flow/investment relationship

Abstract This paper introduces a panel threshold model with covariate-dependent and time-varying thresholds (PTCT), which extends the classical panel threshold model of Hansen, B. E. 1999. “Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference.” Journal of Econometrics 93: 345–68 to a framework with multiple covariate-dependent and time-varying thresholds. Based on the within-group transformation and Markov chain Monte Carlo (MCMC) technique, we develop methods for estimation and inference for threshold parameters in the proposed panel threshold model. We also suggest test statistics for threshold effect, threshold constancy, and for determining the number of thresholds. Monte Carlo simulations indicate that the estimation, inference and testing procedures work well in finite samples. Empirically, using the same data as in Hansen, B. E. 1999. “Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference.” Journal of Econometrics 93: 345–68 we revisit the cash flow/investment relationship and find quite different results.

Racial inequality in frictional labor markets: Evidence from minimum wages

We provide the first causal analysis of how state and federal minimum wage policies in the U.S. have affected labor market frictions and racial wage gaps. Using stacked event studies, binned difference-in-differences estimators, within-person analyses and classic panel methods, we find that minimum wages increased wages of black workers between 16 and 64% more than among white workers and reduced the overall black-white wage gap by 10% (and by 56% among workers most affected by the policies). Racial differences in initial wages cannot explain this differential effect. Rather, minimum wages expand job opportunities for black workers more than for white workers. We present a model with labor market frictions in which minimum wages expand the job search radius of workers who do not own automobiles and who live farther from jobs. Our causal results using the ACS show that minimum wages increase commuting via automobile among black workers but not among white workers, supporting our model. Minimum wages also reduce racial gaps in separations and hires, further suggesting the policies especially enhance job opportunities for black workers.

MODELING REGRESSION ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (RANFIS) FOR PANEL DATA

Panel data combine cross-sectional and time-series data. Data on economic, business, social, and development issues are often presented in panel data. In constructing the panel data regression model, it is necessary to take various steps for testing the model specifications, including the Chow test and the Hausman test. This study constructed a classical panel data regression model and the regression adaptive neuro-fuzzy inference system (RANFIS). The RANFIS model is a regression model by applying fuzzy and neural network (NN) techniques expected to overcome the problem of uncertainty. One of the main problems in constructing an optimal RANFIS is selecting input variables. The input variables of RANFIS are selected on the basis of the best classical regression. These inputs are classified into optimal clusters, which depend on the degree of fuzzy membership functions. The rule bases of RANFIS are determined on the basis of optimal inputs and its clusters. The empirical study in this research is to construct a panel data regression model for the Human Development Index (HDI) in Central Java in 2017-2019. HDI depends on several variables such as the school participation rate, number of health workers, public health complaints, population growth rate, and poverty severity index as predictor variables. Based on classical regression, three variables were used as optimal inputs for RANFIS modeling. Evaluation of model performance was measured based on the RMSE and MAPE values. Based on the RANFIS, the values of RMSE and MAPE were 3.227 and 3.299, respectively. Keywords: Panel Data Regression, Human Development Index, Regression Adaptive Neuro-Fuzzy Inference System DOI： https://doi.org/10.35741/issn.0258-2724.58.3.57

Political patronage and banks’ leverage in the Middle Eastern and North African region: A new neural panel regression analysis

This research examines the effect of political patronage on financing decisions for a sample of 67 commercial banks from the Middle Eastern and North African region for the period 2003–2017. For this purpose, a fixed-effects neural panel model was developed to investigate this relationship, which has not been done in previous research. In contrast to classical panel regression, our model allows for the detection of all possible combinations among the variables. Although scant evidence on a direct impact of the political connection exists, the results reveal that the effect of political patronage on leverage is non-linear through its interaction with bank profitability and efficiency. The non-linear effect is also related to the political and institutional environments.

More general panel data models for hospitality and tourism research

PurposeThis paper introduces more advanced panel data specifications that would exploit heterogeneity and allow for arbitrary forms of autocorrelation and heteroskedasticity in the error terms.Design/methodology/approachIn line with Assaf and Tsionas (2019a, 2019b), this paper builds on the Mundlak device to propose panel data models to allow for random slope coefficients, as well as time slope coefficients. This paper allows for arbitrary heteroskedasticity and autocorrelation, thus mitigating possible model misspecification. This paper develops and estimates the model in a Bayesian framework. This paper’s methods can be generalized to many nonlinear models including limited dependent variable models.FindingsThis paper compares several competing models such as a classical panel data model, which has only firm effects. This paper also examines the role of standard deviations in the formation of firm effects and time effects in the Mundlak device. This paper clearly shows that our framework introduces the best flexibility and model fit.Research limitations/implicationsThis paper illustrates the importance of using more flexible models (i.e. unit-specific and time-varying coefficients) for future estimation of panel data in the field.Originality/valueThis paper discusses techniques that will improve panel data estimation in the hospitality and tourism literature.

Analysis of genomic alterations in pan-cancer from a large real-world Chinese population.

10568 Background: Effective targeted therapy sorely depends on comprehensive and precise genomic profiling. Besides, single nucleotide variations (SNVs) and InDels, gene fusions, as drivers and therapeutic targets of great importance, are not yet well characterized in Chinese patients. Methods: Formalin-fixed, paraffin-embedded (FFPE) tumor tissues from 1,384 pan-cancer patients were collected and sequenced using next-generation sequencing (NGS) targeting 40 cancer genes including SNV, fusion and assessing copy number variation (CNV) (AmoyDx HANDLE Classic Panel). qPCR and fluorescence in situ hybridization (FISH) were further applied to verify NGS detected fusion genes. Results: 1,384 patients were recruited including 890 lung cancers, 272 colorectal cancers, 174 gastric cancers, and 48 endometrial cancers. The prevalence of fusion genes (5.49%), including ALK-fusion (2.67%), ROS1-fusion (0.94%), RET-fusion (0.72%), NRG1-fusion (0.29%), and NTRK-fusion (0.07%), was nearly double the frequency of previously reported data from East Asians. Prevalence of fusion genes varied in different types of cancers. For instance, ALK (3.7%), ROS1 (1.5%), RET (1.0%) and NRG1 (0.2%) fusions were largely found in patients with non-small cell lung cancer (NSCLC), and were rarely detected in other cancer types. Further analysis of genomic alterations in fusion-positive patients revealed that, TP53 (21%) was the most frequently co-occurred mutated gene with ALK fusion, while RET fusions and ROS1 fusions were rarely accompanied by other mutated genes. In addition, two patients with RBPMS- NRG1 fusion were accompanied by BRAF and RB1 mutations, whereas no co-occurred mutation was found in patients with other partners of NRG1. Patients with inconsistent results of RNA-based NGS and FISH validation fusions are still being followed for treatment and survival, updated data will be available at the time of the presentation. Conclusions: Genomic alterations in real-world Chinese populations have specific characteristics, especially fusion genes. Further characterization of these variants is essential for clinics to guide appropriate targeted therapies.

Drag Effect of Economic Growth and Its Spatial Differences under the Constraints of Resources and Environment: Empirical Findings from China's Yellow River Basin.

As the global economic development intensifies the plunder of resources and the environment, the constraints are becoming more and more obvious. Based on the background of the strategy for ecological conservation and high-quality development of the Yellow River Basin, this paper intends to construct a resource-environment-constrained economic growth drag effect model and a spatial Dubin model, and explore the economic growth drag effect and its spatial differences in the Yellow River Basin under the constraints of resources and environment. The study found that the total drag effects of the overall economic growth of the Yellow River Basin that were obtained by the classic panel model without spatial effects is significantly negative. This is consistent with the conclusion that the average total drag effects of 80 prefecture-level cities is negative. The total drag effects of the overall economic growth of the Yellow River Basin changes from unconstrained to medium-constrained after adding spatial constraints, indicating that the spatial correlation of factors will restrict economic growth. From the level of the Yellow River sub-catchment, the total drag effect of the direct effects of the upper, middle, and lower reaches of the Yellow River is consistent with the total drag effect of the total effect. It shows that the upper economic growth is strongly constrained by the local resources and environment, while the downstream is strongly constrained by the adjacent resources and the environment. The research results provide references for resolving the resources and environment constraints in the Yellow River Basin. It provides useful inspiration for promoting ecological protection and high-quality development strategies in the Yellow River Basin.

Reduction of Dust on Solar Panels through Unipolar Electrostatic Traveling Wave

In this study, a novel electrostatic cleaning scheme has been applied to a new designed and developed electrode having high cleaning efficiency. In this method, a high voltage, four-channel, 1 Hz square wave signal is applied to a specially designed electrode array. Models of the electric field distribution of the proposed electrode array were developed and analyzed using Ansys Maxwell simulation software and printed circuits boards (PCBs) were produced. The performance of dust removal using the electrodes was evaluated. A 1 MW solar power plant was taken as a case study, and performance and cost were compared with the classical panel cleaning method (wet cleaning system). Our study has shown that the electrostatic cleaning method can be carried out with lower cost and higher efficiency than existing methods.

Supply and demand approaches to the urban residential property prices determination; transactions evidence from Nigeria

ABSTRACT This study provided an empirical framework that isolates the supply and demand perspectives in urban residential property transactions towards removing the information shield and asymmetry nature of property transaction information in Nigeria. A three-stage classical panel longitudinal design was adopted through feedback from completed transaction data on residential properties from the licensed estate agents. The study provided balanced and complete transaction information desired by the parties to any type of residential property transaction and bridged the wide gap between transaction expectation and actualization to prevent improper signalling in property transaction prescriptions. The study outcome generated vital indices and metrics for creating a complete property transaction database for the attraction of international professionals to participate in Nigeria’s urban property market transactions. This study is the first one in Nigeria to link the asking prices on sales and rental transactions in residential property transactions from the start to the conclusion.

RETRACTED ARTICLE: Analysis of the correlation between agricultural innovation ecosystem and economic growth

This article has been retracted from publication in the Taylor & Francis journal, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science. Following publication, concerns were raised by multiple third-parties around the content of the special issue and the decision-making process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that this article included in Special Issue titled “ Envisage Computer Modelling and Statistics for Agriculture”, guest edited by Gunasekaran Manogaran was not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines.

Possibilities of Useful Use of Glued Wooden Construction Residues.

People have erected buildings with the use of timber structures for a long time. The uses of timber constructions are very diverse—it is used for the production of exterior wall and roof constructions, window frames and doors, and it is used for dry as well as wet premises. Scandinavian countries have extremely vast experience of using timber structures. Latvia has a rather extensive timber processing and timber structure manufacturing sector. Many companies are involved in timber processing, however, to enable even more extensive use of timber structures, environmental and technically economic requirements of contemporary building must be taken into consideration. Environmental requirements for timber structures provide certain advantages in comparison to other building materials, but technically economic requirements are very important as well. The development of manufacturing of glued constructions and research of production processes of these constructions allows one to find solutions for the reduction in the cost of timber structures, and the results of such research can ensure significant development of the use of timber structures in building, as well as reduce total construction costs. The basic objective of the study is to investigate the residual materials arising as a result of processing cross-laminated timber constructions (CLT panels), material generated as a result of high levels of construction production, and research of the opportunities to reprocess the residual materials generated as a result of laminated timber structure manufacturing into materials suitable for production of building constructions. The majority of CLT panels are manufactured using 20, 30 and 40 mm thick boards, and, during the panel manufacturing process, there are various standard thicknesses of panels, for example, 60, 80, 100, 120, 140, 160 mm, etc. Various layers are used for the creation of various thicknesses depending on the necessary technical properties. Various arrangements of the thickness of a single panel will cause different structural and physical behaviour (i.e., impact of changes in moisture, fire resistance, etc.). During the research and for the purposes of testing of CLT panels, only residues with equal types and thicknesses of lamellae were selected. Two main purposes were included in the panel testing process: (1) Comparison of technical performance of the residues of CLT panels with the classic CLT panel. Curve strength and tensile strength tests were performed in accordance with LVS EN standards (LVS EN 16351: 2016 and LVS EN 408 + A1: 2012). All the samples were prepared according to the LVS EN standards. (2) To assess the impact of two resins (melamine urea formaldehyde (MUF) and polyurethane (PU)), widely used in industry, on structural properties of recycled CLT material. Results of the research show that recycling residues of glued wooden constructions may lead to good results, and manufacturing residues of CLT panels may be successfully used in construction and for the reduction in CLT panel manufacturing costs.

The Impact Of Financial Distress, Audit Committee, And Firm Size On The Integrity Of Financial Statements

This study aims to examine the impact of Financial Distress, Audit Committee, and Firm Size on The Integrity of Financial Statements. The integrity of financial statements can reflect the company's financial performance. The research sample obtained were based on purposive sampling technique, is including 32 consumer goods industry sectors firms listed in Indonesian Stock Exchange over the period of 2013-2017. Sources of research data are annual reports. Data analysis used descriptive statistics, classic assumption tests, estimation models, and panel data regression analysis. The panel data regressions analysis was the methodology employed for verifying the factors that may influence the integrity of financial statements. Based on the results of the Chow test and the Hausman test, the appropriate panel data regression model to be used for this study is the fixed effects model. This study provides empirical evidence that financial distress is negatively associated with the the integrity of financial statements, while audit committee and company size have positive effect on the the integrity of financial statements. 
 Keywords: Financial Distress, Audit Committee, Firm Size, Integrity of Financial Statements.

Are \u201cInternet+\u201d tactics the key to poverty alleviation in China\u2019s rural ethnic minority areas? Empirical evidence from Sichuan Province

China has made great efforts to alleviate poverty in rural ethnic minority areas and targeted achieving the poverty-alleviation task by the end of 2020. Aba, Ganzi, and Liangshan, three of the poorest ethnic prefectures in Sichuan Province, Southwest China, have all implemented “Internet+” tactics since 2013, which have had the positive effect of increasing family revenues by improving communication infrastructure and encouraging the large-scale use of e-commerce. This paper aims to comprehensively investigate whether “Internet+” tactics play a key role in poverty alleviation in Sichuan’s rural ethnic minority areas and to propose further measures to enhance the efficiency of e-commerce practice. To this end, we conduct an analysis using the framework of classic growth theory and use panel data from 2000 to 2018 to examine the relationship between Communication Infrastructure Investment (CII) and a set of poverty-alleviation indicators, including local GDP growth rate (LGGR), local government revenue (LGR), and per-capita income of residents (PCIR). The results indicate that strengthening CII improves the PCIR and local economic growth, playing a key role in poverty alleviation. However, the stimulation of CII on LGGR and LGR wanes as time passes. More financial and technical actions will be needed to improve the efficiency and quality of current strategies for sustainable development in those areas.

Excavate the Fayum Mummy Portraits and Bury Ancient Egyptian Stereotypes

The Fayum mummy portraits from ancient Greco–Roman Egypt are exceptional in that they are the only surviving classical (ancient Greek and Roman) panel paintings from antiquity and are antecedents o...

Development of the Panel ARDL by Adding Space-Time effect to Modeling Monthly Paddy Producer Price in Java

Classic panel data modelling has large cross section data (N) and small time series data (T). The aim of the study was to develop a panel data type T > N by adding space-time effect to a panel ARDL model. The basic idea was to combine between the AR (Autoregressive), DL (Distributed Lag), and ST (space-time) effect. The model was applied to paddy producer price at the farmer level in Java from January 2016 to December 2019 where the explanatory variable was the Farmers’ Terms of Trade. Both variables were stationary in the first-difference I (1). The results showed that the ST-ARDL model was good for T > N panel data types. The ST-ARDL model with reparameterization of explanatory variables was able to overcome the problem of multicollinearity. The ST-ARDL model was able to improve the performance of the classic panel data model which was able to reduce the RMSE and increased R2-adj. The linear combination of this model was cointegrated or had a long-term equilibrium relationship. Another result of the study was the ST-ARDL model provided better estimation performance than the AR (p), ARDL (p, q) and GSTAR (p, λ) models with the smaller MAPE values. For further research, the ST-ARDL model can be developed by adding the effect of space-time interaction.