Nonparametric Panel Data Models Research Articles

Time-varying impacts of green credit on carbon productivity in China: New evidence from a non-parametric panel data model

In the context of global climate change threatening human survival, and in a post-pandemic era that advocates for a global green and low-carbon economic recovery, conducting an in-depth analysis to assess whether green finance can effectively support low-carbon economic development from a dynamic perspective is crucial. Unlike existing research, which focuses solely on the average effects of green credit (GC) on carbon productivity (CP), we introduce a non-parametric panel data model to investigate GC's impact on CP across 30 provinces in China from 2003 to 2021, verifying a significant time-varying effect. Specifically, during the first phase (2003–2008), GC negatively impacted CP. In the second phase (2009–2014), this negative influence gradually diminished and transformed into a positive effect. In the third phase (2015–2021), GC continued to positively influence CP, although this effect became insignificant during the pandemic. Further subgroup analysis reveals that in the regions with low environmental regulations, GC did not significantly boost CP throughout the sample period. In contrast, in the regions with high environmental regulations, GC's positive effect persisted in the mid to late stages of the sample period. Additionally, compared to the regions with low levels of marketization, the impact of GC on CP was more pronounced in highly marketized regions. This indicates that the promoting effect of GC on CP depends on strong support from environmental regulations and well-functioning market mechanisms. By adopting a non-parametric approach, this study reveals variations in the impact of GC on CP across different stages and under the influence of the pandemic shock, offering new insights into the relationship between GC and China's CP.

INFERENCE IN PARTIALLY IDENTIFIED PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS

In this paper, we develop methods for statistical inferences in a partially identified nonparametric panel data model with endogeneity and interactive fixed effects. Under some normalization rules, we can concentrate out the large-dimensional parameter vector of factor loadings and specify a set of conditional moment restrictions that are involved with only the finite-dimensional factor parameters along with the infinite-dimensional nonparametric component. For a conjectured restriction on the parameter, we consider testing the null hypothesis that the restriction is satisfied by at least one element in the identified set and propose a test statistic based on a novel martingale difference divergence measure for the distance between a conditional expectation object and zero. We derive a tight asymptotic distributional upper bound for the resultant test statistic under the null and show that it is divergent at rate-N under the global alternative. To obtain the critical values for our test, we propose a version of multiplier bootstrap and establish its asymptotic validity. Simulations demonstrate the finite sample properties of our inference procedure. We apply our method to study Engel curves for major nondurable expenditures in China by using a panel dataset from the China Family Panel Studies.

A nonparametric panel data model for examining the contribution of tourism to economic growth

A nonparametric panel data model for examining the contribution of tourism to economic growth

Exploring the time-varying asymmetric effects of environmental regulation policies and human capital on sustainable development efficiency: A province level evidence from China

The Sustainable Development Goals (SDGs) for 2022 progress report indicates that global policies to reach SDGs 7 and 8 might not be as good as they could be. In the recent discussions at COP27 and in academic literature, sustainable development efficiency has also been brought up as a way to close this gap. Achieving sustainable development efficiency necessitates a favorable policy environment within economies. Environmental policies, human capital, and broader policy dimensions are just a few of the factors that influence this environment. The existing policy discourse and academic literature suggest that environmental regulation policies, human capital development, industrialization, urbanization, and GDP are potential drivers to achieve sustainable development efficiency. Hence, the study tends to erect a policy framework by investigating the time-varying effects of environmental regulation policies and human capital on sustainable development efficiency with the consort of industrialization and urbanization in a large economy—China. To this end, the study deploys the province-level panel data from 1998 to 2017 and utilizes several advanced econometric methods (Non-Parametric Panel Data Model, Wavelet Quantile Correlation, and Non-Parametric Panel Granger Causality Test). The results suggest that positive shocks in environmental regulation policies and human capital have favorable effects on sustainable development efficiency; however, their negative shocks have adverse impacts over time. Further, industrialization and urbanization significantly deteriorate the sustainable development efficiency. Based on the results, phase-wise policies are recommended in order to achieve SDGs 7 and 8.

Heterogeneous effects of oil price fluctuations: Evidence from a nonparametric panel data model in Canada

In this paper, we employ a nonparametric panel data technique to examine the impacts of oil price fluctuations on GDP per capita in two groups of oil-exporting and oil-importing provinces in Canada over the period 1984–2016. This technique allows the trend and coefficient functions to vary over the sample period as unknown functional forms. The results show that the relationship between oil price and GDP per capita is time-varying in both groups of Canadian provinces. While both groups of provinces benefited from the striking oil price hike of the 2000s, the relatively low oil prices before 2000 had positive effects only in oil-importing provinces. Our results highlight the vital role of interprovincial trade in mitigating the adverse effects of oil price volatility on the Canadian provinces.

Dynamic impacts of energy consumption on economic growth in China: Evidence from a non-parametric panel data model

To empirically gauge the efficacy of energy policies, we propose a non-parametric method to investigate the relationship between economic growth and energy consumption from both time and space perspectives. Specifically, we rely on the local linear dummy variable estimation (LLDVE) method to explore the time-varying province-specific trends, the common trend, and the coefficients based on panel data from 26 provinces in China from 1995 to 2017. We find that the promotion effect of energy consumption on economic growth changes over time, as evidenced by the inverted U shape of the relationship. Moreover, the non-parametric model captures such an effect better than the parametric model. With the dual goals of sustainable economic growth and carbon emissions reduction in mind, we classify the sample according to the degree of carbon intensity, which indicates that energy efficiency should be improved in high-carbon development areas, while more attention should be paid to investment and innovations in low-carbon development areas.

الجامعة المستنصرية / كلية الادارة والاقتصاد

The water is polluted as a result of the presence of foreign substances, whether organic or inorganic. These substances spoil the water quality, which is what happens to the Tigris River. These waste products pollute the waters of the Tigris River, harmful to human health, causing diseases, and among these diseases is an amoeba, whose data represented by the numbers of people infected with the disease were taken on both sides of Karkh and Rusafa in Baghdad for the year 2018 as a dependent variable (Y) in a complete series (12) month. The concentrations of water pollutants were taken on the number of cases of the disease. Seven pollutant concentrations were relied upon, which represent the explanatory variables (Xi), which represent cross-sectional data for ten stations located on the banks of the Tigris River on both sides of Karkh and Rusafa. Because of dealing with cross-sections and time series, Panel data is the best name for it. We used parametric and non-parametric Panel data models with several non-parametric estimators, which are the Nadaria Watson weighted estimator and the Nadaria Watson unweighted estimator to determine which of the estimators is the most efficient that gives the best model, and the number of recorded injuries In hospitals and health centers on both sides of Karkh and Rusafa, which suffer from the problem of heterogeneity of variance, and that the variables of pollutant concentrations suffer from the problem of autocorrelation, and that the appropriate parametric model is the random effect model for the panel data according to the Houseman test.

Time-varying geopolitical risk and oil prices

This paper examines the time-varying effect of oil price on geopolitical risk. Using monthly panel data of 16 countries for the period 1997:M01 – 2020:M02 and employing a varying-coefficient nonparametric panel data model, we find that oil price as well as its volatility have a time-varying effect on geopolitical risk. Our results reveal that oil price is negatively associated with geopolitical risk for much of our sample period, albeit the weakening of the effect in recent periods. In contrast, oil price volatility is positively related with geopolitical risk for most of the sample period under the study. Further, we find a significant degree of heterogeneity in the time-varying country-specific trend functions of geopolitical uncertainty. Our findings have important implications for policymakers and market participants in managing geopolitical risk.

Nonparametric panel data regression with parametric cross-sectional dependence

Summary In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A generalised least squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterising the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analysing the implications of the European Monetary Union for its member countries.

Test for the covariance matrix in time-varying coefficients panel data models with fixed effects

This paper proposes tests for the null of sphericity and identity matrix for nonparametric time-varying coefficient panel data models with fixed effects. Firstly, based on the local linear smoothing technique, the estimators of the unknown coefficient functions and model residuals are obtained. Secondly, proper test statistics are proposed aiming at tests for sphericity or identity matrix with a large number of cross-sectional units and time series observations. In addition, the limiting distributions of the proposed test statistics are derived based on random matrix theory. At last, some simulation studies are conducted to examine the finite sample performance for the proposed test statistics and a real data example is analyzed.

Long-term determinants of income inequality: evidence from panel data over 1870–2016

We investigate the major factors which drive income inequality in the OECD countries, using long panel data which span the period from 1870 to 2016. We consider two measures of inequality: the Gini coefficient and the income share of the top 10% of the population. Employing the panel vector auto-regression method, we show that a positive shock to the real interest rate and government spending is negatively and significantly associated with income inequality in the middle, as well as at the top end of the income distribution. An increase in real GDP per capita leads to an increase in income inequality, measured by the Gini coefficient, whereas an advance in financial development reduces it. We find that income inequality responds negatively to positive innovation shocks initially, but this effect becomes positive with some time lag for top-income inequality. Educational attainment significantly reduces top-income inequality. Our results are robust to alternative specifications, including the local projection method and estimations based on different samples. We also capture the time dynamics in our series using a time-varying nonparametric panel data model and show that the real interest rate and financial development are negatively associated with income inequality for most of the period in the post-World War II era, while the effect of real GDP per capita is positive and significant over the same period.

Kernel-based spatial error model for analyzing spatial panel data

Spatial panel data model captures spatial interactions across spatial units and over time. Lots of effort have been devoted to develop effective estimation methods for parametric and nonparametric spatial panel data models. Varying coefficient model has received a great deal of attention as an important tool for modeling panel data. In this paper we propose a kernel-based spatial error model for the purpose of analyzing spatial panel data. This model is based on the idea of fixed effect time-varying coefficient model and the kernel technique of support vector machine along with the technique of regularization. A generalized cross validation method is also considered for choosing the hyperparameters which affect the performance of the proposed model. The proposed model is evaluated through numerical studies.

Urbanization and Housing Prices in OECD Countries: 1870 to 2016

We examine relationship between urbanization and real house prices for a panel of OECD countries using a long historical data that covers the period 1870 to 2016. We use parametric and non-parametric panel data models that estimate the impulse responses and the time-varying coefficients. Our parametric estimates show that an increase in the rate of urbanization has a significant positive effect on real house prices, while the non-parametric estimates show that the relationship between urbanization and real house prices is time-varying. We find that mortgage to GDP ratio is a channel through which urbanization contributes to higher house prices.

Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method

We propose statistical inferential procedures for nonparametric panel data models with interactive fixed effects in a kernel ridge regression framework. Compared with the traditional sieve methods, our method is automatic in the sense that it does not require the choice of basis functions and truncation parameters. The model complexity is controlled by a continuous regularization parameter which can be automatically selected by the generalized cross-validation. Based on the empirical process theory and functional analysis tools, we derive the joint asymptotic distributions for the estimators in the heterogeneous setting. These joint asymptotic results are then used to construct the confidence intervals for the regression means and the prediction intervals for future observations, both being the first provably valid intervals in literature. The marginal asymptotic normality of the functional estimators in a homogeneous setting is also obtained. Our estimators can also be readily modified and applied to other widely used semiparametric models, such as partially linear models. Simulation and real data analyses demonstrate the advantages of our method. Supplementary materials for this article are available online.

Oil prices and economic policy uncertainty: Evidence from a nonparametric panel data model

We examine the relationship between oil prices and economic policy uncertainty in G7 countries. To do so, we employ a nonparametric panel data technique that allows the trend and coefficient functions to evolve as unknown time-varying functional forms. We also estimate country-specific and common trend functions allowing them to evolve over time. Using monthly data from G7 countries over the period 1997:01–2018:06, we find that the effect of oil prices on economic policy uncertainty is time-varying. Our results show that the estimated time-varying coefficient function of the oil price was negative in years in which increases in oil prices were driven by a surge in global aggregate demand. Further, our nonparametric local linear estimates show that the country-specific and common trend functions are increasing over time. Our findings are robust to endogeneity and alternative specifications.

R&D intensity and carbon emissions in the G7: 1870–2014

We examine the effect of research and development (R&D) intensity on carbon dioxide (CO2) emissions in the Group of Seven (G7) countries since the nineteenth century using a non-parametric panel data model. Our estimates suggest that the relationship between R&D and CO2 emissions is time-varying. The estimated time-varying coefficient function of R&D was negative for three quarters of the period studied, but was positive for a 35-year period (1955–1990) during the second half of the twentieth century. Our non-parametric local linear estimates show that the common trend functions gradually increased for the first 110 years (1870–1980), but then flattened out and showed a slight decrease for the next three decades.

Non-parametric Panel Data Models with Interactive Fixed Effects

This article studies non-parametric panel data models with multidimensional, unobserved individual effects when the number of time periods is fixed. I focus on models where the unobservables have a factor structure and enter an unknown structural function non-additively. The setup allows the individual effects to impact outcomes differently in different time periods and it allows for heterogeneous marginal effects. I provide sufficient conditions for point identification of all parameters of the model. Furthermore, I present a non-parametric sieve maximum likelihood estimator as well as flexible semiparametric and parametric estimators. Monte Carlo experiments demonstrate that the estimators perform well in finite samples. Finally, in an empirical application, I use these estimators to investigate the relationship between teaching practice and student achievement. The results differ considerably from those obtained with commonly used panel data methods.

Nonparametric panel data model for crude oil and stock market prices in net oil importing countries

This paper introduces an innovative nonparametric panel data approach to model the long-run relationship between the monthly oil price index and stock market price indices of ten large net oil importing countries; namely, the United States, Japan, China, South Korea, India, Germany, France, Singapore, Italy and Spain. In the proposed model, we allow the coefficient on the oil price index to be a time-varying function which evolves over time in a way that is assumed to be unknown. We also allow the common trend function to evolve over time, as well as extending the model further to incorporate country-specific trend functions. We employ a data-driven local linear method to estimate these time-varying trend and coefficient functions. The results show that, despite being largely positive, there are several downward trends, reflecting the aftermath of the Iraq war and the recent unprecedented drop in the oil price. Overall, we find that the nonparametric panel data model better captures the way in which the underlying stock-oil price relationship has evolved over time in comparison to the point estimates of the parametric counterpart. Moreover, we find that stock market fundamentals play a significant role in determining the oil-stock price relationship. Our findings have important implication for policymakers and financial speculators.

NONPARAMETRIC AND SEMIPARAMETRIC PANEL DATA MODELS: RECENT DEVELOPMENTS

AbstractIn this paper, we provide an intensive review of the recent developments for semiparametric and fully nonparametric panel data models that are linearly separable in the innovation and the individual‐specific term. We analyze these developments under two alternative model specifications: fixed and random effects panel data models. More precisely, in the random effects setting, we focus our attention in the analysis of some efficiency issues that have to do with the so‐called working independence condition. This assumption is introduced when estimating the asymptotic variance–covariance matrix of nonparametric estimators. In the fixed effects setting, to cope with the so‐called incidental parameters problem, we consider two different estimation approaches: profiling techniques and differencing methods. Furthermore, we are also interested in the endogeneity problem and how instrumental variables are used in this context. In addition, for practitioners, we also show different ways of avoiding the so‐called curse of dimensionality problem in pure nonparametric models. In this way, semiparametric and additive models appear as a solution when the number of explanatory variables is large.

Estimation Of Spatial Dynamic Nonparametric Durbin Models With Fixed Effects

Background:Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel data models (SDPDM).Material and methods:Above all, we propose an iterative approach to estimate the spatial dynamic nonparametric Durbin model with corresponding hypothesis test and we find that convergence occurs since the second iteration. So we use a three stage iterative approach to improve the iterative approach.Results:The results indicate that the three stage iterative approach is more reliable when T or N is large and the accuracy of the nonparametric components estimation is very important.Conclusions:We believe that the three stage iterative approach can be applied to other spatial dynamic nonparametric panel data models as well.