scholarly journals Kernel estimation for panel data with heterogeneous dynamics

2019 ◽  
Author(s):  
Ryo Okui ◽  
Takahide Yanagi
Keyword(s):  
Panel Data ◽  
Kernel Smoothing ◽  
Kernel Estimation ◽  
Kernel Estimator ◽  
Series Length ◽  
Cross Sectional ◽  
Sample Mean ◽  
Jackknife Method ◽  
Nonparametric Kernel ◽  
Very High

Abstract This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances and autocorrelations for each unit and then apply kernel smoothing to compute their density functions. The dependence of the kernel estimator on bandwidth makes asymptotic bias of very high order affect the required condition on the relative magnitudes of the cross-sectional sample size (N) and the time-series length (T). In particular, it makes the condition on N and T stronger and more complicated than those typically observed in the long-panel literature without kernel smoothing. We also consider a split-panel jackknife method to correct bias and construction of confidence intervals. An empirical application illustrates our procedure.

scholarly journals ASYMPTOTICALLY UNBIASED ESTIMATION OF AUTOCOVARIANCES AND AUTOCORRELATIONS WITH LONG PANEL DATA

2010 ◽  
Vol 26 (5) ◽  
pp. 1263-1304 ◽  
Author(s):  
Ryo Okui
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Unobserved Heterogeneity ◽  
Small Samples ◽  
Unbiased Estimation ◽  
Series Length ◽  
Cross Sectional ◽  
Long Run ◽  
The Individual ◽  
Time Invariant

An important reason for analyzing panel data is to observe the dynamic nature of an economic variable separately from its time-invariant unobserved heterogeneity. This paper examines how to estimate the autocovariances of a variable separately from its time-invariant unobserved heterogeneity. When both cross-sectional and time series sample sizes tend to infinity, we show that the within-group autocovariances are consistent, although they are severely biased when the time series length is short. The biases have the leading term that converges to the long-run variance of the individual dynamics. This paper develops methods to estimate the long-run variance in panel data settings and to alleviate the biases of the within-group autocovariances based on the proposed long-run variance estimators. Monte Carlo simulations reveal that the procedures developed in this paper effectively reduce the biases of the estimators for small samples.

scholarly journals A homogeneous approach to testing for Granger non-causality in heterogeneous panels

2020 ◽  
Author(s):  
Artūras Juodis ◽  
Yiannis Karavias ◽  
Vasilis Sarafidis
Keyword(s):  
Panel Data ◽  
Cross Sections ◽  
Fixed Effects ◽  
Wald Test ◽  
Panel Data Models ◽  
Finite Sample ◽  
Cross Sectional ◽  
Data Set ◽  
Jackknife Method ◽  
Heterogeneous Panels

AbstractThis paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional (N) and time series (T) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies in the fact that under the null hypothesis, the Granger-causation parameters are all equal to zero, and thus they are homogeneous. Therefore, we put forward a pooled least-squares (fixed effects type) estimator for these parameters only. Pooling over cross sections guarantees that the estimator has a $$\sqrt{NT}$$ NT convergence rate. In order to account for the well-known “Nickell bias”, the approach makes use of the well-known Split Panel Jackknife method. Subsequently, a Wald test is proposed, which is based on the bias-corrected estimator. Finite-sample evidence shows that the resulting approach performs well in a variety of settings and outperforms existing procedures. Using a panel data set of 350 U.S. banks observed during 56 quarters, we test for Granger non-causality between banks’ profitability and cost efficiency.

scholarly journals Enhancing Optical Correlation Decision Performance for Face Recognition by Using a Nonparametric Kernel Smoothing Classification

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5092
Author(s):  
Matthieu Saumard ◽  
Marwa Elbouz ◽  
Michaël Aron ◽  
Ayman Alfalou ◽  
Christian Brosseau
Keyword(s):  
Face Recognition ◽  
Image Recognition ◽  
Fourier Transforms ◽  
Kernel Smoothing ◽  
Kernel Estimation ◽  
Regression Function ◽  
Correlation Peak ◽  
Optical Correlation ◽  
The Individual ◽  
Nonparametric Kernel

Optical correlation has a rich history in image recognition applications from a database. In practice, it is simple to implement optically using two lenses or numerically using two Fourier transforms. Even if correlation is a reliable method for image recognition, it may jeopardize decision making according to the location, height, and shape of the correlation peak within the correlation plane. Additionally, correlation is very sensitive to image rotation and scale. To overcome these issues, in this study, we propose a method of nonparametric modelling of the correlation plane. Our method is based on a kernel estimation of the regression function used to classify the individual images in the correlation plane. The basic idea is to improve the decision by taking into consideration the energy shape and distribution in the correlation plane. The method relies on the calculation of the Hausdorff distance between the target correlation plane (of the image to recognize) and the correlation planes obtained from the database (the correlation planes computed from the database images). Our method is tested for a face recognition application using the Pointing Head Pose Image Database (PHPID) database. Overall, the results demonstrate good performances of this method compared to competitive methods in terms of good detection and very low false alarm rates.

scholarly journals KERNEL ESTIMATION WHEN DENSITY MAY NOT EXIST

2008 ◽  
Vol 24 (3) ◽  
pp. 696-725 ◽  
Author(s):  
Victoria Zinde-Walsh
Keyword(s):  
Empirical Studies ◽  
Kernel Estimation ◽  
Kernel Estimator ◽  
Generalized Functions ◽  
Continuous Variables ◽  
Asymptotic Results ◽  
Absolutely Continuous ◽  
Conditional Mean ◽  
Special Cases ◽  
Nonparametric Kernel

Nonparametric kernel estimation of density and conditional mean is widely used, but many of the pointwise and global asymptotic results for the estimators are not available unless the density is continuous and appropriately smooth; in kernel estimation for discrete-continuous cases smoothness is required for the continuous variables. Nonsmooth density and mass points in distributions arise in various situations that are examined in empirical studies; some examples and explanations are discussed in the paper. Generally, any distribution function consists of absolutely continuous, discrete, and singular components, but only a few special cases of nonparametric estimation involving singularity have been examined in the literature, and asymptotic theory under the general setup has not been developed. In this paper the asymptotic process for the kernel estimator is examined by means of the generalized functions and generalized random processes approach; it provides a unified theory because density and its derivatives can be defined as generalized functions for any distribution, including cases with singular components. The limit process for the kernel estimator of density is fully characterized in terms of a generalized Gaussian process. Asymptotic results for the Nadaraya–Watson conditional mean estimator are also provided.

BOOTSTRAP AND k-STEP BOOTSTRAP BIAS CORRECTIONS FOR THE FIXED EFFECTS ESTIMATOR IN NONLINEAR PANEL DATA MODELS

2016 ◽  
Vol 32 (6) ◽  
pp. 1523-1568 ◽  
Author(s):  
Min Seong Kim ◽  
Yixiao Sun
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Markov Models ◽  
Model Parameters ◽  
Series Length ◽  
Cross Sectional ◽  
Incidental Parameters ◽  
Fixed Effects Estimator ◽  
Coverage Accuracy ◽  
Bias Corrections

Because of the incidental parameters problem, the fixed effects maximum likelihood estimator in a nonlinear panel data model is in general inconsistent when the time series length T is short and fixed. Even if T approaches infinity but at a rate not faster than the cross sectional sample size n, the fixed effects estimator is still asymptotically biased. This paper proposes using the standard bootstrap and k-step bootstrap to correct the bias. We establish the asymptotic validity of the bootstrap bias corrections for both model parameters and average marginal effects. Our results apply to static models as well as some dynamic Markov models. Monte Carlo simulations show that our procedures are effective in reducing the bias of the fixed effects estimator and improving the coverage accuracy of the associated confidence interval.

The relationship betweenparityand the incident of perinealruptures on women’s physiological maternity

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Evi Rosita ◽  
Siti Nurnaningrum
Keyword(s):  
Public Health ◽  
Health Center ◽  
P Value ◽  
Chi Square ◽  
Cross Sectional ◽  
Midwifery Care ◽  
Public Health Center ◽  
Record Data ◽  
The Relationship ◽  
Very High

There are about 2.8 million incident of perineal rupture in maternal physiological labor. In 2050,it is estimated that the incidence of perineal rupture can be 6.3 million if it is not accompanied by a good midwifery care. In 2016, in Trawas, there was (89%) perineal rupture in primiparas and (57%) perineal rupture in multiparas. Perineal rupture incidences due to parity were still very high. This study aims to analyze the relationship between parity and the incidence of perineal rupture . It is quantitative studyusing a cross sectional approach, by using analysis of physiological maternity women  medical record data from January to April 2017 of 130 peoplein Trawas Public Health Center, Mojokerto Regency.The dataanalysis used was Chi - Square , indicated by p value = 0,000 with ɑ = 0,05. It means that the value of p <ɑ, so H1 is accepted. It can be concluded that there is a relationship between parity and the incidence of perineal rupture on physiological maternity women in Trawas Public Health Center,Mojokerto Regency. Midwives can apply collaboration with patients and their families to have physical and psychologicalpreparation with an alternative of hypnobirthing methods.

HIGH PREVALENCE OF MALNUTRITON AND WORM INFECTION AMONG VAN KIEU AND PAKOH CHILDREN AGED 12-36 MONTHS IN DAKRONG DISTRICT, QUANG TRI PROVINCE

2012 ◽  
pp. 129-134
Author(s):  
Thi Lan Tran ◽  
Thi Huong Le ◽  
Xuan Ninh Nguyen
Keyword(s):  
Cross Sectional Study ◽  
Age Group ◽  
Infection Status ◽  
Sectional Study ◽  
Group Recommendation ◽  
Related Factors ◽  
Normal Children ◽  
Cross Sectional ◽  
High Prevalence ◽  
Very High

Objectives: Assess the nutritional status, worm infection status and some related factors among children aged 12-36 months of Dakrong district, Quang Tri province. Subject and method: A cross sectional study was carried out in 2010, in 680 children aged 12-36 months in 4 communes of Dakrong district, Quang Tri province. Results: The malnutrition rate was 55.0% for underweight, 66.5% for stunting and 16.2% for wasting. The prevalence of malnutrition increases by age group. The prevalence of worm infection was 31.6%, the highest prevalence was belong to Ascaris infection (24.6%), followed by Hookworm and Trichuris (6.5% and 6.2%, respectively). The prevalence of worm infection among children under two is very high (27.0%). The prevalence of worm infection was distributed quite equally between the malnutrition children group and normal children group. Recommendation: Early deworming forchildren from 12 months should be considered as important strategy against the malnutrition of children in Dakrong district, Quang Tri province

An Examination of Audit Report Lag for Banks: A Panel Data Approach

2000 ◽  
Vol 19 (2) ◽  
pp. 159-174 ◽  
Author(s):  
B. Charlene Henderson ◽  
Steven E. Kaplan
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Explanatory Power ◽  
Omitted Variables ◽  
Audit Report ◽  
Cross Sectional ◽  
Cross Sectional Analysis ◽  
The Cross ◽  
Sectional Analysis

This study investigates the determinants of audit report lag (ARL) for a sample of banks. Researchers have been interested in the determinants of ARL, in part, because it impacts the timeliness of public disclosures. However, prior ARL research has relied exclusively on regression analysis of cross-sectional samples of companies from many industries. In addition to focusing exclusively on banks, panel data analysis is introduced and compared with cross-sectional analysis to demonstrate its power in dynamic settings and its potential to improve estimation. Results reveal important differences between cross-sectional analysis and panel data analysis. First, bank size is negatively related to ARL in cross-section but positively related to ARL using panel data analysis. The cross-sectional size estimate is subject to omitted variables bias, and furthermore, cross-sectional analysis fails to capture variation in size over time in relation to ARL. Panel data analysis both accounts for omitted variables and captures the dynamics of the relationship between size and ARL. As well, the panel data model's explanatory power far exceeds that of the cross-sectional model. This is primarily due to the panel model's use of firm-specific intercepts that both capture the role of reporting tradition and eliminate heterogeneity bias. Thus, panel data analysis proves to be a powerful tool in the analysis of ARL.

Just Like Leaves in the Wind? Exploring the Effect of the Interplay of Media Coverage and Personal Characteristics on Issue Salience

2017 ◽  
Author(s):  
Agatha Kratz ◽  
Harald Schoen
Keyword(s):  
Individual Differences ◽  
Panel Data ◽  
Financial Crisis ◽  
Media Coverage ◽  
News Coverage ◽  
Personal Characteristics ◽  
Election Campaign ◽  
Issue Salience ◽  
Cross Sectional ◽  
The Impact

This chapter explores the effect of the interplay of personal characteristics and news coverage on issue salience during the 2009 to 2015 period and during the election campaign in 2013. We selected four topics that played a considerable role during this period: the labor market, pensions and healthcare, immigration, and the financial crisis. The evidence from pooled cross-sectional data and panel data supports the notion that news coverage affects citizens’ issue salience. For obtrusive issues, news coverage does not play as large a role as for rather remote topics like the financial crisis and immigration. The results also lend credence to the idea that political predilections and other individual differences are related to issue salience and constrain the impact of news coverage on voters’ issue salience. However, the evidence for the interplay of individual differences and media coverage proved mild at best.

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