“When Life Gives You Lemons”: Using Cross‐Sectional Surveys to Identify Chronic Poverty in the Absence of Panel Data

2022 ◽  
Author(s):  
Kimberly Bolch ◽  
Luis F. Lopez‐Calva ◽  
Eduardo Ortiz‐Juarez
Keyword(s):  
Panel Data ◽  
Cross Sectional ◽  
Chronic Poverty

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.

Nonparametric panel data regression with parametric cross-sectional dependence

2021 ◽  
Author(s):  
Alexandra Soberon ◽  
Juan M Rodriguez-Poo ◽  
Peter M Robinson
Keyword(s):  
Panel Data ◽  
Monte Carlo Study ◽  
Generalized Least Squares ◽  
Linear Estimator ◽  
Dependence Structure ◽  
Finite Sample ◽  
Cross Sectional ◽  
Panel Data Regression ◽  
Local Linear Estimator ◽  
Cross Sectional Dependence

Abstract In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A Generalized Least Squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterizing 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 analyzing the implications of the European Monetary Union for its member countries.

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

2021 ◽  
Author(s):  
Lajos Horváth ◽  
Zhenya Liu ◽  
Gregory Rice ◽  
Yuqian Zhao
Keyword(s):  
Panel Data ◽  
Common Factors ◽  
Real Data ◽  
Change Points ◽  
High Dimensional ◽  
Asymptotic Results ◽  
Cross Sectional ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Cross Sectional Dependence

Abstract 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 {N, T} → ∞, 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.

scholarly journals The Effect of Foreign Portfolio Equity Purchases on Security Returns in Kenya: Evidence from NSE Listed Financial Institutions

2017 ◽  
Vol 9 (4) ◽  
pp. 202
Author(s):  
Loice Koskei
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Stock Returns ◽  
Financial Institutions ◽  
Sampling Technique ◽  
Ordinary Least Squares ◽  
Capital Requirements ◽  
Cross Sectional ◽  
International Portfolio ◽  
Security Returns

Foreign portfolio inflows increase the liquidity and the volume of finance available for financial institutions. At the same time, as foreign portfolio inflows finances in part the capital requirements of local companies, it can also increase the competitiveness of these companies. A huge surge of the inflows can be very inflationary because this forces the Central Bank of Kenya to expand the country’s monetary base by releasing counterpart domestic currency which eventually feeds into the inflationary process. The main aim of this study was to find out the effect of international portfolio equity purchases on security returns of listed financial institutions in Kenya. The study population was 21 financial institutions listed on the Nairobi Securities Exchange. Using purposive sampling technique the study concentrated on 14 financial institutions. The research design of the study was causal as it is concerned more with understanding the connection between cause and effect relationships. The study adopted panel data regression using the Ordinary Least Squares (OLS) method where the data included time series and cross-sectional. A unit root test was carried in this study to examine stationarity of variables because it used panel data which combined both cross-sectional and time series information. Panel estimation results indicated that international portfolio equity purchases have no effect on stock returns of listed financial institutions in Kenya. The study recommended implementation of regulations and policies that would attract foreign portfolio equity inflows in financial institutions.

scholarly journals Welfare and the depth of informality: Evidence from five African countries

2021 ◽  
Author(s):  
Eva-Maria Egger ◽  
Cecilia Poggi ◽  
Héctor Rufrancos
Keyword(s):  
Panel Data ◽  
Linear Relationship ◽  
Social Insurance ◽  
Household Head ◽  
African Countries ◽  
Cross Sectional ◽  
Hours Worked ◽  
Household Level ◽  
Sub Saharan ◽  
The Relationship

This study explores the relationship between household poverty and depth of informality by proposing a new measure of informality at the household level. It is defined as the share of activities (hours worked or income earned) without social insurance for wage workers in the household. We apply cross-sectional regressions to five urban sub-Saharan African countries, showing that a household head informality dummy obscures a non-linear relationship between the depth of household informality and welfare outcomes. In some countries, a small share of income from formal jobs is associated with at least the same welfare as a fully formal portfolio. By assessing transitions between household portfolios with panel data for urban Nigeria, we also show that most welfare differences are explained by selection and that movements in and out of formality cannot sufficiently change welfare trajectories. The results call for better inclusion of informal profiles to social insurance programmes.

FDI determinants in an idiosyncratic country. A reappraisal over the Russian regions during transition years

2012 ◽  
Vol 45 (1-2) ◽  
pp. 1-10 ◽  
Author(s):  
Concetta Castiglione ◽  
Yulia Gorbunova ◽  
Davide Infante ◽  
Janna Smirnova
Keyword(s):  
Panel Data ◽  
Institutional Environment ◽  
Institutional Context ◽  
Russian Regions ◽  
Cross Sectional ◽  
Foreign Investors ◽  
Investment Pattern ◽  
Transition Experience ◽  
The Impact ◽  
Fdi Determinants

We investigate the factors attracting FDI into highly diversified Russian regions during the phase of transition, and verify the impact of transition experience on the current FDI inflow. Using cross-sectional and panel data, we demonstrate that the highly inhomogeneous investment pattern is explained, in addition to classical demand factors, by specific economic and socio-institutional regional characteristics. Russia appears as an idiosyncratic country where foreign investors seek a stable social and institutional context. Using recent FDI data we show that transition experiences influence current FDI inflow, particularly when the strength of the institutional environment and availability of infrastructures are taken into account.

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