Household Panel Survey for Research on Unemployment and Poverty. Proceedings of the American Statistical Association, Section on Survey Research Methods, 130, 609-622. West, B.T. and Olson, K. (2010). How Much of Interviewer Variance is Really Nonresponse ErrorVariance? Public Opinion Quarterly, 74, 1004-1026. Wiggins, R.D., Longford, N.T., and O’Muircheartaigh, C.A. (1992). A Variance Components Approach to Interviewer Effects. In Survey and Statistical Computing, A. Westlake, R. Banks, C. Payne, and T. Orchard (eds). Amsterdam: North-Holland. Zhang, D. and Lin, X
Spatial environmental heterogeneity are well known characteristics of field forest genetic trials, even in small experiments (<1ha) established under seemingly uniform conditions and intensive site management. In such trials, it is commonly assumed that any simple type of experimental field design based on randomization theory, as a completely randomized design (CRD), should account for any of the minor site variability. However, most published results indicate that in these types of trials harbor a large component of the spatial variation which commonly resides in the error term. Here we applied a two-dimensional smoothed surface in an individual-tree mixed model, using tensor product of linear, quadratic and cubic B-spline bases with different and equal number of knots for rows and columns, to account for the environmental spatial variability in two relatively small (i.e., 576 m2 and 5,705 m2) forest genetic trials, with large multiple-tree contiguous plot configurations. In general, models accounting for site variability with a two-dimensional surface displayed a lower value of the deviance information criterion than the classical RCD. Linear B-spline bases may yield a reasonable description of the environmental variability, when a relatively small amount of information available. The mixed models fitting a smoothed surface resulted in a reduction in the posterior means of the error variance (σ2e), an increase in the posterior means of the additive genetic variance (σ2a) and heritability (h2HT), and an increase of 16.05% and 46.03% (for parents) or 11.86% and 44.68% (for offspring) in the accuracy of breeding values, respectively in the two experiments.
The linear regression model requires robust estimation of parameters, if the measured data are contaminated by outlying measurements (outliers). While a number of robust estimators (i.e. resistant to outliers) have been proposed, this paper is focused on estimating the variance of the random regression errors. We particularly focus on the least weighted squares estimator, for which we review its properties and propose new weighting schemes together with corresponding estimates for the variance of disturbances. An illustrative example revealing the idea of the estimator to down-weight individual measurements is presented. Further, two numerical simulations presented here allow to compare various estimators. They verify the theoretical results for the least weighted squares to be meaningful. MM-estimators turn out to yield the best results in the simulations in terms of both accuracy and precision. The least weighted squares (with suitable weights) remain only slightly behind in terms of the mean square error and are able to outperform the much more popular least trimmed squares estimator, especially for smaller sample sizes.
This paper examines the return and volatility spillovers of different sectoral stock prices in Nigeria using monthly data from January 2007 to December 2016. We employ the Diebold and Yilmaz (2012) spillover approach and rolling sample analysis to capture the inherent secular and cyclical movements in the sector stocks market.We show that there is substantial difference between the behaviour of the sectoral stock return and volatility spillover indices over time. We find evidence of interdependence among sector stocks given the spillover indices. While the return spillover index reveals increased integration among the sectoral stocks, the volatility spillover index experiences significant bursts during major market crises. Interestingly, return and volatility spillovers exhibit both trends and bursts respectively.
[ Chowdhury, 2017 ]. 1.3 Research objective The present study, examine the relationship between modeled independent variables and unemployment for the years 1994-2016 in South Asian countries. It’s an emphasis on the unemployment rate that either proposed variables relationship with unemployment is significant or not. The objectives of this study were as follows: Examine the short-run and the long-run relationship between unemployment and other selected variables. Determination of how much forecast errorvariance of the independent variable can be described by exogenous
Background: In recent years’ income inequality has been an economic issue. The primary instrument for redistributing income is personal income tax. However, based on economic theory income inequality concerns indicators such as wages, transfer payments, taxes, social security contributions, and geographical mobility. Objectives: The objective of this paper is to examine the impact of certain labor market indicators on personal income taxation in Federation of Bosnia and Herzegovina (FB&H). Methods/Approach: Since personal income taxation consists of a very broad definition and for the purpose of this research only, income from dependent (employment) activity is observed. The econometric analysis is conducted using error correction modeling, as well as forecast errors variance decomposition. Results: The error correction model is estimated, and the cointegrating equation indicates that monthly wage and number of employees statistically significantly positively affect personal income taxes in FB&H in the long-run. After two years, the selected labor market indicators explain a considerable part of forecasting error variance of personal income tax revenues. Conclusions: The implementation of reforms in the labor market and tax policies of the FB&H is suggested. In order to achieve necessary reforms, efficient governance and general stable political environment are required.
Primary Energy Consumption, CO2 Emissions and Economic Growth: Evidence from India
This study examined static and dynamic causal relationships between primary energy consumption, gross domestic product, and CO2 emissions for India during the period 1970-2007. We tested for the presence of unit root and cointegration among the variables by incorporating endogenously determined structural breaks in the data. The causality is examined between test variables using Granger's approach (in VAR framework), and Dolado and Lütkepohl's approach. We find evidence of no cointegration relationship among the test variables in the presence of structural breaks. Further, static analysis shows that primary energy consumption does not granger-cause GDP, whereas GDP granger-causes primary energy consumption. The dynamic analysis shows conflicting results on the causal relationship between energy consumption and GDP. Since GDP explains 75.9% of the forecast error variance of primary energy consumption, whereas primary energy consumption explains only 0.96% of the forecast error variance of GDP, we can suggest that India should adopt policies that reduce energy consumption.
Large-scale establishment surveys often exhibit substantial temporal or cross-sectional variability in their published standard errors. This article uses a framework defined by survey generalized variance functions to develop three sets of analytic tools for the evaluation of these patterns of variability. These tools are for (1) identification of predictor variables that explain some of the observed temporal and cross-sectional variability in published standard errors; (2) evaluation of the proportion of variability attributable to the abovementioned predictors, equation error and estimation error, respectively; and (3) comparison of equation error variances across groups defined by observable predictor variables. The primary ideas are motivated and illustrated by an application to the U.S. Current Employment Statistics program.
This paper analyses the volatility of retail fuel prices in nine different EU countries and the spillover effects between fuel prices across selected countries from Central and Eastern Europe and the Eurozone over the 2008-2019 period. In particular, we use the GARCH-GJR model in order to investigate fuel price volatility and identify potential asymmetric dynamics. Moreover, in order to assess the links between fuel prices across countries, we estimate a VAR model and compute spillover measures using the Generalised Forecast Error Variance Decomposition (GFEVD) approach formulated by Diebold and Yilmaz (2009). Our results provide evidence of weak links between retail fuel prices across EU countries, with slightly higher spillovers originating from some developed economies such as France and Italy.
The paper analyses the influence of oil price volatility on Exchange Rate Variability, External Reserves, Government Expenditure and real Gross Domestic Product using the methodology of Vector Auto-Regressive (VAR) to carry out regression analysis, impulse response function and factor error variance decomposition for robust policy recommendations. The results of the research show that unstable oil price exerts varying degrees of deleterious effect on exchange rate variability, external reserves, Government expenditure and real gross domestic product (GDP). Based on the findings of the study, we recommend the need for the country to branch out its revenue sources. This will further shield the dangle effect of the fluctuation in prices of oil. Serious policy attention should be attached to agricultural reformation, industrial policy drives, mines and mineral development to diversify Nigeria’s economy following the downward slide in the oscillations in oil prices to address the problem of excessive dependence on crude oil exportation. This will help to achieve sustainable growth and development in Nigeria.