Financial Resilience, Income Dependence and Organisational Survival in UK Charities

The financial well-being of the charity sector has important social implications. Numerous studies have analysed whether the concentration of income in a few sources increases financial vulnerability. However, few studies have systematically considered whether the type of income (grants, donation, fund-raising activities) affects the survival prospects of the charity. We extend the literature by (a) explicitly modelling the composition of sources of income, (b) allowing for short-term volatility as well as long-term survival and (c) testing alternative specifications in a nested form. We show that the usual association between income concentration per se and financial vulnerability is a specification error. Greater vulnerability is associated with dependence on grant funding, not overall concentration. Previous studies showing that concentration of income per se is problematic are picking up a proxy effect. We also show that the volatility of income streams may be an important factor in the survival of charities, but that this also varies between income sources.

Generalized ridge estimators adapted in structural equation models

Multicollinearity is detected via regression models, where independent variables are strongly correlated. Since they entail linear relations between observed or latent variables, the structural equation models (SEM) are subject to the multicollinearity effect, whose numerous consequences include the singularity between the inverse matrices used in estimation methods. Given to this behavior, it is natural to understand that the suitability of these estimators to structural equation models show the same features, either in the simulation results that validate the estimators in different multicollinearity degrees, or in their application to real data. Due to the multicollinearity overview arose from the fact that the matrices inversion is impracticable, the usage of numerical procedures demanded by the maximum likelihood methods leads to numerical singularity problems. An alternative could be the use of the Partial Least Squares (PLS) method, however, it is demanded that the observed variables are built by assuming a positive correlation with the latent variable. Thus, theoretically, it is expected that the load signals are positive, however, there are no restrictions to these signals in the algorithms used in the PLS method. This fact implies in corrective areas, such as the observed variables removal or new formulations of the theoretical model. In view of this problem, this paper aimed to propose adaptations of six generalized ridge estimators as alternative methods to estimate SEM parameters. The conclusion is that the evaluated estimators presented the same performance in terms of accuracy, precision while considering the scenarios represented by model without specification error and model with specification error, different levels of multicollinearity and sample sizes.

Meta-Analyses of Built Environment Effects on Travel: No New Platinum Standard

This commentary presents a critique of a particular, strictly quantitative way of reviewing research findings within the field of land use and transportation studies, so-called meta-analyses. Beyond criticism raised earlier, the article draws attention to serious bias resulting when meta-analysis include studies encumbered with model specification error due to poor understanding of causal mechanisms. The article also discusses underestimated limitations due to neglect of differences between geographical contexts and inconsistent measurement of variables across studies. An example of an alternative approach is offered at the end of the article.

A simple solution to the specification error

SummaryWe develop a simple method that completely eliminates the specification error and spurious relationships in regression. Furthermore, we introduce a stronger test of causality. We apply our method to oil prices.

The perfect regression and causality test: A solution to regression problems

Summary We introduce a method that eliminates the specification error and spurious relationships in regression. In addition, we introduce a test of strong causality. Furthermore, hypothesis testing (inference) becomes almost unneeded. Moreover, this method virtually resolves error problems such as heteroscedasticity, autocorrelation, non-stationarity and endogeneity.

Does public spending affect unemployment in an emerging market?

The Nigerian economy in the last two decades up until 2013 has been growing at an average of 6% and yet unemployment was equally growing in the region of 20% within the same period. This paradoxical situation has led to a flurry of studies and postulations aimed at providing explanation and solution to the phenomenon. This study making use of a regression model with annual data from 1980 to 2013, empirically determined the impact of public sector expenditures (CEXP and REXP) together with private sector investment (PINV) on unemployment (UNEMP) in Nigeria. Capital expenditure and private sector investment both in the medium to long-run were found to serve as catalyst towards reduction of unemployment, while recurrent expenditure was not statistically strong enough to do same. The R-2 (0.84) showed that greater proportion of the total variations in UNEMP was brought about by variations in the regressors. Further tests like autocorrelation, hetroscedasticity, specification error, and multicollinearity indicated respectively that there is no presence of autocorrelation hence the model produced a parsimonious result; the variance is constant over time; the link test confirmed by Ramsey reset test suggested there was no specification error; and lastly the variance inflation factor (VIF) of the variables implies that there is no evidence of multicollinearity. The study recommends, inter alia, that the proportion of capital expenditure in Nigerian budget profile should be systematically increased while the recurrent expenditure should be reduced; and there is need to stimulate competition among investors through removal of structural and institutional rigidities and government should design clear policy incentives to private sector investment.

A New High Order Fuzzy ARMA Time Series Forecasting Method by Using Neural Networks to Define Fuzzy Relations

Linear time series methods are researched under 3 topics, namely, AR (autoregressive), MA (moving averages), and ARMA (autoregressive moving averages) models. On the other hand, the univariate fuzzy time series forecasting methods proposed in the literature are based on fuzzy lagged (autoregressive (AR)) variables, having not used the error lagged (moving average (MA)) variables except for only two studies in the fuzzy time series literature. Not using MA variables could cause the model specification error in solutions of fuzzy time series. For this reason, this model specification error should be eliminated. In this study, a solution algorithm based on artificial neural networks has been proposed by defining a new high order fuzzy ARMA time series forecasting model that contains fuzzy MA variables along with fuzzy AR variables. It has been pointed out by the applications that the forecasting performance could have been increased by the proposed method in accordance with the fuzzy AR models in the literature since the proposed method is a high order model and also utilizes artificial neural networks to identify the fuzzy relation.