Dynamique des Inégalités de Revenu au Togo entre 2006 et 2015

Ce papier porte sur l'analyse de la dynamique des inégalités de revenu au Togo. En se fondant sur les données des enquêtes QUIBB 2006, 2011 et 2015, et en utilisant la méthode de décomposition d'Oaxaca‐Blinder pour les estimations économétriques, les résultats montrent une augmentation des inégalités de revenu entre 2006 et 2015. Les résultats révèlent en outre une augmentation des inégalités sur la même période si on considère le genre et le milieu de résidence. Cependant, il se dégage que l'âge, le milieu de résidence, le niveau d'éducation sont les facteurs qui expliquent significativement les inégalités de revenu entre les hommes et les femmes d'une part et que le niveau d'éducation, la taille du ménage et la région de résidence expliquent significativement les inégalités de revenu entre le milieu urbain et rural d'autre part. Nous utilisons par ailleurs le modèle DER pour capter l'effet de la part du revenu affectée aux différentes consommations des ménages sur les inégalités globales. Les résultats montrent que la part du revenu affecté aux dépenses alimentaires et non alimentaires constituent l'essentiel des sources des inégales entre les ménages.     

Forecasting the price of gold

This article seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate and statistically significant forecasts for gold price. We report the results from the nine most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the random walk (RW) as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the RW in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the RW at horizons of 1 and 9 steps ahead, and on average, the exponential smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24-month forecasting horizons. Moreover, we find that the univariate models used in this article are able to outperform the Bayesian autoregression and Bayesian vector autoregressive models, with exponential smoothing reporting statistically significant results in comparison with the former models, and classical autoregressive and the vector autoregressive models in most cases.     

Forecasting market risk of portfolios: copula-Markov switching multifractal approach

This paper proposes a new methodology for modeling and forecasting market risks of portfolios. It is based on a combination of copula functions and Markov switching multifractal (MSM) processes. We assess the performance of the copula-MSM model by computing the value at risk of a portfolio composed of the NASDAQ composite index and the S&P 500. Using the likelihood ratio (LR) test by Christoffersen [1998. “Evaluating Interval Forecasts.” International Economic Review 39: 841–862], the GMM duration-based test by Candelon et al. [2011. “Backtesting Value at Risk: A GMM Duration-based Test.” Journal of Financial Econometrics 9: 314–343] and the superior predictive ability (SPA) test by Hansen [2005. “A Test for Superior Predictive Ability.” Journal of Business and Economic Statistics 23, 365–380] we evaluate the predictive ability of the copula-MSM model and compare it to other common approaches such as historical simulation, variance–covariance, RiskMetrics, copula-GARCH and constant conditional correlation GARCH (CCC-GARCH) models. We find that the copula-MSM model is more robust, provides the best fit and outperforms the other models in terms of forecasting accuracy and VaR prediction.     

Mobile money adoption and households’ vulnerability to shocks: Evidence from Togo

ABSTRACT

We investigate the determinants of mobile money adoption process and whether its use helps households in Togo to be resilient to predictable and unpredictable life events. Using ordered logit and sequential logit models, our results show that in the adoption process, households benefit from weak ties of social groups such as religious group and informal saving group for the adoption of mobile money. We equally find that being client of banks or microfinance institutions act as powerful channels from one step to another in the process. Besides, our findings reveal that households whoever use mobile money seem to be more resilient to climatic shocks such as drought, irregular rain, soil degradation, erosion and fertility reduction and to shock that affect households’ assets (non-climatic: high prices of agricultural inputs). However, the picture is more contrasted when the individuals are classified by disadvantaged groups such as rural people, women, less educated and people with low incomes.