Revenue Mobilization in African Countries: Does Natural Resource Endowment Matter?*

Abstract: Natural resource endowment offers great opportunities for achieving high levels of growth and development, notably via fiscal revenue mobilization throughout the entire chain of operations from exploration to production to exports. In the case of African countries, however, resource‐rich countries have not yet been able to take full advantage of their resource wealth to mobilize government revenue. In fact it appears that they have often been outperformed by their resource‐scarce counterparts in this regard. Is the low revenue performance a result of distorted incentives induced by the natural resource bonanza or the lack of capacity to harness the revenue potential from the natural resource industry? This paper explores these questions and provides some empirical evidence based on data from a sample including African countries as well as countries from Latin America, Asia, and the Middle East for the period 1980–2007. The paper undertakes an econometric analysis to examine the factors that determine revenue performance in African countries from a comparative perspective, with a focus on the role of natural resource endowment. The results are consistent with the evidence from the literature, especially with regard to the role of economic structure (notably the share of agriculture in GDP), the tax base (per capita income), and trade. We compute an index of revenue performance that relates the actual revenue to the level predicted by the econometric model and we find that African resource‐rich countries have performed poorly relative to their resource‐scarce counterparts and compared to the oil‐rich Middle Eastern countries. The paper concludes with some policy implications for African countries.     

What drives international equity correlations? Volatility or market direction?

We consider impulse response functions to study the impact of both return and volatility on the correlation between international equity markets. Using data on the US (as the reference country), Canada, the UK and France equity indices, empirical evidence shows that without taking into account the effect of return, there is an (asymmetric) effect of volatility on correlation. The volatility seems to have an impact on correlation especially during downturn periods. However, once we introduce the effect of return, the impact of volatility on correlation disappears. These observations suggest that, the relation between volatility and correlation is an association rather than a causality. The strong increase in the correlation is driven by the past of the return and the market direction rather than the volatility.     

Further results on projection-based inference in IV regressions with weak,collinear or missing instruments

We study a general family of Anderson–Rubin-type procedures, allowing for arbitrary collinearity among the instruments and endogenous variables. Using finite-sample distributional theory, we show that the proposed procedures, besides being robust to weak instruments, are also robust to the exclusion of relevant instruments and to the distribution of endogenous regressors. A solution to the problem of computing linear projections from general possibly singular quadric surfaces is derived and used to build finite-sample confidence sets for individual structural parameters. The importance of robustness to excluded instruments is studied by simulation. Applications to the trade-growth relationship and to education returns are presented.     

Portfolio selection in a data-rich environment

We model portfolio weights as a function of latent factors that summarize the information in a large number of economic variables. This approach (hereafter diffusion index approach) offers the opportunity to exploit a much richer information base to improve portfolio selection. We use factor analysis to estimate the space spanned by the factors. This provides consistent estimates for the optimal weights as the number of economic variables and sample size go to infinity. We consider an empirical application to illustrate the practical usefulness of our approach. The results indicate that the diffusion index approach helps to improve the portfolio performance.     

Short and long run causality measures: Theory and inference

The concept of causality introduced by Wiener [Wiener, N., 1956. The theory of prediction, In: E.F. Beckenback, ed., The Theory of Prediction, McGraw-Hill, New York (Chapter 8)] and Granger [Granger, C. W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods, Econometrica 37, 424–459] is defined in terms of predictability one period ahead. This concept can be generalized by considering causality at any given horizon h$h$ as well as tests for the corresponding non-causality [Dufour, J.-M., Renault, E., 1998. Short-run and long-run causality in time series: Theory. Econometrica 66, 1099–1125; Dufour, J.-M., Pelletier, D., Renault, É., 2006. Short run and long run causality in time series: Inference, Journal of Econometrics 132 (2), 337–362]. Instead of tests for non-causality at a given horizon, we study the problem of measuring causality between two vector processes. Existing causality measures have been defined only for the horizon 1, and they fail to capture indirect causality. We propose generalizations to any horizon h$h$ of the measures introduced by Geweke [Geweke, J., 1982. Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77, 304–313]. Nonparametric and parametric measures of unidirectional causality and instantaneous effects are considered. On noting that the causality measures typically involve complex functions of model parameters in VAR and VARMA models, we propose a simple simulation-based method to evaluate these measures for any VARMA model. We also describe asymptotically valid nonparametric confidence intervals, based on a bootstrap technique. Finally, the proposed measures are applied to study causality relations at different horizons between macroeconomic, monetary and financial variables in the US.     

Portfolio risk management in a data-rich environment

We study risk assessment using an optimal portfolio in which the weights are functions of latent factors and firm-specific characteristics (hereafter, diffusion index portfolio). The factors are used to summarize the information contained in a large set of economic data and thus reflect the state of the economy. First, we evaluate the performance of the diffusion index portfolio and compare it to both that of a portfolio in which the weights depend only on firm-specific characteristics and an equally weighted portfolio. We then use value-at-risk, expected shortfall, and downside probability to investigate whether the weights-modeling approach, which is based on factor analysis, helps reduce market risk. Our empirical results clearly indicate that using economic factors together with firm-specific characteristics helps protect investors against market?risk.     

Moments of multivariate regime switching with application to risk-return trade-off

We use a Fourier transform to derive multivariate conditional and unconditional moments of multi-horizon returns under a regime-switching model. These moments are applied to examine the relevance of risk horizon and regimes for buy-and-hold investors. We analyze the impact of time-varying expected returns and risk (variance and covariance) on portfolio allocations' “term structure”—portfolio allocations as a function of the investment horizon. Using monthly observations on S&P composite index and 10-year Government Bond, we find that the term structure of the optimal allocations depends on market conditions measured by the probability of being in bull state. At short horizons and when this probability is low, buy-and-hold investors decrease their holdings of risky assets. We also find that the conditional optimal portfolio performs quite well at short and intermediate horizons and less at long horizons.     

Partial Structural Break Identification

We propose an extension of the existing information criterion‐based structural break identification approaches. The extended approach helps identify both pure structural change (break) and partial structural change (break). A pure structural change refers to the case when breaks occur simultaneously in all parameters of regression equation, whereas a partial structural change happens when breaks occur in some parameters only. Our approach consistently outperforms other well‐known approaches. We also extend the simulation studies of Bai and Perron ( 2006 and Hall, Osborn and Sakkas ( 2013 ) by including more general cases. This provides more comprehensive results and reveals the cases where the existing identification approaches lose power, which should be kept in mind when applying them.     

Analytical Value-at-Risk and Expected Shortfall under regime-switching

It is well known that the use of Gaussian models to assess financial risk leads to an underestimation of risk. The reason is because these models are unable to capture some important facts such as heavy tails and volatility clustering which indicate the presence of large fluctuations in returns. An alternative way is to use regime-switching models, the latter are able to capture the previous facts. Using regime-switching model, we propose an analytical approximation for multi-horizon conditional Value-at-Risk and a closed-form solution for conditional Expected Shortfall. By comparing the Value-at-Risks and Expected Shortfalls calculated analytically and using simulations, we find that the both approaches lead to almost the same result. Further, the analytical approach is less time and computer intensive compared to simulations, which are typically used in risk management.     

A Better Understanding of Granger Causality Analysis: A Big Data Environment

This paper aims to provide a better understanding of the causal structure in a multivariate time series by introducing several statistical procedures for testing indirect and spurious causal effects. In practice, detecting these effects is a complicated task, since the auxiliary variables that transmit/induce indirect/spurious causality are very often unknown. The availability of hundreds of economic variables makes this task even more difficult since it is generally infeasible to find the appropriate auxiliary variables among all the available ones. In addition, including hundreds of variables and their lags in a regression equation is technically difficult. The paper proposes several statistical procedures to test for the presence of indirect/spurious causality based on big data analysis. Furthermore, it suggests an identification procedure to find the variables that transmit/induce the indirect/spurious causality. Finally, it provides an empirical application where 135 economic variables were used to study a possible indirect causality from money/credit to income.