Current Account Deficit in Mauritius: Risks and Prospects

Mauritius is often cited by international institutions, including the International Monetary Fund and World Bank, as a success story in economic development. The island has, since the early 1970s, adopted an export‐led growth strategy to power its economy. However, a constant decline over the last decade in the exports to gross domestic product (GDP) ratio has resulted in a worsening current account to GDP ratio, which is now a cause for concern. Using a three‐regime, self‐exciting threshold autoregressive (SETAR) model, this paper finds that the Mauritian economy may converge to either of two current account equilibria, namely a deficit of 9% or a surplus of 2.5% on a seasonally adjusted basis. A dynamic simulation exercise suggests that the Mauritian current account is more likely to switch from surplus to deficit equilibrium than from deficit to surplus equilibrium. Given that the prevailing deficit is in the vicinity of the deficit equilibrium, structural policies aiming to boost productivity and efficiency are indispensable for pulling Mauritius out of the “deficit trap,” the more so since the island has been experiencing a continuous erosion of trade preferences, which formerly enabled it to have privileged access for its exports to the EU market.     

The causal interaction between financial development and human development in Bangladesh

This note investigates the causal relationship between financial development measured as the domestic credit to private sector (% of GDP) and human development measured by the Barro–Lee index in Bangladesh. The bootstrap causality tests with leverage adjustments are implemented in order to avoid any distributional assumption. It is found that human development is causing financial development. However, there is no significant causality running from financial development on human development. The policy implication of these empirical findings is also elaborated.     

BOOTSTRAPPING COVARIATE UNIT ROOT TESTS: AN APPLICATION TO INFLATION RATES

This paper proposes a bootstrap procedure for the covariate point optimal tests (CP_T) of Elliott and Jansson. Although the covariate tests enjoy large power gains over the traditional univariate unit root tests, our simulations show that they still suffer from severe size distortions at finite samples. Through simulations, we demonstrate the superiority of the bootstrap procedure in the sense that it can yield desirable size and power properties for the CP_T tests when the Akaike's information criterion is used. Moreover, we show the empirical relevance of the bootstrap tests by applying them to inflation in the G‐10 countries, and then obtain strong evidence against the unit root hypothesis for most countries at the 5% significance level.     

Measuring the degree of market power in the Greek manufacturing industry

This paper investigates the competitive conditions in the Greek manufacturing industry, estimates the net and the total welfare losses due to the possible existence of market power and investigates factors affecting the market power at sectoral level and over time. The bootstrap method is applied to assign measures of accuracy to the statistical estimates. The empirical results imply the presence of imperfect competition in the Greek manufacturing industry and the existence of welfare losses. Furthermore, the findings indicate that labor intensity, the sector size and the openness influence the market power at the sectoral level and labor intensity, while the number of firms and the openness affect the market power over time.     

An Evaluation of Testing Procedures for Long Horizon Event Studies

We conduct a comprehensive simulation study to evaluate testing procedures for long horizon event studies. The simulation results raise the following concerns about some popular practices: (1) using the four-factor model that includes the Fama-French three factors and a momentum-related factor causes serious over rejection of the null hypothesis; (2) using reference portfolios as benchmark tends to overestimate event firms' long-term returns; and (3) the computation-intensive bootstrap test has low power for long event horizons. Moreover, unless the number of event firms in a study is very large, all testing procedures suffer substantial loss of power quickly as event horizon increases, especially for samples of small firms. Of particular interest, the combination of the nonparametric sign test with a single firm benchmark shows the best performance consistently in our simulations.     

Improving Tests of Abnormal Returns by Bootstrapping the Multivariate Regression Model with Event Parameters

Parametric dummy variable-based tests for event studies usingmultivariate regression are not robust to nonnormality of theresidual, even for arbitrarily large sample sizes. Bootstrapalternatives are described, investigated, and compared for caseswhere there are nonnormalities, and cross-sectional and time-seriesdependencies. Independent bootstrapping of residual vectorsfrom the multivariate regression model controls type I errorrates in the presence of cross-sectional correlation, and surprisingly,even in the presence of time-series dependence structures. Theproposed methods not only improve upon parametric methods, butalso allow development of new and powerful event study testsfor which there is no parametric counterpart.     

Bootstrap unit root tests in panels with cross-sectional dependency

We apply bootstrap methodology to unit root tests for dependent panels with N cross-sectional units and T time series observations. More specifically, we let each panel be driven by a general linear process which may be different across cross-sectional units, and approximate it by a finite order autoregressive integrated process of order increasing with T. As we allow the dependency among the innovations generating the individual series, we construct our unit root tests from the estimation of the system of the entire N cross-sectional units. The limit distributions of the tests are derived by passing T to infinity, with N fixed. We then apply bootstrap method to the approximated autoregressions to obtain critical values for the panel unit root tests, and establish the asymptotic validity of such bootstrap panel unit root tests under general conditions. The proposed bootstrap tests are indeed quite general covering a wide class of panel models. They in particular allow for very general dynamic structures which may vary across individual units, and more importantly for the presence of arbitrary cross-sectional dependency. The finite sample performance of the bootstrap tests is examined via simulations, and compared to that of commonly used panel unit root tests. We find that our bootstrap tests perform relatively well, especially when N is small.     

Model selection for nonlinear time series

We investigate the finite-sample performance of model selection criteria for local linear regression by simulation. Similarly to linear regression, the penalization term depends on the number of parameters of the model. In the context of nonparametric regression, we use a suitable quantity to account for the Equivalent Number of Parameters as previously suggested in the literature. We consider the following criteria: Rice T, FPE, AIC, Corrected AIC and GCV. To make results comparable with other data-driven selection criteria we consider also Leave-Out CV. We show that the properties of the penalization schemes are very different for some linear and nonlinear models. Finally, we set up a goodness-of-fit test for linearity based on bootstrap methods. The test has correct size and very high power against the alternatives investigated. Application of the methods proposed to macroeconomic and financial time series shows that there is evidence of nonlinearity.First version received: September 2002/Final version received : October 2003I would like to thank Cees Diks, Cars Hommes and an anonymous referee for useful comments that significantly improved the paper.     

A re-examination of international portfolio diversification based on evidence from leveraged bootstrap methods

This article investigates the issue of international portfolio diversification with respect to the three largest financial markets in the world—namely the US, Japan and the UK. In addition to making use of traditional portfolio analysis, we also suggest a procedure to calculate bootstrap correlation coefficients that can take into account the dynamic structure between the markets as measured by bootstrapped causality tests. Weekly data is used. The results from the first approach are supporting international diversification. The bootstrapped causality tests provide additional empirical support for this conclusion since the size of the causal effects is negligible and the bootstrap correlations are similar as the standard ones.     

Testing for random effects in panel models with spatially correlated disturbances

In the empirical analysis of panel data the Breusch–Pagan (BP) statistic has become a standard tool to infer on unobserved heterogeneity over the cross-section. Put differently, the test statistic is central to discriminate between the pooled regression and the random effects model. Conditional versions of the test statistic have been provided to immunize inference on unobserved heterogeneity against random time effects or patterns of spatial error correlation. Panel data models with spatially correlated error terms are typically set out under the presumption of some known adjacency matrix parameterizing the correlation structure up to a scaling factor. This paper delivers a bootstrap scheme to generate critical values for the BP statistic allowing robust inference under misspecification of the adjacency matrix. Moreover, asymptotic results are derived for the case of a finite cross-section and infinite time dimension. Finite sample simulations show that misspecification of spatial covariance features could lead to large size distortions, while the robust bootstrap procedure retains asymptotic validity.