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This chapter defines what road safety advertising campaigns are and the objectives that they typically seek to achieve. The argument put forward in this chapter is that when theoretically informed in their design and sensitive to the array of potential personal, social, and cultural influences which may be at play, road safety advertising can contribute to both reinforcing and transforming contemporary traffic safety culture. This chapter offers guidance to researchers and practitioners in the field regarding relevant theory which may be applied to inform message design and evaluation.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

We compare environmental impacts associated with incoming foreign direct investment versus domestic capital in China. We use aggregate data on Chinese provinces’ economic and pollution indicators to explore the effects of the financial origin of fixed capital. Our simultaneous models consider three prime channels through which these effects work: economic scale, sectoral composition, and pollution intensity. Results show that emissions associated with foreign financed capital are lower than with domestically financed capital for some but not all of the considered types of pollution.

According to the market timing theory, firms try to take advantage of windows of opportunity to raise capital by exploiting temporary cost fluctuations of alternative financing sources. In this context, the main objective of this paper is to examine the influence and persistence of market timing in the financing decisions of Brazilian firms that launched IPOs in the period from 2001 to 2011.

We analyze the influence of past market values on the capital structure of these firms, based on the main models proposed by Baker and Wurgler (2002), adapted to reflect the characteristics of Brazilian firms’ financial statements.

We find evidence of market timing, but this behavior is not sufficiently persistent in the period studied to the point of determining these firms’ capital structure. We believe the fact that Brazilian companies rarely carried out follow-on primary equity issues after floating their capital in the period analyzed, due to the presence of more advantageous financing sources (particularly from the national development bank, BNDES), explains the results. Therefore, Brazilian firms appear to be pay heed to different funding sources, in search of windows of opportunity, to guide their financing decisions and determine their capital structures.

The Brazilian capital market has been developing intensely in recent years, making it increasingly relevant to analyze the financing and investment decisions of the country’s listed companies. The Brazilian literature on capital structure is extensive, but few works have addressed the issue of market timing.

This chapter investigates the performance of different dimension reduction approaches for large vector autoregressions in multi-step ahead forecasts. The authors consider factor augmented VAR models using principal components and partial least squares, random subset regression, random projection, random compression, and estimation via LASSO and Bayesian VAR. The authors compare the accuracy of iterated and direct multi-step point and density forecasts. The comparison is based on macroeconomic and financial variables from the FRED-MD data base. Our findings suggest that random subspace methods and LASSO estimation deliver the most precise forecasts.