Search

Since the early 1990's, the International Maritime Organization (IMO) has designated fourteen sea areas as Particular Sensitive Sea Areas (PSSA) that enjoy special protection because of their various important attributes and vulnerability to potential harm by increasing shipping activities. The United Nations Educational, Scientific and Cultural Organization (UNESCO) has identified two possible sites for possible designation as PSSA under IMO: the Banc d'Arguin National Park (Mauritania) and the Tubbataha Reef National Park (Philippines). This article presents an integrated framework for the estimation of total risk exposure due to shipping activities and various risk measures for ships trading in the areas of interest. Using a unique and comprehensive combination of data, we test whether ship specific risk increased over time. The results confirm an increase in the considered risk measures of ships trading through or nearby West Africa (Banc d’Arguin) and South-East Asia (Tubbataha) in general and also close to both regions and therefore support the recommendation for an increased level of protection.

Efficiency of outbound warehouse operations depends on the management of demand forecasts and associated labor planning. A case study in consumer electronics shows that warehouse management systematically over-forecasts actual orders, by 3% on average and by 6-12% in busy periods (at the end of each month and also in the months September, October, and November). A time series model that corrects order forecasts for the biases in preceding weeks reduces the bias to less than 2%, both on average and also in busy periods. The arrangements with the labor provider imply potential benefits of intentional over-forecasting and the associated ample labor supply for the warehouse. As compared to under-forecasted days, labor productivity on over-forecasted days is higher by 12% for loading activities and by 4% for picking and total outbound activities. Similar productivity gains are found if unbiased forecasts are compared with the optimal bias obtained from non-linear models estimated from daily data on bias and labor efficiency. The positive effects of intentional over-forecasting on productivity are confirmed in a structural equations model. By following similar methodologies as described in this paper, warehouse managers can determine the amount of intentional forecast bias that works best for their situation. The information required for this kind of evidence-based labor management consists of historical data on order sizes, forecasts, and labor productivity, and the outcomes depend on the available hiring strategies and cost structures.

Port of Rotterdam Authority is a publicly owned but corporatized port development company. In 2004, this organisation was transformed from a municipal department to an independently operating company. The corporatisation intended to improve the overall performance of the port of Rotterdam. Relevant performance indicators to evaluate the effect of this corporatisation include market share, turnover, operating costs, profits, and investments. These indicators are evaluated for two periods, one prior to the corporatisation (1997-2003) and the other afterwards (2005-2011). The comparison of these two periods shows that corporatisation has led to significant performance improvements. This finding is relevant for the ongoing discussion on port governance models.

Recent dynamics in iron ore markets are driven by rapid changes in economic activities that affect commodity markets, trade flows, and shipping activities. Time series models for the relation between these variables in Southeast Asia and the Australasian region are supplemented with models for safety and pollution risk. Steel production in China, Japan, and South Korea is related to iron ore exports and vessel activity in Australia, with an estimated time lag of about two months. The Purchasing Manager Index, which is popular among traders as indicator of economic activity, is found to have predictive power both for steel production and for iron ore exports. The growth in economic activity and vessel movements is associated with significantly higher risks for ship accidents and pollution.

Safety management and risk profiling to identify substandard ships are of importance to the shipping industry. Whereas current methods rely heavily on detention risk and flag state performance, we extend the risk assessment by considering various risk dimensions and by evaluating a wide range of risk factors. Apart from detention risk, we consider also the risk of various types of accidents (total loss, very serious, and serious) and damage (hull and machinery, cargo, pollution, loss of life, and third party liabilities). Risk factors include ship particulars like ship type and classes of companies and owners, as well as historical information on past accidents, inspections, and changes of particulars. We present methods to summarize and visualize various risk dimensions and we pay particular attention to the identification of potentially risky companies. The empirical results are obtained by combining rich data sets with information on ship arrivals, inspections, and accidents for the period from 2006 to 2010. The presented methods and results are of interest to various stakeholders in the industry, such as charterers, insurance companies, maritime administrations, and the International Maritime Organization.

Pre-election polls can suffer from survey effects. For example, surveyed individuals can become more aware of the upcoming election so that they become more inclined to vote. These effects may depend on factors like political orientation and prior intention to vote, and this may cause biases in forecasts of election outcomes. We advocate a simple methodology to estimate the magnitude of these survey effects, which can be taken into account when translating future poll results into predicted election outcomes. The survey effects are estimated by collecting survey data both before and after the election. We illustrate our method by means of a field study with data concerning the 2009 European Parliament elections in the Netherlands. Our study provides empirical evidence of significant positive survey effects with respect to voter participation, especially for individuals with low intention to vote. For our data, the overall survey effect on party shares is small. This effect can be more substantial for less balanced survey samples, for example, if political orientation and voting intention are correlated in the sample. We conclude that pre-election polls that do not correct for survey effects will overestimate voter turnout and will have biased party shares.

Various ways of extracting macroeconomic information from a data-rich environment are compared with the objective of forecasting yield curves using the Nelson-Siegel model. Five issues in factor extraction are addressed, namely, selection of a subset of the available information, incorporation of the forecast objective in constructing factors, specification of a multivariate forecast objective, data grouping before constructing factors, and selection of the number of factors in a data-driven way. Our empirical results show that each of these features helps to improve forecast accuracy, especially for the shortest and longest maturities. The data-driven methods perform well in relatively volatile periods, when simpler models do not suffice.

Macroeconomic forecasting is not an easy task, in particular if future growth rates are forecasted in real time. This paper compares various methods to predict the growth rate of US Industrial Production (IP) and of the Composite Coincident Index (CCI) of the Conference Board, over the coming month, quarter, and half year. It turns out that future IP growth rates can be forecasted in real time from ten leading indicators, by means of the Composite Leading Index (CLI) or, even somewhat better, by principal components regression. This amends earlier negative findings for IP by Diebold and Rudebusch. For CCI, on the other hand, simple autoregressive models do not provide significantly less accurate forecasts than single-equation and bivariate vector autoregressive models with the CLI. This amends some of the more positive results for CCI recently reported by the Conference Board. Not surprisingly, all forecast methods improve considerably if `ex post' data are used, after possible data updates and revisions.

Immigration tends to have a mitigating effect on the socioeconomic gender gap among immigrants. To explain this finding, we propose a gender convergence hypothesis that states that migration to a modern ‘open’ society offers women the opportunity to improve their position relative to that of men. In such a society, there are (almost) equal chances to participate in education and paid labor. The equalizing effect will be larger if the immigrants come from less developed regions, since women then have more room to improve their position. However, there may also be countervailing cultural powers within the immigrant group. The gender convergence hypothesis proposed here is tested for immigrants in the Netherlands. Using survey data, we investigate the educational and labor market position of Turkish, Moroccan, Surinamese, and Antillean males and females. We find convergent trends, particularly among Moroccan immigrants who come from less developed regions in their country of origin and who meet less cultural in-group barriers than, for example, Turkish immigrants.

Pre-election polls can suffer from survey effects. For instance, individuals taking part in the poll may become more aware of the upcoming election so that they become more inclined to vote. Such effects cause biases in forecasted outcomes of elections. We propose a simple methodology that takes such survey effects explicitly into account when translating poll results into election outcomes. By collecting data both before and after the election, the survey effects can be estimated and used as correction factors in later polls. We illustrate our method by means of a field study with data collected before and after the 2007 regional elections (for `Provincial States') in the Netherlands. Our study provides empirical evidence of significant positive survey effects with respect to voter participation, and this effect is the largest for left-wing voters. That is, surveys seem to motivate left-wing people who otherwise would not have participated in the elections. This means that both the voter turnout and the number of seats going to left-wing parties may be overestimated by pre-election polls that do not correct for survey effects.

We propose a new method of leading index construction that combines the need for data compression with the objective of forecasting. This so-called principal covariate index is constructed to forecast growth rates of the Composite Coincident Index. The forecast performance is compared with an alternative index based on principal components and with the Composite Leading Index of the Conference Board. The results show that the new index, which takes the forecast objective explicitly into account, provides significant gains over other single-index methods, both in terms of forecast accuracy and in terms of predicting recession probabilities.

Direct extrapolation of survey results on purchase intentions may give a biased view on actual consumer behavior. This is because the purchase intentions of consumers may be affected by the survey itself. On the positive side, such effects can be incorporated in econometric models to get reliable estimates of actual behavior of non-surveyed consumers, which often is the ultimate purpose of survey studies. This paper proposes a reasonably simple methodology to correct for such possible survey effects and to get consistent pre- dictions beyond the survey sample. The potential merits of the method are illustrated by a supermarket survey on easy-to-prepare food products and related health issues. This indicates that the required corrections can be quite substantial and that predictions that neglect survey effects can be seriously biased indeed.

This paper is concerned with time series forecasting in the presence of a large number of predictors. The results are of interest, for instance, in macroeconomic and financial forecasting where often many potential predictor variables are available. Most of the current forecast methods with many predictors consist of two steps, where the large set of predictors is first summarized by means of a limited number of factors -for instance, principal components- and, in a second step, these factors and their lags are used for forecasting. A possible disadvantage of these methods is that the construction of the components in the first step is not directly related to their use in forecasting in the second step. This motivates an alternative method, principal covariate regression (PCovR), where the two steps are combined in a single criterion. This method has been analyzed before within the framework of multivariate regression models. Moti- vated by the needs of macroeconomic time series forecasting, this paper discusses two adjustments of standard PCovR that are necessary to allow for lagged factors and for preferential predictors. The resulting nonlinear estimation problem is solved by means of a method based on iterative majorization. The paper discusses some numerical aspects and analyzes the method by means of simulations. Further, the empirical per- formance of PCovR is compared with that of the two-step principal component method by applying both methods to forecast four US macroeconomic time series from a set of 132 predictors, using the data set of Stock and Watson (2005).

This article proposes a modified method for the construction of diffusion indexes in macroeconomic forecasting using principal component regres- sion. The method aims to maximize the amount of variance of the origi- nal predictor variables retained by the diffusion indexes, by matching the data windows used for constructing the principal components and for es- timating the diffusion index models. The method is applied to construct forecasts of eight monthly US macroeconomic time series, using the data set of Stock and Watson (2002a). The results show that the proposed method leads, on average, to simpler models with smaller forecast errors than previously used methods.

Forecasting with many predictors is of interest, for instance, in macroeconomics and finance. This paper compares two methods for dealing with many predictors, that is, principal component regression (PCR) and principal covariate regression (PCovR). The forecast performance of these methods is compared by simulating data from factor models and from regression models. The simulations show that, in general, PCR performs better for the first type of data and PCovR performs better for the second type of data. The simulations also clarify the effect of the choice of the PCovR weight on the orecast quality.

Estimation results in linear regression models are sometimes in contrast with what was expected on the basis of a certain set of hypotheses or theory, in the sense that one or more parameters have the "wrong sign". One could be inclined to think that this is due to collinearity across explanatory variables, suggesting one should leave out one or more of the collinear variables. In this note we show that this is not a valid approach. Additionally, we show that "wrong signs" can occur because of correlations between included and omitted variables, so that "wrong signs" may occur if the model is not correctly specified. That is, if we find 'wrong signs" we should start questioning our model choice, not the data.

The behavioural framework has several attractions to offer for the identification of multivariable systems. Some of the variables may be left unexplained without the need for a distinction between inputs and outputs; criteria for model quality are independent of the chosen parametrization; and behaviours allow for a global (i.e., non-local) approximation of the system dynamics. This is illustrated with a behavioural least squares method with an application in dynamic factor analysis.

Model estimates of the pay-off of the Betuwe railway have played an important role for politicians to decide to build this railway. This has already been discussed extensively, especially with respect to the macro-economic effects of this investment. A deeper analysis shows that these effects are very difficult to assess. The NIJFER institute for example calculated a long term profit of 52.1 biljon Dutch guilders, but our analysis of this model shows that the profit lays with 50 reliability between 15 and 30 biljon Dutch guilders. Our conclusion is that this political investment decision is not sufficiently supported by the expected macro-economic pay-off. A prudent use of quantitative research requires that the involved uncertainties in the model outcomes is properly taken into account.