Philip Hans Franses’s research while affiliated with Erasmus University Rotterdam and other places

This paper proposes to use factor models with cluster structure to forecast growth rates of house prices in the US. We assume the presence of global and cluster-specific factors and that the clustering structure is unknown. We adopt a computational procedure that automatically estimates the number of global factors, the clustering structure and the number of clustered factors. The procedure enhances spatial clustering so that the nature of clustered factors reflects the similarity of the time series in the time domain and their spatial proximity. Considering house prices in 1975–2023, we highlight the existence of four main clusters in the US. Moreover, we show that forecasting approaches incorporating global and cluster-specific factors provide more accurate forecasts than models using only global factors and models without factors.

This paper puts forward a new and simple method to combine forecasts, which is particularly useful when the forecasts are strongly correlated. It is based on the Mincer Zarnowitz regression, and a subsequent determination using Shapley values of the weights of the forecasts in a new combination. For a stylized case, it is proved that such a Shapley‐value‐based combination improves upon an equal‐weight combination. Simulation experiments and a detailed illustration show the merits of the Shapley‐value‐based forecast combination.

This paper introduces a simple and reproducible method to modify model forecasts using expert forecasts which is useful in crisis times. The idea is to add the expert forecast as an additional observation of the dependent variable, and to extend the model with an additional explanatory variable such as the square of a deterministic trend. Next, the new model forecast is combined with the expert forecast using equal weights. We show that it works well for gross domestic product growth forecasts for 2020 for 12 countries and that it improves upon an equal‐weighted combination of the original model forecast and expert forecast.

Two key features of airline departure delays are that they cascade and that there can be exceptional peaks. We model these features using an intensity‐based Hawkes process. Our application to all KLM departure delays at Amsterdam Schiphol airport in January 2015 shows that volatility in departure delays is endogenous. We correlate the key parameters of the estimated Hawkes process with daily weather conditions and find that these conditions amplify the self‐exciting feature of departure delays.

Forecasting inflation may be challenging, especially when inflation is high. Over the past decades, many developing countries have faced, and some are currently facing high inflation. For these countries, it is challenging to have predictive inflation accuracy. This paper presents a mixed-data sampling (MIDAS) method to model and forecast inflation in Suriname. We use the weekly money supply from the central bank’s balance sheet as an explanatory variable. We apply this method for forecasting inflation for Suriname, where average inflation in the 1990s exceeded 350%. The results of the MIDAS models with annual inflation data show large variations in forecasts. Some of these models include money supply as an explanatory variable. We assess the models’ forecasting performance based on the forecast error. The MIDAS models lead to a substantial improvement in forecast accuracy, also for the years with high inflation. We show that our method is particularly relevant for forecasting high inflation rates.

This paper investigates the business cycle synchronization in Africa, which is important for the definition of optimal monetary unions. Previous studies adopted either a cross-sectional or a time series approach, and this may have led to substantial variation in the findings for common business cycles across African countries. Considering fifty-two African economies, we study business cycles’ synchronization with a novel spatio-temporal hierarchical approach founded on the gravity model. The introduction of the spatial dimension is particularly relevant for Africa, where infrastructure constraints make monetary unions less feasible due to higher trading costs. We find positive evidence for the Economic Community of West African States (ECOWAS) and the Common Monetary Area (CMA) feasibility, while our results do not support the Central Africa CFA and the East African Monetary Union (EAMU) monetary unions.

Entrepreneurship and innovation create a positive impact on the economy and society. Globally, governments invest resources to support new ventures and facilitate innovation. In this study, we examine this phenomenon by studying the pathway that goes from individual entrepreneur to innovation impact. We measure the effect of entrepreneurial motives on different types of innovations, with a particular focus on its amplification by formal and informal institutional conditions. Specifically, we use multi-level models to analyze annual data of 29 countries for 2006 to 2018. We find that opportunity-driven entrepreneurs are associated with higher levels of radical innovation, breakthrough innovation, and disruptive innovation. Better tax policies and less bureaucracy amplify this positive effect on radical innovation and breakthrough innovation. For necessity-driven entrepreneurs, the regulatory quality amplifies the positive effect on radical innovation and disruptive innovation, while monetary resources dampen this effect. Our findings show that the differences in innovation impact can be explained by differences in entrepreneurial motives and their specific interactions with formal and informal institutional conditions.

The parallel market nominal exchange rate of the United States dollar vis‐à‐vis the Surinamese dollar (USD/SRD) exhibited periods of severe volatility which were often followed by episodes of stability, usually at a cost of sharp depreciations. This study seeks to model this exchange rate using autoregressive conditional duration models. These models are suitable for modelling events occurring with irregular intervals. Exchange rates in developing countries have distinct features compared to exchange rates in countries with well‐established and accessible financial markets. A key feature is that for these developing countries, exchange rates only occasionally experience jumps. Our findings suggest that past exchange rate changes appear to be a significant driver of future exchange rate jumps. Furthermore, our results show that money, international reserves, and commodity prices can explain jumps in the market USD/SRD exchange rate.

We analyze the effectiveness of the legislative framework of the maritime industry developed by the International Maritime Organization (IMO), the International Labor Organization (ILO) and regional regulators and industry. With a unique combination of 310 time series using data of 44 years (1977–2020), we use 41 econometric models to highlight the effect of 116 main legislative milestones and their variants. We consider multiple endpoints of interest such as safety, piracy and crime, oil and chemical pollution, biosecurity, and air emissions. The results demonstrate that qualities of the fleet have improved. The time between adoption and entry into force lies at 3.83 years on average while negative effects are equally important for the time between adoption and entry into force as compliance often is obtained prior to entry into force of a requirement. Safety related milestones lead with measured negative effects followed by pollution and good results are found for the fishing fleet. Regional measures show stronger negative effects due to increased enforcement powers. Many of the milestones of interest show the desired effect of reducing incidents or decreasing pollution including the IMO International Safety Management Code, the Instruments Implementation Code, the Recognized Organization Code (RO), several SOLAS specific codes dealing with stability, fire testing and construction, the IMO Member States Audit Scheme, MARPOL Annex I, II and VI. Although human related areas improved compared to a previous study, there is still room for improvement.