Steady state adjusting trends using a data-driven local polynomial regression

Economic variables usually follow a dynamic trend pattern. However, it is difficult to estimate this trend precisely as numerous economically- and statistically-based estimation methods exist. This contribution proposes a data-driven nonparametric trend that is local polynomial, to improve arbitrary trend estimations of commonly used methods concerning the selection of the smoothing parameter and the dependence structure. An iterative plug-in (IPI) algorithm determines the bandwidth endogenously and allows a theory-based interpretation of the length of growth processes. This length of the bandwidth reflects the lengths of the steady state periods. Consequently, the bandwidth identifies the time period of stable economic conditions and can detect economic changes. To demonstrate the power of this estimation approach, an extensive simulation study is performed. Furthermore, examples using US and UK GDP data along with a guide for the optimal choice of algorithms for empirical applications are provided. This proposed method yields new insights for growth dynamics, cyclical movements and their dependence.     

Optimal dividend strategies with time-inconsistent preferences

This paper studies the optimal dividend strategies of an insurance company when the manager has time-inconsistent preferences. We consider the problem for a naive manager and a sophisticated manager, and analytically derive the optimal dividend strategies when claim sizes follow an exponential distribution. Our results show that the manager with time-inconsistent preferences tends to pay out dividends earlier than her time-consistent counterpart and that the sophisticated manager is more inclined to pay out dividends than the naive manager. Furthermore, we extend these results to the case with claim sizes following a mixed exponential distribution, and provide a numerical analysis to reveal the sensitivity of the optimal dividend strategies to changes in the premium, claims and surplus volatility.     

Are impatient farmers more risk-averse? Evidence from a lab-in-the-field experiment in rural Uganda

Based on data from a field-experiment in rural Uganda, we show that impatient farmers are more risk-averse than patient farmers. We relied on a simplified version of the Convex Time Budget (CTB) method to elicit farmers’ time preferences and on an independent method for eliciting their risk-preferences. We report two important findings. First, we show that our simplified CTB method applied to farmers from Uganda replicates the key findings of Andreoni and Sprenger’s lab experiments that involved student subjects. Second, we establish the existence of a negative correlation between risk tolerance and impatience, based on two independent measures.     

Food preferences as a proxy for adventurousness

Food preferences have been demonstrated to be a proxy measure for adventurousness, in general. This study demonstrates that a single variable of food preferences unveils the four dimensions of adventurousness that are applicable in a general tourism context. The findings suggest that adding this simple semantic differential question to visitor surveys can be a powerful tool to evaluate the critical factor of adventurousness in tourism.     

Correlated daily time series and forecasting in the M4 competition

We participated in the M4 competition for time series forecasting and here describe our methods for forecasting daily time series. We used an ensemble of five statistical forecasting methods and a method that we refer to as the correlator. Our retrospective analysis using the ground truth values published by the M4 organisers after the competition demonstrates that the correlator was responsible for most of our gains over the naïve constant forecasting method. We identify data leakage as one reason for its success, due partly to test data selected from different time intervals, and partly to quality issues with the original time series. We suggest that future forecasting competitions should provide actual dates for the time series so that some of these leakages could be avoided by participants.     

Understanding on-the-go consumption: A retail mix perspective

On-the-go (OTG) consumption is a growing phenomenon in the food and beverage industry. Drawing on the theory of consumption values, this study is conducted to acquire a better understanding of the influence that retail-mix elements have on OTG consumption outcomes. Specifically, this study examines the role of value for money, customer service, general assortments and healthy assortments in driving intention and satisfaction related to OTG consumption. The study also assesses the conditional value generated by the consumer's health orientation, impulsiveness and perceived time pressure. Based on a sample of 433 OTG consumers, and applying multi-group structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), value for money was identified as the key driver of OTG consumption outcomes. Impulsiveness and time pressure are shown to increase the effects of value for money perceptions on OTG consumption intention, while health orientation enhances the relationship between customer service and satisfaction. This study contributes to the literature on OTG consumption, explains the managerial implications for retailers, and offers recommendations to target OTG consumers better.     

The moderating role of demographic variables on customer expectations in airport retail patronage intentions of travellers

The international airport retail business is expanding annually, yet most research into traveller’s experience with the aviation industry have centred on aeronautical features. The study gathered information that would guide the understanding of airport customer retail expectations. Specifically, the study examines the effects of product-relevant factors, market-relevant factors, and perceived service quality on retail patronage intentions, taking into account the moderating role of demographic variables. The purposive sample of three hundred and thirty (330) travellers that were selected at the Kotoka International Airport Terminal 1 and 2 (KIA T1&2) in Accra, the capital of Ghana. The findings show that product-relevant factors, market-relevant factors and overall service quality significantly influence airport retail patronage intention. The effect demographic variables was noticeable among older and high-income consumers. Building on previous studies, we find that product-relevant factors, market-relevant factors, and perceived service quality function as viable signals that drives customer expectations.     

Food beliefs and food supply chains: The impact of religion and religiosity in Israel

This paper demonstrates that religion and religiosity affect norms, which affect food consumption patterns and production. Heterogeneity and asymmetric information lead to multiple certification channels as well as multiple supply chains. Major supply chains may address multiple constituencies that are secular or less religious. Technological change affects norms and thus the food system. We obtain these results by analyzing the food systems for meat products in Israel where there are three religions – Jews, Muslims, and Christians – and people assign themselves three levels of religiosity – secular, conservative, and orthodox. Israel has multiple Kosher and Halal certifiers and several specialized supermarket chains for orthodox groups. Its main supermarket chains serve secular and some conservative segments. The immigration of secular Jews from Russia led to the proliferation of non-Kosher supply chains and products, and increased consumption of pork. New technologies and higher incomes led to emergence of fast food chains serving orthodox Jews that had previously tended to eat at home.     

The not-so-generalised effects of the Generalized System of Preferences

We use an empirical gravity equation to study how non-reciprocal trade preferences (NRTPs), enacted mainly through the Generalized System of Preferences, affect the exports of the beneficiary nations. In line with existing studies, the average trade effect stemming from non-reciprocal preferences is highly unstable across specifications. However, once we allow for heterogeneous effects, results become robust and economically important. Specifically, NRTPs have a strong effect on the exports of beneficiaries when they are members of the World Trade Organization and are very poor. Not-so-poor beneficiaries also expand foreign sales, but only if they are not WTO members. For all others, the average export effects of NRTPs are mute.     

Markov-switching dynamic factor models in real time

We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as mixed sampling frequencies and ragged-edge data. First, we evaluate the theoretical gains of using data that are available promptly for computing probabilities of recession in real time. Second, we show how to estimate the model that deals with unbalanced panels of data and mixed frequencies, and examine the benefits of this extension through several Monte Carlo simulations. Finally, we assess its empirical reliability for the computation of real-time inferences of the US business cycle, and compare it with the alternative method of forecasting the probabilities of recession from balanced panels.