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.     

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.     

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.     

An empirical investigation of water consumption forecasting methods

Many regions on earth face daily limitations in the quantity and quality of the water resources available. As a result, it is necessary to implement reliable methodologies for water consumption forecasting that will enable the better management and planning of water resources. This research analyses, for the first time, a large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas of Europe, which faces issues of droughts and overconsumption in the hot summer months. Using the R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting techniques including a machine-learning algorithm, with very promising results.     

Non-stationary additive utility and time consistency

Within a continuous time life cycle model of consumption and savings, I study the properties of the most general class of additive intertemporal utility functionals. They are not necessarily stationary, and do not necessarily multiplicatively separate a discount factor from “per-period utility”. I prove rigorously that time consistency holds if and only if the per-period felicity function is multiplicatively separable in $t$, the date of decision and in $s$, the date of consumption, or equivalently, if the Fisherian instantaneous subjective discount rate does not depend on $t$. The model allows to explain “anomalies in intertemporal choice” even when the agents are time consistent and various empirical regularities. On the other hand, the model allows to characterize mathematically the “effective consumption profile” of naive, time-inconsistent agents.     

Early labor market outcomes of university graduates: Does time to degree matter?

Using university administrative and survey data drawn from the AlmaLaurea Consortium, we analyze the effect of time to degree on the early labor market performance of Italian graduates. The empirical strategy allows identifying separately the impact of elapsed time to degree on the transition from university to work and on earnings from other determinants specific to the academic path completed. Findings suggest that delayed graduation reduces the employment probability (0.8% points for each year of delay), and this effect is still persistent five years after graduation. Once employed, graduates not completing their degree within the minimum period are also penalized in their net monthly earnings, even five years after graduation. The most penalized groups are women and graduates in non-scientific fields.     

A quantitative analysis of optimal sustainable monetary policies

This study examines the quantitative properties of optimal sustainable monetary policies using a monetary model with a stabilization bias. As in Kurozumi (2008), the optimal sustainable policy is a strategy considered in the absence of commitment technologies; however it is implemented following an optimal quasi-sustainable policy derived by assuming that the commitment technologies are present. This study finds that solving for the policy function of the optimal quasi-sustainable policy yields a result basically identical to the Ramsey-optimal commitment policy under a set of parameters commonly used in the literature. The simulation shows two further results: policymakers have incentive to deviate from the Ramsey-optimal commitment policy when the lagged output gap is large and the optimal quasi-sustainable policy endogenously diminishes the steadfastness of policymakers׳ commitment.     

Revisiting the matching function

There is strong empirical evidence for Cobb–Douglas matching functions. We show in this paper that this widely found relation between matches on the one hand and unemployment and vacancies on the other hand can be the result of different underlying mechanisms. Obviously, it can be generated by assuming a Cobb–Douglas matching function. Less obvious, the same relationship results from a vacancy free-entry condition and idiosyncratic productivity shocks. A positive aggregate productivity shock leads to more vacancy posting, a shift of the idiosyncratic selection cutoff and thereby more hiring. We calibrate a model with both mechanisms to administrative German labor market data and show that idiosyncratic productivity for new contacts is an important driver of the elasticity of the job-finding rate with respect to the market tightness. Accounting for idiosyncratic productivity can explain the observed negative time trend in estimated matching efficiency and asymmetric business cycle responses to large aggregate shocks.     

Do time constraint and emergency purchase situation exert same influence on shopping? A study under haptic touch influence

Marketing literature has identified emergency purchase situation and time constraint as important situational influences. However, both of them are often equated with each other as both require shopping to be done in a short time. This study makes an attempt to eliminate the confusion by studying the impact of two situation variables on product evaluation and purchase intention under the influence of haptic touch. Two studies conducted to test the hypotheses show that while emergency purchase situation has a significant impact under no-touch influence, the same can not be said for time constraint situation.