Search results

Business cycle theory is normally described as having evolved out of a previous tradition of writers focusing exclusively on crises. In this account, the turning point is seen as residing in Clément Juglar's contribution on commercial crises and their periodicity. It is well known that the champion of this view is Schumpeter, who propagated it on several occasions. The same author, however, pointed to a number of other writers who, before and at the same time as Juglar, stressed one or another of the aspects for which Juglar is credited primacy, including the recognition of periodicity and the identification of endogenous elements enabling the recognition of crises as a self-generating phenomenon. There is indeed a vast literature, both primary and secondary, relating to the debates on crises and fluctuations around the middle of the nineteenth century, from which it is apparent that Juglar's book Des Crises Commerciales et de leur Retour Périodique en France, en Angleterre et aux États-Unis (originally published in 1862 and very much revised and enlarged in 1889) did not come out of the blue but was one of the products of an intellectual climate inducing the thinking of crises not as unrelated events but as part of a more complex phenomenon consisting of recurring crises related to the development of the commercial world – an interpretation corroborated by the almost regular occurrence of crises at about 10-year intervals.

Managing a large hospital network can be an extremely challenging task. Management must rely on numerous pieces of information when making business decisions. This chapter focuses on the number of bed days (NBD) which can be extremely valuable for operational managers to forecast for logistical planning purposes. In addition, the finance staff often requires an expected NBD as input for estimating future expenses. Some hospital reimbursement contracts are on a per diem schedule, and expected NBD is useful in forecasting future revenue.Two models, time regression and autoregressive integrated moving average (ARIMA), are applied to nine years of monthly counts of the NBD for the Rhode Island Hospital System. These two models are compared to see which gives the best fit for the forecasted NBD. Also, the question of summarizing the time data from monthly to quarterly time periods is addressed. The approaches presented in this chapter can be applied to a variety of time series data for business forecasting.

This chapter enquires into the contribution of two British writers, Herbert Somerton Foxwell and Henry Riverdale Grenfell, who elaborated upon the hints provided by Jevons towards a description of long waves in the oscillations of prices. Writing two decades after Jevons, they witnessed the era of high prices turning into the great depression of the last quarter of the nineteenth century, the causes of which they saw in the end of bimetallism. Not only did they take up Jevons’s specific explanation of the long fluctuations, but they also based their discussion upon graphical representation of data and incorporated in their treatment a specific trait (the superposition principle) of the ‘waves’ metaphor emphasized by the Manchester statisticians in the 1850s and 1860s. Their contribution is also interesting for their understanding of crises versus depressions at the time of the emergence of the interpretation of oscillations as a cycle, which they have only partially grasped – as distinct from the approach of later long wave theorists.

This chapter summarizes some of the results from the Working Group “National Travel Surveys” in COST Action 355. All 50 presentations could not be reviewed here; thus, we focus on three crucial topics:

• the periodicity of data collection, but also its longitudinal aspects (advantages of continuous surveying, repeated cross-sections vs. panel surveys, etc.),

• new technologies for improving the efficiency and accuracy of mobility surveys (computer-assisted telephone, Web-based, interviews, GPS, GSM, RDS, etc.),

• innovative approaches, exemplified by qualitative surveys combined with conventional quantitative ones, and by biographical approaches.

the periodicity of data collection, but also its longitudinal aspects (advantages of continuous surveying, repeated cross-sections vs. panel surveys, etc.),

new technologies for improving the efficiency and accuracy of mobility surveys (computer-assisted telephone, Web-based, interviews, GPS, GSM, RDS, etc.),

innovative approaches, exemplified by qualitative surveys combined with conventional quantitative ones, and by biographical approaches.

A major consequence of global environmental change is projected to be the alteration in flood periodicity, magnitude, and geographic patterns. There are a number of extant methods designed to help identify areas vulnerable to these consequences, the construction of composite vulnerability indices prominent among them. In this paper we have implemented the Order Rated Effectiveness (ORE) model (Klimberg & Ratick, 2020) to produce composite flood vulnerability indicators through the aggregation of six constituent vulnerability indicators future projected for 204 hydrologic subbasins that cover the contiguous US. The ORE aggregation results, when compared with those obtained using the Weighted Linear Combination and Data Envelopment Analysis, provided a more robust and actionable distribution of composite vulnerability results for decision-makers when prioritizing Hydrologic Unit Codes for further analysis and for effectively and efficiently implementing adaptation and mitigation strategies to address the flooding consequences due to global climate change.

Purpose — The introduction of new technology to public transport systems has provided an excellent opportunity for passive data collection. In this paper, we explore the possibility of automatically generating level of service indicators that could be used for operation planning and monitoring of Transantiago, the public transport system of Santiago, Chile.

Design/methodology/approach — After basic processing of the raw automatic vehicle location (AVL) and automatic fare collection (AFC) data, we were able to generate bus speed indicators, travel time measurements and waiting time estimates using data from 1week. The results were compared with manual measures when available.

Findings — The advantage is that these measurements and estimates are reliable because they are obtained from large samples and at nearly no cost. Moreover, they can be applied to any set of data with a selected periodicity.

Research limitations — The scope of this research is limited to what can be observed with AVL and AFC data. Additional information is required to incorporate other dimensions, such as personal characteristics and/or more detail in the origin/destination (OD) of the trips.

Practical implications — Nevertheless, these results are valuable for the planning and operation management of public transport systems because they provide large amounts of information that is difficult and expensive to obtain from direct measurements.

Originality/value — This paper proposes tools to obtain valuable information at a low cost. These tools can be implemented in many cities that have certain technological devices incorporated into their public transport systems.

Forecasting the number of bed days (NBD) needed within a large hospital network is extremely challenging, but it is imperative that management find a predictive model that best estimates the calculation. This estimate is used by operational managers for logistical planning purposes. Furthermore, the finance staff of a hospital would require an expected NBD as input for estimating future expenses. Some hospital reimbursement contracts are on a per diem schedule, and expected NBD is useful in forecasting future revenue.

This chapter examines two ways of estimating the NBD for a large hospital system, and it builds from previous work comparing time regression and an autoregressive integrated moving average (ARIMA). The two approaches discussed in this chapter examine whether using the total or combined NBD for all the data is a better predictor than partitioning the data by different types of services. The four partitions are medical, maternity, surgery, and psychology. The partitioned time series would then be used to forecast future NBD by each type of service, but one could also sum the partitioned predictors for an alternative total forecaster. The question is whether one of these two approaches outperforms the other with a best fit for forecasting the NBD. The approaches presented in this chapter can be applied to a variety of time series data for business forecasting when a large database of information can be partitioned into smaller segments.

This research studies private information from extreme price movements or jumps. The authors calculate the private information using a reduced form model from the stochastic volatility jump process and use several statistical robustness tests as well as several frequencies to improve our consistency. This study reveals that private information is significant in explain the existence of jumps in capital markets in Southeast Asia, whereas macroeconomic events cannot explain them. The authors determine empirically that private information in Malaysia, Singapore, Thailand, and Indonesia are not persistent and its value gradually decreases when we use the lower frequency. Based on the Fama–Macbeth regression, this study shows that private information in the capital market has a strong positive relationship with individual returns in Indonesia’s capital market and Thailand’s capital market for all frequencies.