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This article proposes a new approach to detecting the presence of common cyclical features when several time series are sampled at different frequencies. We generalize the common-frequency approach introduced by Engle and Kozicki (1993) and Vahid and Engle (1993). We start with the mixed-frequency VAR representation investigated in Ghysels (2012) for stationary time series. For non-stationary time series in levels, we show that one has to account for the presence of two sets of long-run relationships. The first set is implied by identities stemming from the fact that the differences of the high-frequency I (1) regressors are stationary. The second set comes from possible additional long-run relationships between one of the high-frequency series and the low-frequency variables. Our transformed vector error-correction model (VECM) representations extend the results of Ghysels (2012) and are important for determining the correct set of variables to be used in a subsequent common cycle investigation. This fact has implications for the distribution of test statistics and for forecasting. Empirical analyses with quarterly real gross national product (GNP) and monthly industrial production indices for, respectively, the United States and Germany illustrate our new approach. We also conduct a Monte Carlo study which compares our proposed mixed-frequency models with models stemming from classical temporal aggregation methods.

We investigate the relationship between airline network structure and airport congestion. More specifically, we study the ways in which airlines adjust capacity to delays depending on the network type they operate. We find some evidence suggesting that airlines operating hub-and-spoke structures react less to delays than airlines operating fully connected configurations. In particular, network airlines have incentives to keep frequency high even if this is at the expense of a greater congestion at their hub airports. We also show that airlines in slot-constrained airports seem to react to higher levels of congestion by using bigger aircraft at lower frequencies; thus, we conclude that conditioning the number of available slots on the levels of delays at the airport seems an effective measure that creates the right incentives for airlines to reduce the congestion they generate.

To explore and measure wearout or the acceptance threshold, beyond which, messages in the form of mobile text advertising generate irritation. To assess the set of factors that positively or negatively, according to literature, influences the attitude towards advertising in short message service (SMS) format and on this basis to propose future research along this line. There is also a focus on irritation antecedents.

Two surveys are used to prevent unbiased answers. The first one is driven to study the wearout effect in the SMS context. An offline survey is carried out using a structured questionnaire. A sample size of 188 using convenience sampling is collected. The second research is driven to study irritation and attitude towards SMS advertising. Data are collected through an online questionnaire which is published through social media platforms, an e-mail mailing list and a quick response (QR) code. An international sample size of 253 applying a convenience and snow ball sampling procedure is collected.

The wearout threshold and irritation antecedents in the mobile advertising context are identified as well as positive and negative factors which influence attitude towards SMS advertising. The replies do not match exactly with the significant factors found in previous research.

There are some, among them, sample size and sampling procedure; only one sector was analysed and, although reliability is acceptable, the number of items in each measurement scale was reduced to only two.

Wearout and the characteristics of an SMS message capable to generate positive attitude are described.

Guidelines to improve public attitudes towards SMS advertising and prevention from wearout are given.

Wearout in the mobile advertising context is explored and some insights regarding irritation antecedents and the role played by frequency and other positive factors in the causal model proposed by the academy are assessed.

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.

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent literature. In this article, we discuss classical and Bayesian methods of estimating mixed-frequency VARs, and use them for forecasting and structural analysis. We also compare mixed-frequency VARs with other approaches to handling mixed-frequency data.

In this chapter, we aim to measure the actual volatility within a model-based framework using high-frequency data. In the empirical finance literature, it is widely discussed that tick-by-tick prices are subject to market micro-structure effects such as bid-ask bounces and trade information. These market micro-structure effects become more and more apparent as prices or returns are sampled at smaller and smaller time intervals. An increasingly popular measure for the variability of spot prices on a particular day is realised volatility that is typically defined as the sum of squared intra-daily log-returns. Recent theoretical results have shown that realised volatility is a consistent estimator of actual volatility, but when it is subject to micro-structure noise and the sampling frequency increases, the estimator diverges. Parametric and nonparametric methods can be adopted to account for the micro-structure bias. Here, we measure actual volatility using a model that takes account of micro-structure noise together with intra-daily volatility patterns and stochastic volatility. The coefficients of this model are estimated by maximum likelihood methods that are based on importance sampling techniques. It is shown that such Monte Carlo techniques can be employed successfully for our purposes in a feasible way. As far as we know, this is a first attempt to model the basic components of the mean and variance of high-frequency prices simultaneously. An illustration is given for three months of tick-by-tick transaction prices of the IBM stock traded at the New York Stock Exchange.

While organization theories acknowledge the influence of specific assets on dependence and increasingly represent the latter as a structure of mutual dependence (dependence of A on B and dependence of B on A), there is, to the best of our knowledge, no empirical test concerning the impact of specific assets on a structure of dependence. Our chapter aims to fill this gap. It is all the more original in that it considers a case study where dependence changes sides according to the characteristics of the transaction. We examine the dependence between Chilean exporters and European importers when trading fresh produce. Such dependence originates with the need for just-in-time coordination and compliance with a compelling demand in a context of high price uncertainty.

Using a unique dataset from international trade in fresh produce between Chile and the rest of the world, we justify the use of a concentration sales ratio as a proxy for dependence and test the influence of a variety of specific assets on the side of dependence by using both categorical and dimensional approaches. Original findings show that certain transaction attributes have a strong influence on the side of dependence. In particular, the higher the frequency and the level of specific assets such as volume, niche varieties, and joint sales with other products, in the transaction, the greater the likelihood of a higher ratio of dependence for the importer rather than the exporter. Conversely, in the event of low levels of specific assets and less frequent operations, dependence tends to be greater on the side of the exporter.

In this project, we propose and test a new device – wearable sociometric badges containing small microphones – as a low-cost and relatively unobtrusive tool for measuring stress response to group processes. Specifically, we investigate whether voice pitch, measured using the microphone of the sociometric badge, is associated with physiological stress response to group processes.

We collect data in a laboratory setting using participants engaged in two types of small-group interactions: a social interaction and a problem-solving task. We examine the association between voice pitch (measured by fundamental frequency of the participant’s speech) and physiological stress response (measured using salivary cortisol) in these two types of small-group interactions.

We find that in the social task, participants who exhibit a stress response have a statistically significant greater deviation in voice pitch (from their overall average voice pitch) than those who do not exhibit a stress response. In the problem-solving task, participants who exhibit a stress response also have a greater deviation in voice pitch than those who do not exhibit a stress response, however, in this case, the results are only marginally significant. In both tasks, among participants who exhibited a stress response, we find a statistically significant correlation between physiological stress response and deviation in voice pitch.

We conclude that wearable microphones have the potential to serve as cheap and unobtrusive tools for measuring stress response to group processes.