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We analyze the interaction among the common and country-specific components for the inflation rates in 12 euro area countries through a factor model with time-varying parameters. The variation of the model parameters is driven by the score of the predictive likelihood, so that, conditionally on past data, the model is Gaussian and the likelihood function can be evaluated using the Kalman filter. The empirical analysis uncovers significant variation over time in the model parameters. We find that, over an extended time period, inflation persistence has fallen and the importance of common shocks has increased relatively to that of idiosyncratic disturbances. According to the model, the fall in inflation observed since the sovereign debt crisis is broadly a common phenomenon since no significant cross-country inflation differentials have emerged. Stressed countries, however, have been hit by unusually large shocks.

It is found that one unit root, common trend is shared by the quarterly auction price series of five frequently auctioned types of stamps. The common trends analysis provides specific, stationary linear combinations, or cointegrating portfolios, of the auction price levels. The quarterly returns for the system of cointegrated auction prices can be represented by an error correction model using past returns and cointegrating vectors. There is evidence of a positive relationship between changes in the common trend and leading changes in industrial production

This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data.

This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals.

Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level.

Data are measured as the average price in squared meters, and the resulting index is not quality controlled.

The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices.

This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.

This chapter re-examines the determinants of health care expenditure (HCE), using a panel of 32 Organization for Economic Cooperation and Development (OECD) countries from 1990 to 2012. In particular, a panel semiparametric technique (i.e., a partially linear model) is employed, with cross-sectional dependence allowed. Beside the study of coefficients, this chapter investigates the trending functions of HCE. After the common and individual trends of HCE are estimated via semiparametric methods, the authors calibrate them with polynomial specifications, leading to out-of-sample forecasting. The validities of the calibration are tested as well. Contrary to those studies that do not take into account time series properties, our finding suggests that medical care is not a luxury commodity. Other determinants, such as public financing, and the supply of doctors, are all positively related to HCE. Moreover, the calibrated trending models perform well in out-of-sample forecasting.

The main aim of this paper is to examine labour productivity trends in Italy over the period 1981‐2004.

To this end, relying on recent developments in the analysis of non‐stationary dependent panels, the paper develops a new method for estimating total factor productivity (TFP) trends.

The conclusions confirm the view that the recent decline in Italian labour productivity growth is mostly due to a widespread fall in TFP growth.

The main assumption underlying the proposed TFP estimation method is that technology growth is driven by a single trend common to all units included in the panel (industries, regions or countries).

The paper provides two distinct contributions: empirically, it provides robust evidence that TFP slow‐down is the main cause of recent negative trends in labour productivity in Italy. Methodologically, the paper proposes an approach to estimating TFP that enjoys several advantages: only basic data for input and output flows are needed, the non‐stationary nature of the data is explicitly taken into account, and confidence intervals for TFP growth can be computed. This method can thus be easily applied to many routinely available datasets, to either corroborate existing growth accounting estimates or to obtain previously unavailable estimates.

This paper aims to examine the integration of housing markets in Canada by examining housing price data (1999–2016) of six metropolitan areas in different provinces, namely, Calgary, Vancouver, Winnipeg, Toronto, Montreal and Halifax. The authors test for cointegration, driver cities of long-run relationships, long-run Granger causality and instantaneous causality in light of the global financial crisis (GFC) (2007–2008).

The authors use Johansen’s system cointegration approach with structural breaks. Moving average representation is used for common stochastic trend(s) analysis. Finally, the authors apply vector error correction model-based Granger causality and instantaneous causality.

Cities’ housing prices are in long-run equilibrium. Post-crisis Canadian housing markets became more integrated. The Calgary, Vancouver, Toronto and Montreal markets drive the Canadian housing market, leading all cities toward long-run equilibrium. Strong long-run Granger causality exists, but the authors observe no instantaneous causality. Price information takes time to disseminate, and long-run price adjustments play a significant role in causation.

The findings of cointegration increasing after the GFC and strong lead–lag can be used by investors to arbitrage and optimize portfolios. This can also help national and local policymakers in mitigating risk. Incorporating these findings can lead to better price forecasting.

This study presents many novelties for the Canadian housing market: it is the first to use repeat-sales regional pricing indices to test long-run behaviors, conduct common stochastic trend analyzes and present causality relations.

This study aims to determine college/university students’ mobile phone practices to understand key user preferences and set a baseline understanding for the development of prospective library informational mobile services.

This study is based on the results of an online-survey method. The authors recruited students majoring in the Information Science program in School of Information at Pratt Institute (USA) (74 participants) and in Kyiv National University of Cultural and Arts (Ukraine) (89 participants).

The general trends in the use of mobile technologies by American and Ukrainian students were identified. Key components of library mobile services are offered. Such services may include information and reference service via messengers; development of library mobile applications of audio and video content; access to educational, scientific, popular science literature and fiction; and supplementing online services with library chatbots.

This study was limited to only students majoring in information science at university; however, the data obtained helps outline several general trends common to student youth who are actively working with information.

The obtained results will be useful in the practical activities of libraries and other information institutions for the development of a comprehensive information service based on mobile technology.

This study contributes to understanding how to improve library informational service considering users habits and preferences. Libraries management and librarians can use the findings as a basis for prioritizing the improvement of library informational mobile service.

A decade ago, the AIDS pandemic was driven by determinants such as poverty, deprivation, migrancy, patriarchy and gender-based violence. Today, however, the socio-economic and structural drivers of HIV infections have assumed or added other dimensions, including social and electronic media and reality television. These new dimensions saw further expression with the advent of the COVID-19 pandemic from 2020 onwards. To consign both HIV and the COVID-19 pandemics to history’s museum of pandemics, strategists must employ greater infiltration and mastery of social and electronic media and reality TV. In the case of HIV, these created social clouds or bubbles where unprotected sex, transactional sex and multiple concurrent sexual partnerships are manufactured and proliferated globally. The same was the case with the COVID-19 pandemic, in which case these social clouds or bubbles created an alternative narrative about the source of the pandemic, who and how people get infected, and both the requisite remedies and preventions in this regard. With reality television gaining popularity on low-cost paid channels and free-to-air television; with smartphone penetration widening and costs of access to data falling, a social cloud has been created, enabling the cultural majority (those who control the media and capital) to set trends for everyone, including those with less means. These trends in turn become a standard many aspire to live by. The ontological density of the poor and lower middle-class women is lost through the universalisation of social and cultural trends set by middle elites who control the production and reproduction of knowledge and shape international and national imagination. It is these discourses, and their shaping of imagination as a consequence, that this chapter deals with. It looks at both the implications and consequences which, in the case of pandemics such as these, can be dire.

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.

The purpose of this paper is to identify differences in managing a single project compared with that of a project portfolio, where focus and requirements are expanded, and where clear links to organizational objectives exist. Further, the aim is to propose a methodology for the management of risk within the context of a project portfolio.

The concepts and framework described in this paper have emerged primarily from an in‐depth action research study in a major provider of transport solutions. The work has been conducted within one division, with presence in most of mainland Europe, Scandinavia, and the UK.

The paper finds that the proposed methodology would manage portfolio risk in two ways. First, it provides a means for single projects to gain experiences from other projects within the portfolio. Second, portfolio common risks and trends of issues can be identified. Such risks can become risks for succeeding projects, or require action from outside the single project.

The paper shows that the pilot study consisted of 16 projects within one project portfolio. Other project portfolios, with other prerequisites, might result in different findings, since some factors not included in this research such as cultural aspects or organizational factors could affect the findings.

In this paper the identification and analysis of commonalities and risk trends between projects provide the possibility to manage risks from a portfolio perspective.

The paper sees that existing risk management processes do not support projects in managing risk within a project portfolio. Instead, the proposed methodology provides the project portfolio manager with a consolidated view of the total risk exposure within the portfolio. Additionally, this methodology finds risks and trends not otherwise possible to identify.