ALICE: Composite leading indicators for euro area inflation cycles

This paper develops Area-wide Leading Inflation CyclE (ALICE) indicators with the aim to provide early signals for turning points in the euro area headline and core inflation cycles. The headline (core) ALICE leads the reference cycle by three (four) months and the lead times extend to five (nine) months based on longer-leading series. Both ALICE indicators signalled turning points in the inflation cycles ex post and also performed well in a simulated real-time exercise. Moreover, in particular the headline ALICE also appeared to be useful for quantitative forecasting of the direction and level of inflation.     

Unequal access to food during the nutritional transition: evidence from Mediterranean Spain†

Unequal access to food is one of the main issues in nutritional history, but scarcity of sources has hampered the quantification of this phenomenon. This study uses hospital diets to address this gap. It uses records from between 1852 and 1923 concerning hospital diets in the psychiatric section of the Hospital General de Valencia (Spain), from which it is possible to infer the actual intake of nutrients for six groups of patients and members of staff. The results reveal considerable differences in terms of diet and nutrition. While the most favoured groups (nuns and well-off patients) had by 1852 reduced their relative intake of cereals and increased that of meat, in line with the general trend of the nutritional transition, the poor and orphans were still behind the trend by 1923. Hospital staff were on a high-calorie diet that was adequate for carrying out physically demanding tasks, yet still suffered from a significant deficit in nutrient intake. These inequalities indicate that the nutritional transition was an uneven and non-linear process, with substantial differences according to social group.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Does tourism really cause stress? A natural experiment utilizing ArcGIS Survey123

ABSTRACT

Tourism related stress has recently been revealed as an issue for tourism host community residents. Prior studies have relied on directly asking residents if tourism caused them stress, resulting in possible participant priming. This study utilized the ArcGIS Survey123 app to collect data on resident stress from a community with high levels of tourism visitation and a community with low levels of tourism visitation. During the study, researchers never mentioned tourism. Half of study participants in the community with high tourism visitation discussed tourism directly as a stressor, accounting for almost 5% of all stressful experiences, while no participants in the community with low tourism visitation mentioned tourism as a stressor. Geospatial data revealed that tourism related stressors occurred outside of the ‘tourism zone’ in the community with high tourism visitation, and tourism related stressors mirrored perceived tourism impacts measured in a post experiment survey of study participants.     

Information flows and crashes in dynamic social networks

We develop a dynamic model of information transmission and aggregation in social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity, in which agents’ opinions are widely dispersed.

     

Cost-effectiveness of second-line atezolizumab in Canada for advanced non-small cell lung cancer (NSCLC)

Aim: To assess the cost-effectiveness in Canada of atezolizumab compared with docetaxel or nivolumab for the treatment of advanced NSCLC after first-line platinum-doublet chemotherapy.

Materials and methods: A three-state partitioned-survival model was developed. Clinical inputs were obtained from the phase III OAK trial comparing atezolizumab with docetaxel in patients with advanced NSCLC who progressed after first-line platinum-doublet chemotherapy. Overall survival (OS) and progression-free survival (PFS) were extrapolated beyond the trial period using parametric models. A cure model assuming a 1% cure fraction was fitted to the OS data for atezolizumab. Outcomes for nivolumab were informed by a network meta-analysis (NMA) vs atezolizumab. Resource use and costs were informed by clinical expert opinion and published Canadian sources. Utility values were obtained from the OAK trial. The perspective of the analysis was that of the Canadian publicly-funded healthcare system. The base case time horizon was 10?years, and the discount rate was 1.5% annually for both costs and effects. Scenario analyses were performed to test the robustness of the results and all analyses were performed probabilistically.

Results: Atezolizumab demonstrated a quality-adjusted life-year (QALY) gain of 0.60 compared with docetaxel at an incremental cost of $85,073, resulting in an incremental cost-effectiveness ratio (ICER) of $142,074/QALY. Atezolizumab dominated nivolumab (regardless of dosing regimen), based on modest differences in both QALYs and costs. Docetaxel was most likely to be cost effective at willingness-to-pay (WTP) thresholds below $125,000/QALY gained, while atezolizumab was most likely to be cost effective beyond this WTP threshold. In most scenario analyses, the results remained robust to changes in parameters. A reduced time horizon and alternative approaches to the NMA had the greatest impact on cost-effectiveness results.

Conclusion: Atezolizumab represents a cost-effective therapeutic option in Canada for the treatment of patients with advanced NSCLC who progress after first-line platinum doublet chemotherapy.     

Technological leadership and sectorial employment growth: A spatial econometric analysis for U.S. counties

This paper studies the determinants of technological catch‐up considering spatial and sectoral aggregation of industries. We investigate how geographical and technological proximity to the technology leader impact regional employment growth. We model technological progress by means of a hierarchical process of catch‐up to the technology leader. We also incorporate measures for knowledge spillover effects to test the roles of competition, specialisation, and diversity at the industry level. Empirical results using data at the county level for different economic sectors (2‐dig NAICS) for the United States indicate that human capital plays a crucial role in promoting sectoral employment growth. The association between technological/geographical distance to the technology leader and employment growth varies across sectors.