The endo–exo problem in high frequency financial price fluctuations and rejecting criticality

The endo–exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of the deterministic background intensity. With these methods, we strongly reject the hypothesis that the considered financial markets are critical at univariate and bivariate microstructural levels.     

Consistent inference in fixed-effects stochastic frontier models

The classical stochastic frontier panel data models provide no mechanism to disentangle individual time invariant unobserved heterogeneity from inefficiency. Greene (2005a, b) proposed the so-called “true” fixed-effects specification that distinguishes these two latent components. However, due to the incidental parameters problem, his maximum likelihood estimator may lead to biased variance estimates. We propose two alternative estimators that achieve consistency for $n\to \infty$ with fixed $T$. Furthermore, we extend the Chen et al. (2014) results providing a feasible estimator when the inefficiency is heteroskedastic and follows a first-order autoregressive process. We investigate the behavior of the proposed estimators through Monte Carlo simulations showing good finite sample properties, especially in small samples. An application to hospitals’ technical efficiency illustrates the usefulness of the new approach.     

Research collaboration model in academic social networks

ABSTRACT

This paper proposes a hybrid collaboration recommendation method that accounts for research similarities and the previous research cooperation network. Research cooperation is measured by combining the collaboration time and the number of co-authors who already collaborated with at least one scientist. Research similarity is based on authors’ previous publications and academic events they attended. A weighted directed graph is built to discover new collaborators by using direct and indirect connections between scientists. Moreover, a consensus-based system is built to integrate bibliography data from different sources. The experimental results show that our method improves the recommendation performances over other methods.     

Crowdsourcing-based semantic relation recognition for natural language questions over RDF data

ABSTRACT

Natural language query systems over RDF data need to rely on the semantic relations in query. First, we propose the new crowdsourcing model that used to produce semantic relations dataset. The model not only inherits completeness of the iterative model and accuracy of the parallel model, but also saves human resources. Second, we mine the rules of semantic relation recognition from the correlations between dependency structures and semantic relations. Third, we propose an algorithm of semantic relation recognition for natural language query over RDF data, and experiments demonstrate that it can recognize more semantic relations than existing methods.     

Neighbors and friends: How do European political parties respond to globalization?

This paper explores the relationship between globalization and party positions accounting for potential differences between left- and right-wing parties. The analysis is based on a panel model of 36 political parties in 18 Western European countries between 1970 and 2015. We find that right-wing parties move leftward in response to globalization, while left-wing parties do not alter their position. Additionally, we find that ideological party positions are affected by foreign parties’ positions of the same ideological bloc. These findings indicate that there is a convergence towards the left of the political spectrum due to right-wing party moderation.     

Determinants of total efficiency in water utilities in Mexico

By using panel data analysis across 368 water utilities in Mexico over the period 2010–2014, we find that small firms are more efficient than medium and large providers and that increasing production is associated with less efficiency. Billing, income collection and profitability are relevant factors to improve efficiency. Medium and large firms require to measure consumption by service with meters in order to increase efficiency, while small firms do not require it.     

Some issues in blockchain for accounting and the supply chain,with an application of distributed databases to virtual organizations

This paper reviews some recent blockchain‐based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a ‘blockchain‐like’ system for virtual organizations.     

Tracing VARDI coefficients: a proposal

ABSTRACT

We propose a new approach for tracing the so-called ‘value-added-(re)distribution-important coefficients’ (in short the VARDI coefficients) in a world input–output model. From the perspective of a selected group of economies, VARDI coefficients may be defined as those elements in world input–output matrix in the case of which a small change in their levels leads to the maximization of a share of this group of economies in value added in global value chains. Due to the rapid development of the World Input Output Database, this approach may be easily applied in empirical research to different groups of countries and sectors in world IO models. In an illustrative empirical case study, we use the new approach in order to answer a question regarding what the main directions of the future macroeconomic policy of the U.S. could be in order to ensure the maximization of the country’s share in global value added.     

Network analysis of the Caucasus’ image

Destination image is influenced by many factors, including destination promotion materials, the mass media as a general familiarity agent, the perceivers’ own characteristics and even researchers’ methodological choices. To isolate and minimize the impact of research on destination image, different qualitative and quantitative designs and analysis techniques have been utilized. However, no previous study utilized the network analysis technique, which may be useful to reveal a picture of destination image with the interconnections and hidden dynamisms of dimensions as well as its correlates. The present study applies this technique on qualitative data from an online sample of Americans on their perceptions of a relatively remote and unfamiliar destination, the Caucasus region, in order to hone in on the dramatic impact of mass media on destination image. Results revealed networks of meanings with residue of mass media messages about Boston bombing, with some differences among different genders and education levels.     

Forecasting GDP growth with NIPA aggregates: In search of core GDP

In addition to GDP, which is measured using expenditure data, the U.S. national income and product accounts (NIPAs) provide a variety of measures of economic activity, including gross domestic income and other aggregates that exclude one or more of the components that make up GDP. Similarly to the way in which economists have attempted to use core inflation—which excludes volatile energy and food prices—to predict headline inflation, the omission of GDP components may be useful in extracting a signal as to where GDP is going. We investigate the extent to which these NIPA aggregates constitute “core GDP.” In an out-of-sample forecasting exercise using a novel real-time dataset of NIPA aggregates, we find that consumption growth and the growth of GDP excluding inventories and trade have historically outperformed a canonical univariate benchmark for forecasting GDP growth, suggesting that these are promising measures of core GDP growth.