Nonresponse and measurement errors in income: matching individual survey data with administrative tax data

A (local) survey on income carried out in the city of Modena in 2002 generated four categories of units: interviewees, refusals, noncontacts, and sometimes unused reserves. In this study, all units were matched with their corresponding records in the Ministry of Finance 2002 databases for fiscal incomes of 2001 and the 2001 Census. Considering all four categories, participation increased by education level and activity status, while it decreased among low or high incomes. Considering interviewees only, over- and under-reporting, as well as measurement errors, were investigated by comparing the surveyed income with fiscal income. Age and level of income were the main covariates affecting the behaviours of taxpayers

Testing threshold cointegration in Wagner's Law: the role of military spending

This paper uses historical data since mid-19th century to test the validity of Wagner's Law for the Italian economy. Unlike the previous studies, we accommodate possible nonlinear asymmetric effects of total goverment spending and GDP toward their long-run equilibrium. Our results show the presence of a threshold cointegrating relationship between the two variables with significantly different error correction adjustments in normal and extreme regimes. Particularly, we find the validity of Wagner's Law from 1862 to 2009, only when we take into account strong nonlinear responses of government spending during the WWI and WWII period. Robustness checks clearly recognize nonlinear behaviour of government expenditure driven by military spendin

Learning from failure. Big data analysis for detecting the patterns of failure in innovative startups

This paper aims at identifying appropriate models for analyzing large dataset to serve a twofold goal: firstly, to better understand the dynamics impacting innovative startups’ performance and their managerial practice and, secondly, to detect their patterns of failure. Therefore, we investigate the interaction of economic-financial, context and governance dimensions of 4,185 Italian innovative startups created from 2012 to 2015. Once startups have been grouped, we focus only on those unsuccessful. Then failure patterns have been uncovered integrating the use of factor and cluster analysis, where factor scores for each firm are used to identify a set of homogeneous groups based on clustering methods. The integrated use of those large dimensional data techniques permits to classify items in rigorous ways and to unfold structures of the data, which are not apparent in the beginning. The analysis suggests that each pattern of failure is a multidimensional construct and as a consequence can generate different managerial implications. Therefore, an effective handling of failure requires management to use appropriate intervention targeted at the challenges faced at that particular pattern of failure in different firm’s age

Groups of Fibonacci type revisited

This article concerns a class of groups of Fibonacci type introduced by Johnson and Mawdesley that includes Conway?s Fibonacci groups, the Sieradski groups, and the Gilbert-Howie groups. This class of groups provides an interesting focus for developing the theory of cyclically presented groups and, following questions by Bardakov and Vesnin and by Cavicchioli, Hegenbarth, and Repov?s, they have enjoyed renewed interest in recent years. We survey results concerning their algebraic properties, such as isomorphisms within the class, the classification of the finite groups, small cancellation properties, abelianizations, asphericity, connections with Labelled Oriented Graph groups, and the semigroups of Fibonacci type. Further, we present a new method of proving the classification of the finite groups that deals with all but three groups

Eigenvalue Ratio Estimators for the Number of Dynamic Factors

In this paper we introduce three dynamic eigenvalue ratio estimators for the number of dynamic factors. Two of them, the Dynamic Eigenvalue Ratio (DER) and the Dynamic Growth Ratio (DGR) are dynamic counterparts of the eigenvalue ratio estimators (ER and GR) proposed by Ahn and Horenstein (2013). The third, the Dynamic eigenvalue Difference Ratio (DDR), is a new one but closely related to the test statistic proposed by Onatsky (2009). The advantage of such estimators is that they do not require preliminary determination of discretionary parameters. Finally, a static counterpart of the latter estimator, called eigenvalue Difference Ratio estimator (DR), is also proposed. We prove consistency of such estimators and evaluate their performance under simulation. We conclude that both DDR and DR are valid alternatives to existing criteria. Application to real data gives new insights on the number of factors driving the US economy

Determinants of Central Bank independence: a random forest approach

In this paper we implement an effcient non-parametric statistical method, Random survival forests, for the selection of the determinants of Central Bank Independence (CBI) among a large data base of political and economic variables for OECD countries.This statistical technique enables us to overcome omitted variables and overftting problems. It turns out that the economic variables are major determinants compared to the political ones and linear andnonlinear effects of chosen predictors on CBI are found

Contending memory in heterogeneous SoCs: Evolution in NVIDIA Tegra embedded platforms

Modern embedded platforms are known to be constrained by size, weight and power (SWaP) requirements. In such contexts, achieving the desired performance-per-watt target calls for increasing the number of processors rather than ramping up their voltage and frequency. Hence, generation after generation, modern heterogeneous System on Chips (SoC) present a higher number of cores within their CPU complexes as well as a wider variety of accelerators that leverages massively parallel compute architectures. Previous literature demonstrated that while increasing parallelism is theoretically optimal for improving on average performance, shared memory hierarchies (i.e. caches and system DRAM) act as a bottleneck by exposing the platform processors to severe contention on memory accesses, hence dramatically impacting performance and timing predictability. In this work we characterize how subsequent generations of embedded platforms from the NVIDIA Tegra family balanced the increasing parallelism of each platform's processors with the consequent higher potential on memory interference. We also present an open-source software for generating test scenarios aimed at measuring memory contention in highly heterogeneous SoCs

SERPINB3 delays glomerulonephritis and attenuates the lupus-like disease in lupus murine models by inducing a more tolerogenic immune phenotype

Objective: To explore the effects of SERPINB3 administration in murine lupus models with a focus on lupus-like nephritis. Methods: 40 NZB/W F1 mice were subdivided into 4 groups and intraperitoneally injected with recombinant SERPINB3 (7.5 \u3bcg/0.1 mL or 15 \u3bcg/0.1 mL) or PBS (0.1 mL) before (group 1 and 2) or after (group 3 and 4) the development of proteinuria ( 65100 mg/dl). Two additional mice groups were provided by including 20 MRL/lpr mice which were prophylactically injected with SERPINB3 (10 mice, group 5) or PBS (10 mice, group 6). Time of occurrence and levels of anti-dsDNA and anti-C1q antibodies, proteinuria and serum creatinine, overall- and proteinuria-free survival were assessed in mice followed up to natural death. Histological analysis was performed in kidneys of both lupus models. The Th17:Treg cell ratio was assessed by flow-cytometry in splenocytes of treated and untreated MRL/lpr mice. Statistical analysis was performed using non parametric tests and Kaplan-Meier curves, when indicated. Results: Autoantibody levels and proteinuria were significantly decreased and time of occurrence significantly delayed in SERPINB3-treated mice vs. controls. In agreement with these findings, proteinuria-free and overall survival were significantly improved in SERPINB3-treated groups vs. controls. Histological analysis demonstrated a lower prevalence of severe tubular lesions in kidneys of group 5 vs. group 6. SERPINB3-treated mice showed an overall trend toward a reduced prevalence of severe lesions in both strains. Th17:Treg ratio was significantly decreased in splenocytes of MRL/lpr mice treated with SERPINB3, compared to untreated control mice. Conclusions: SERPINB3 significantly improves disease course and delays the onset of severe glomerulonephritis in lupus-prone mice, possibly inducing a more tolerogenic immune phenotype

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA)