Drift Estimation of Multiscale Diffusions Based on Filtered Data

We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coefficient of the homogenized equation requires pre-processing of the data, often in the form of subsampling; this is because the two-scale equation and the homogenized single-scale equation are incompatible at small scales, generating mutually singular measures on the path space. We avoid subsampling and work instead with filtered data, found by application of an appropriate kernel function, and compute maximum likelihood estimators based on the filtered process. We show that the estimators we propose are asymptotically unbiased and demonstrate numerically the advantages of our method with respect to subsampling. Finally, we show how our filtered data methodology can be combined with Bayesian techniques and provide a full uncertainty quantification of the inference procedure

Drift Estimation of Multiscale Diffusions Based on Filtered Data

We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coefficient of the homogenized equation requires pre-processing of the data, often in the form of subsampling; this is because the two-scale equation and the homogenized single-scale equation are incompatible at small scales, generating mutually singular measures on the path space. We avoid subsampling and work instead with filtered data, found by application of an appropriate kernel function, and compute maximum likelihood estimators based on the filtered process. We show that the estimators we propose are asymptotically unbiased and demonstrate numerically the advantages of our method with respect to subsampling. Finally, we show how our filtered data methodology can be combined with Bayesian techniques and provide a full uncertainty quantification of the inference procedure

Trade-off between photovoltaic systems installation and agricultural practices on arable lands: An environmental and socio-economic impact analysis for Italy

The paper introduces and discusses an open-source spatial-based model (called r.green.solar) able to quantify the energy production from solar photovoltaic (PV) ground-mounted panels. Socio-economic and environmental impacts can be evaluated by the model. The model starts from the theoretical quantity of solar PV potential energy and estimates a reduction of total amount of energy based on legal, technical, recommended and economic constraints. Model outputs were used for a trade-off analysis between energy production and traditional crops for food/feed cultivation on not irrigated arable land. The model was tested at regional level for a Mediterranean context (Italy). The results confirm that the economic profitability of PV systems follows a north-south gradient, but the main impacts are related to local peculiarities – such as the disposal of not irrigated arable land and the presence of constraints, in particular the landscape maintenance, the morphological variables and the specialization index – and crop yields. © 2016 Elsevier Ltd. All rightsreserved

Sympathetic skin response in multiple sclerosis : a meta-analysis of case-control studies

The usefulness of sympathetic skin responses (SSR) in multiple sclerosis (MS) has been advocated by several studies in the last 20\ua0years; however, due to a great heterogeneity of findings, a comprehensive meta-analysis of case-control studies is in order to pinpoint consistencies and investigate the causes of discrepancies. We searched MEDLINE, EMBASE and Cochrane databases for case-control studies comparing SSR absence frequency and latency between patients with MS and healthy controls. Thirteen eligible studies including 415 MS patients and 331 healthy controls were identified. The pooled analysis showed that SSR can be always obtained in healthy controls while 34% of patients had absent SSRs in at least one limb (95% CI 22\u201347%; p\ua0<\ua00.0001) but with considerable heterogeneity across studies (I2\ua0=\ua090.3%). Patients\u2019 age explained 22% of the overall variability and positive correlations were found with Expanded Disability Status Scale and disease duration. The pooled mean difference of SSR latency showed a significant increase in patients on both upper (193\ua0ms; 95% CI 120\u2013270\ua0ms) and lower (350\ua0ms; 95% CI 190\u2013510\ua0ms) extremities. We tested the discriminatory value of SSR latency thresholds defined as the 95% confidence interval (CI) upper bound of the healthy controls, and validated the results on a new dataset. The lower limb threshold of 1.964\ua0s produces the best results in terms of sensitivity 0.86, specificity 0.67, positive predicted value 0.75 and negative predicted value 0.80. Despite a considerable heterogeneity of findings, there is evidence that SSR is a useful tool in MS

Monetary Policy Rules and Directions of Causality: a Test for the Euro Area

Using a VAR model in first differences with quarterly data for the euro zone, the study aims to ascertain whether decisions on monetary policy can be interpreted in terms of a “monetary policy rule” with specific reference to the so-called nominal GDP targeting rule (Hall and Mankiw, 1994; McCallum, 1988; Woodford, 2012). The results obtained indicate a causal relation proceeding from deviation between the growth rates of nominal gross domestic product (GDP) and target GDP to variation in the three-month market interest rate. The same analyses do not, however, appear to confirm the existence of a significant inverse causal relation from variation in the market interest rate to deviation between the nominal and target GDP growth rates. Similar results were obtained on replacing the market interest rate with the European Central Bank refinancing interest rate. This confirmation of only one of the two directions of causality does not support an interpretation of monetary policy based on the nominal GDP targeting rule and gives rise to doubt in more general terms as to the applicability of the Taylor rule and all the conventional rules of monetary policy to the case in question. The results appear instead to be more in line with other possible approaches, such as those based on post Keynesian analyses of monetary theory and policy and more specifically the so-called solvency rule (Brancaccio and Fontana, 2013, 2015). These lines of research challenge the simplistic argument that the scope of monetary policy consists in the stabilization of inflation, real GDP, or nominal income around a “natural equilibrium” level. Rather, they suggest that central banks actually follow a more complex purpose, which is the political regulation of the financial system with particular reference to the relations between creditors and debtors and the related solvency of economic units