Trade, Extent of the Market, and Economic Growth 1960-1996

We find that trade and domestic market size are robust determinants of economic growth over the 1960-1996 period when trade openness is measured as the US dollar value of imports and exports relative to GDP in PPP US$ ('real openness'). When trade openness is measured as the US dollar value of imports and exports relative to GDP in exchange rate US$ ('nominal openness') however, trade and the size of domestic markets are often non-robust determinants of growth. We argue that real openness is the more appropriate measure of trade and that our empirical results should be seen as evidence in favor of the extent-of-the-market hypothesis.

A Model of Collateral, Investment, and Adverse Selection

This paper characterizes the relationship between entrepreneurial wealth and aggregate investment under adverse selection. Its main finding is that such a relationship need not be monotonic. In particular, three results emerge from the analysis: (i) pooling equilibria, in which investment is independent of entrepreneurial wealth, are more likely to arise when entrepreneurial wealth is relatively low; (ii) separating equilibria, in which investment is increasing in entrepreneurial wealth, are most likely to arise when entrepreneurial wealth is relatively high and; (iii) for a given interest rate, an increase in entrepreneurial wealth may generate a discontinuous fall in investment.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Fresh News; The Farmers' Market Newsletter, Spring 2004

Newsletter produced by Iowa Department of Agriculture and Land Stewardship

Fresh News; The Farmers' Market Newsletter, Summer 2006

Newsletter produced by Iowa Department of Agriculture and Land Stewardship

Fresh News; The Farmers' Market Newsletter, Fall 2004

Newsletter produced by Iowa Department of Agriculture and Land Stewardship

Fresh News; The Farmers' Market Newsletter, Fall 2005

Newsletter produced by Iowa Department of Agriculture and Land Stewardship

Fresh News; The Farmers' Market Newsletter, Summer 2005

Newsletter produced by Iowa Department of Agriculture and Land Stewardship

Fresh News; The Farmers' Market Newsletter, Spring 2005

Newsletter produced by Iowa Department of Agriculture and Land Stewardship

Fresh News; The Farmers' Market Newsletter, Winter 2006

Newsletter produced by Iowa Department of Agriculture and Land Stewardship

Fresh News; The Farmers' Market Newsletter, Spring 2006

Newsletter produced by Iowa Department of Agriculture and Land Stewardship

Myocardial flow reserve by Rb-82 cardiac PET improves the selection of patients eligible for invasive coronary angiography

Aim: We asked whether myocardial flow reserve (MFR) by Rb-82 cardiac PET improve the selection of patients eligible for invasive coronary angiography (ICA). Material and Methods: We enrolled 26 consecutive patients with suspected or known coronary artery disease who performed dynamic Rb-82 PET/CT and (ICA) within 60 days; 4 patients who underwent revascularization or had any cardiovascular events between PET and ICA were excluded. Myocardial blood flow at rest (rMBF), at stress with adenosine (sMBF) and myocardial flow reserve (MFR=sMBF/rMBF) were estimated using the 1-compartment Lortie model (FlowQuant) for each coronary arteries territories. Stenosis severity was assessed using computer-based automated edge detection (QCA). MFR was divided in 3 groups: G1:MFR<1.5, G2:1.5≤MFR<2 and G3:2≤MFR. Stenosis severity was graded as non-significant (<50% or FFR ≥0.8), intermediate (50%≤stenosis<70%) and severe (≥70%). Correlation between MFR and percentage of stenosis were assessed using a non-parametric Spearman test. Results: In G1 (44 vessels), 17 vessels (39%) had a severe stenosis, 11 (25%) an intermediate one, and 16 (36%) no significant stenosis. In G2 (13 vessels), 2 (15%) vessels presented a severe stenosis, 7 (54%) an intermediate one, and 4 (31%) no significant stenosis. In G3 (9 vessels), 0 vessel presented a severe stenosis, 1 (11%) an intermediate one, and 8 (89%) no significant stenosis. Of note, among 11 patients with 3-vessel low MFR<1.5 (G1), 9/11 (82%) had at least one severe stenosis and 2/11 (18%) had at least one intermediate stenosis. There was a significant inverse correlation between stenosis severity and MFR among all 66 territories analyzed (rho= -0.38, p=0.002). Conclusion: Patients with MFR>2 could avoid ICA. Low MFR (G1, G2) on a vessel-based analysis seems to be a poor predictor of severe stenosis severity. Patients with 3-vessel low MFR would benefit from ICA as they are likely to present a significant stenosis in at least one vessel.