Partial Stochastic Analysis with the Aglink-Cosimo Model: A Methodological Overview

Aglink-Cosimo is a recursive-dynamic partial equilibrium model developed and maintained by the OECD and FAO Secretariats as a collaborative effort. The model is primarily used to prepare the OECD-FAO Agricultural Outlook, a yearly publication aiming at providing baseline projections for the main global agricultural commodities over the medium term. This deterministic projections are enhanced by a Partial Stochastic Analysis tool, which allows for the analysis of specific market uncertainties. This is done by producing counterfactual scenarios to the baseline originating from varying yields and macroeconomic variables stochastically. The aim of this report is to propose and evaluate different methods of analysing stochastically important yields and macroeconomic uncertainty drivers. In a first stage, we identify and evaluate the best parametric method to extract unexplained variability, which we consider as uncertainty in the macro and yield drivers. In a second stage, we test parametric and nonparametric methods side by side to simulate ten years of potentially different macroeconomic and yield environments. The results can be summarised as follows. For yields, we find out that a parametric cubic trend method performs best in the first stage and a non-parametric hierarchical copula (Clayton) method is more appropriate in the second stage. For macroeconomic variables, a vector autoregressive model performs best in the first stage, while a non-parametric hierarchical copula (Frank) method is more appropriate in the second stage.JRC.D.4-Economics of Agricultur

EU commodity market development: Medium-term agricultural outlook. Proceedings of the October 2017 workshop.

The workshop on the 'EU commodity market development: Medium-term agricultural outlook' is an integral part of the intensive validation procedure of the results of the European Commission’s report on 'Prospects for EU agricultural markets and income'. It provides a forum for presentations on preliminary 10-year-ahead projections in EU agricultural commodity markets, and discussing in depth the EU prospects in a global context. This year the workshop was held on October 19-20 in Brussels. The workshop was jointly organised by the Joint Research Centre (JRC) and the Directorate-General for Agriculture and Rural Development (DG AGRI). Participants included policy makers, modelling and market experts from various countries, as well as stakeholders of the agri-food industry. This document summarises the presentations and discussions on the macroeconomic and energy assumptions associated with this outlook, and on each of the EU agricultural markets addressed (arable crops, biofuels, sugar, wine, milk and dairy, meat).JRC.D.4-Economics of Agricultur

Comparison and combination of a hemodynamics/biomarkers-based model with simplified PESI score for prognostic stratification of acute pulmonary embolism: findings from a real world study

Background: Prognostic stratification is of utmost importance for management of acute Pulmonary Embolism (PE) in clinical practice. Many prognostic models have been proposed, but which is the best prognosticator in real life remains unclear. The aim of our study was to compare and combine the predictive values of the hemodynamics/biomarkers based prognostic model proposed by European Society of Cardiology (ESC) in 2008 and simplified PESI score (sPESI).Methods: Data records of 452 patients discharged for acute PE from Internal Medicine wards of Tuscany (Italy) were analysed. The ESC model and sPESI were retrospectively calculated and compared by using Areas under Receiver Operating Characteristics (ROC) Curves (AUCs) and finally the combination of the two models was tested in hemodinamically stable patients. All cause and PE-related in-hospital mortality and fatal or major bleedings were the analyzed endpointsResults: All cause in-hospital mortality was 25% (16.6% PE related) in high risk, 8.7% (4.7%) in intermediate risk and 3.8% (1.2%) in low risk patients according to ESC model. All cause in-hospital mortality was 10.95% (5.75% PE related) in patients with sPESI score ≥1 and 0% (0%) in sPESI score 0. Predictive performance of sPESI was not significantly different compared with 2008 ESC model both for all cause (AUC sPESI 0.711, 95% CI: 0.661-0.758 versus ESC 0.619, 95% CI: 0.567-0.670, difference between AUCs 0.0916, p=0.084) and for PE-related mortality (AUC sPESI 0.764, 95% CI: 0.717-0.808 versus ESC 0.650, 95% CI: 0.598-0.700, difference between AUCs 0.114, p=0.11). Fatal or major bleedings occurred in 4.30% of high risk, 1.60% of intermediate risk and 2.50% of low risk patients according to 2008 ESC model, whereas these occurred in 1.80% of high risk and 1.45% of low risk patients according to sPESI, respectively. Predictive performance for fatal or major bleeding between two models was not significantly different (AUC sPESI 0.658, 95% CI: 0.606-0.707 versus ESC 0.512, 95% CI: 0.459-0.565, difference between AUCs 0.145, p=0.34). In hemodynamically stable patients, the combined endpoint in-hospital PE-related mortality and/or fatal or major bleeding (adverse events) occurred in 0% of patients with low risk ESC model and sPESI score 0, whilst it occurred in 5.5% of patients with low-risk ESC model but sPESI ≥1. In intermediate risk patients according to ESC model, adverse events occurred in 3.6% of patients with sPESI score 0 and 6.65% of patients with sPESI score ≥1.Conclusions: In real world, predictive performance of sPESI and the hemodynamic/biomarkers-based ESC model as prognosticator of in-hospital mortality and bleedings is similar. Combination of sPESI 0 with low risk ESC model may identify patients with very low risk of adverse events and candidate for early hospital discharge or home treatment.

Decomposing the inverse land size-yield relationship

Faster agricultural development requires understanding whether the inverse land size-yield relationship exists or not. To verify the presence of this relationship, this study decomposes a yield index into separate components attributable to (1) efficiency, (2) soil quality, (3) land size, (4) variable inputs, (5) capital inputs, and (6) outputs. Nonparametric productivity accounting methods are used to decompose the inverse land size-yield relationship in a multi-output representation of the technology without specific assumptions on returns to scale. A strongly significant inverse (positively convex) land size-yield relationship is present in the Kenyan data, but vanishes in favor of a linear inverse relationship when accounting for the effect of outputs' diversification

THE ROLE OF SOILS IN PRODUCTION: AGGREGATION, SEPARABILITY, AND YIELD DECOMPOSITION IN KENYAN AGRICULTURE

Agricultural production relies on soils. Increasing global population and the impact of climate change threaten the sustainability of soil for agricultural production. For these reasons, it is necessary to broaden present current methodological approaches to incorporating soil into economic analysis. The first essay proposes a methodology to aggregate quantitative soil characteristics through the use of separability theory in a Data Envelopment Analysis framework. This yields an aggregate soil-quality measure that appropriately aggregates soil characteristics. The application is to Kenyan maize farmers. The second essay develops a nonparametric statistical test of structural separability based on a bias correction of a central limit theorem for Data Envelopment Analysis estimators developed in Kneip, Simar and Wilson (2015a). The proposed nonparametric test for structural separability adapts the statistical procedures to test technology restrictions present in Kneip, Simar and Wilson (2015b). Monte Carlo experiments determine the size and power properties of the proposed test. An empirical analysis of Kenyan household farmers illustrates the use of the methodology. Global needs for higher agricultural production require understanding whether the frequently noted inverse land size-yield relationship is a true empirical regularity or an artifact of data collection methods. To examine this relationship, the third essay of this dissertation generalizes productivity decomposition methods to incorporate the quantification of a soil-productivity contribution. The generalized method decomposes a yield index into separate components attributable to (1) efficiency, (2) soil quality, (3) land size, (4) variable inputs, (5) capital inputs, and (6) output mix. Nonparametric productivity accounting methods are used to decompose the inverse land size-yield relationship in a multi-output representation of the technology without specific assumptions on returns to scale. A strongly significant inverse land size-yield relationship is present among Kenyan farmers

Sources of measured agricultural yield difference

We decompose yield difference relative to a reference level into components attributable to (1) efficiency difference, and movements along the frontier due to (2) land quality, to (3) land size, and to (4) other inputs. The production frontier is built using nonparametric methods requiring no specification of the functional form of the technology. We analyze the contributions to yield relative to a reference unit in terms of the quadripartite decomposition finding that results depend on the choice of the unit of reference. If the reference unit is chosen to be the mean, land size contributions are found to be negatively correlated to yield with usual finite moments regression methods. Also nonparamteric correlation confirms the negative sign of the relationship. If the reference unit is chosen to be the median instead, land size contributions are found to be negatively correlated to yield with usual finite moments regression methods. But nonparametric correlation is not statistically significant because many farmers have no contribution to production difference from their different land sizes. Integrated squared density difference tests show in both cases efficiency has a major role in shaping the distribution

The impacts of climate change on French agricultural productivity

This paper analyzes the impact of changes in stochastic climatic variables on a sample of French agricultural farms between 1990 and 2000. We quantify the productivity impact by decomposing productivity changes over time via nonparametric productivity accounting. This method provides an empirical nonparametric measure of the impact of climate variables on production, a measure of technological change and a measure of efficiency change

A model of firm exit under inefficiency and uncertainty

Der Beitrag untersucht im Rahmen eines theoretischen Modells den Einfluss technischer Effizienz auf das optimale Timing von Betriebsaufgaben unter Unsicherheit. Die Wirkung der Faktoren Unsicherheit und Effizienz auf Betriebsaufgabeentscheidungen wurde in der Literatur bereits ausführlich diskutiert, allerdings nur separat. In dieser Arbeit steht die Analyse der Interaktion beider Faktoren im Mittelpunkt. Ausgangspunkt der Modellierung ist ein klassisches Realoptionsmodell, das Irreversibilität und Unsicherheit von Produktpreisen bei der Bestimmung von Exit-Triggern berücksichtigt. Technische Effizienz wird über eine Produktionsfunktion eingeführt. Die Eigenschaften der Produktionsfunktion werden durch eine Legendre-Transformation auf die Gewinnfunktion des Unternehmens übertragen. Wir betrachten zwei Arten von Wechselwirkungen zwischen Effizienzparameter und der Produktionstechnologie: In einem Fall ist technische Effizienz von den Inputfaktoren der Produktionsfunktion separierbar, im anderen Fall dagegen nicht. Während bei Separierbarkeit eine höhere Effizienz zu niedrigeren Preistriggern und damit zu einer Verschiebung des optimalen Zeitpunkts der Betriebsaufgabe führt ist dieser Zusammenhang bei Nichtseparierbarkeit nicht eindeutig.This paper examines the impact of technical efficiency on the optimal exit timing of firms in a stochastic dynamic framework. While an extensive literature deals with exit behavior under output price uncertainty and efficiency of firms separately, the interplay of these two aspects has not yet been examined. Starting from a standard real options approach, we incorporate technical efficiency via a production function and derive an optimal price trigger at which firms irreversibly exit a market. The profit function in the optimization problem inherits properties from the production function by means of a dual Legendre transform. We consider two types of production technologies which differ in the way efficiency interacts with the primal technology. Assuming separability of efficiency on the primal technology, we show that higher efficiency and higher returns to scale make the firm more reluctant to irreversibly exit the market. We then extend this model to a case where efficiency is not separable from other inputs and derive explicit results from a Cobb-Douglas production function. Unexpectedly, we find that higher efficiency does not always increase the reluctance to exit if firms exhibit low returns to scale