Dynamic Economic Relationships Among U.S. Soy Product Markets: Using a Cointegrated Vector Autoregression Approach with Directed Acyclic Graphs

This paper applies a combined methodology of a recently developed directed acyclic graph (DAG) analysis with Johansen and Juselius' methods of the cointegrated vector autoregression (VAR) model to a monthly U.S. system of markets for soybeans, soy meal, and soy oil. Primarily a methods paper, Johansen and Juselius' procedures are applied, with a special focus on statistically addressing information inherent in well-known sources of non-normal data behavior to illustrate the effectiveness of modeling the system as a cointegrated multi-market system. Perhaps for the first time, methods of the cointegrated VAR model are combined with DAG analysis to account for contemporaneously correlated residuals, and are applied to this U.S. soy-based system. Analysis of the error correction or cointegration space illuminates the empirical nature of policy-relevant market elasticities, price transmission parameters, and effects of important policy and institutional changes/events on U.S. soy-related markets at long-run horizons beyond a single crop cycle. A statistically strong U.S. demand for soybeans emerged as the primary cointegrating relation in the error-correction space. Analysis of the DAG-adjusted cointegrated VAR model's forecast error variance decomposition illuminates how the soy-related variables and the three U.S. soy product markets dynamically interact at alternative time horizons extending up to two-years.directed acyclic graphs, cointegration, vector error correction and vector autoregression models, monthly U.S. soy-based markets., Industrial Organization, Research Methods/ Statistical Methods,

Continuous glucose monitoring in pregnant women with type 1 diabetes (CONCEPTT): a multicentre international randomised controlled trial.

BACKGROUND: Pregnant women with type 1 diabetes are a high-risk population who are recommended to strive for optimal glucose control, but neonatal outcomes attributed to maternal hyperglycaemia remain suboptimal. Our aim was to examine the effectiveness of continuous glucose monitoring (CGM) on maternal glucose control and obstetric and neonatal health outcomes. METHODS: In this multicentre, open-label, randomised controlled trial, we recruited women aged 18-40 years with type 1 diabetes for a minimum of 12 months who were receiving intensive insulin therapy. Participants were pregnant (≤13 weeks and 6 days' gestation) or planning pregnancy from 31 hospitals in Canada, England, Scotland, Spain, Italy, Ireland, and the USA. We ran two trials in parallel for pregnant participants and for participants planning pregnancy. In both trials, participants were randomly assigned to either CGM in addition to capillary glucose monitoring or capillary glucose monitoring alone. Randomisation was stratified by insulin delivery (pump or injections) and baseline glycated haemoglobin (HbA1c). The primary outcome was change in HbA1c from randomisation to 34 weeks' gestation in pregnant women and to 24 weeks or conception in women planning pregnancy, and was assessed in all randomised participants with baseline assessments. Secondary outcomes included obstetric and neonatal health outcomes, assessed with all available data without imputation. This trial is registered with ClinicalTrials.gov, number NCT01788527. FINDINGS: Between March 25, 2013, and March 22, 2016, we randomly assigned 325 women (215 pregnant, 110 planning pregnancy) to capillary glucose monitoring with CGM (108 pregnant and 53 planning pregnancy) or without (107 pregnant and 57 planning pregnancy). We found a small difference in HbA1c in pregnant women using CGM (mean difference -0·19%; 95% CI -0·34 to -0·03; p=0·0207). Pregnant CGM users spent more time in target (68% vs 61%; p=0·0034) and less time hyperglycaemic (27% vs 32%; p=0·0279) than did pregnant control participants, with comparable severe hypoglycaemia episodes (18 CGM and 21 control) and time spent hypoglycaemic (3% vs 4%; p=0·10). Neonatal health outcomes were significantly improved, with lower incidence of large for gestational age (odds ratio 0·51, 95% CI 0·28 to 0·90; p=0·0210), fewer neonatal intensive care admissions lasting more than 24 h (0·48; 0·26 to 0·86; p=0·0157), fewer incidences of neonatal hypoglycaemia (0·45; 0·22 to 0·89; p=0·0250), and 1-day shorter length of hospital stay (p=0·0091). We found no apparent benefit of CGM in women planning pregnancy. Adverse events occurred in 51 (48%) of CGM participants and 43 (40%) of control participants in the pregnancy trial, and in 12 (27%) of CGM participants and 21 (37%) of control participants in the planning pregnancy trial. Serious adverse events occurred in 13 (6%) participants in the pregnancy trial (eight [7%] CGM, five [5%] control) and in three (3%) participants in the planning pregnancy trial (two [4%] CGM and one [2%] control). The most common adverse events were skin reactions occurring in 49 (48%) of 103 CGM participants and eight (8%) of 104 control participants during pregnancy and in 23 (44%) of 52 CGM participants and five (9%) of 57 control participants in the planning pregnancy trial. The most common serious adverse events were gastrointestinal (nausea and vomiting in four participants during pregnancy and three participants planning pregnancy). INTERPRETATION: Use of CGM during pregnancy in patients with type 1 diabetes is associated with improved neonatal outcomes, which are likely to be attributed to reduced exposure to maternal hyperglycaemia. CGM should be offered to all pregnant women with type 1 diabetes using intensive insulin therapy. This study is the first to indicate potential for improvements in non-glycaemic health outcomes from CGM use. FUNDING: Juvenile Diabetes Research Foundation, Canadian Clinical Trials Network, and National Institute for Health Research

Trade liberalization : fears and facts/ Rogowsky

xii, 101 hal.; 23 cm

A Dynamic Model of U.S. Sugar-Related Markets: A Cointegrated Vector Autoregression Approach

The methods of the cointegrated vector autoregression (VAR) model are applied to monthly U.S. markets for sugar and for sugar-using markets for confectionary, soft drink, and bakery products. Primarily a methods paper, we apply Johansen and Juselius' advanced procedures to these markets for perhaps the first time, with focus on achievement of a statistically adequate model through analysis of a battery of advanced statistical diagnostic tests and on exploitation of the system's cointegration properties through rank restrictions, statistically supported hypotheses test restrictions, and inference. The VEC model results illuminate the estimates of crucial policy-relevant market parameters that drive these markets, as well as the dynamic nature of the relationships linking these sugar-based markets

Dynamic Economic Relationships Among U.S. Soy Product Markets: Using a Cointegrated Vector Autoregression Approach with Directed Acyclic Graphs

This paper applies a combined methodology of a recently developed directed acyclic graph (DAG) analysis with Johansen and Juselius' methods of the cointegrated vector autoregression (VAR) model to a monthly U.S. system of markets for soybeans, soy meal, and soy oil. Primarily a methods paper, Johansen and Juselius' procedures are applied, with a special focus on statistically addressing information inherent in well-known sources of non-normal data behavior to illustrate the effectiveness of modeling the system as a cointegrated multi-market system. Perhaps for the first time, methods of the cointegrated VAR model are combined with DAG analysis to account for contemporaneously correlated residuals, and are applied to this U.S. soy-based system. Analysis of the error correction or cointegration space illuminates the empirical nature of policy-relevant market elasticities, price transmission parameters, and effects of important policy and institutional changes/events on U.S. soy-related markets at long-run horizons beyond a single crop cycle. A statistically strong U.S. demand for soybeans emerged as the primary cointegrating relation in the error-correction space. Analysis of the DAG-adjusted cointegrated VAR model's forecast error variance decomposition illuminates how the soy-related variables and the three U.S. soy product markets dynamically interact at alternative time horizons extending up to two-years