In this research, we analyze the determinants of the real exchange rate through the fundamentals and behavioral factors, adding other variables as monetary aggregates, economic growth, domestic savings, and productivity. We worked with thirteen Latin American countries from 1980 to 2018 and we used three estimates such as fixed-effects, random-effects, and System GMM. The findings show that although the real exchange rate has a large random component, due to the high coefficient presented by the past values of that variable, there are other variables such as terms of trade, net foreign assets, tax revenue, monetary aggregates, savings rates and productivity, or real interest rate differentials, relative price and economic growth, which can impact negatively and positively respectively. Keywords: Real Exchange Rate, System GMM, Macroeconomics FactorsJEL Classification: E52, E62, C33, C53

Background Postoperative acute kidney injury (AKI) is a common complication of major gastrointestinal surgery with an impact on short- and long-term survival. No validated system for risk stratification exists for this patient group. This study aimed to validate externally a prognostic model for AKI after major gastrointestinal surgery in two multicentre cohort studies. Methods The Outcomes After Kidney injury in Surgery (OAKS) prognostic model was developed to predict risk of AKI in the 7 days after surgery using six routine datapoints (age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker). Validation was performed within two independent cohorts: a prospective multicentre, international study (‘IMAGINE’) of patients undergoing elective colorectal surgery (2018); and a retrospective regional cohort study (‘Tayside’) in major abdominal surgery (2011–2015). Multivariable logistic regression was used to predict risk of AKI, with multiple imputation used to account for data missing at random. Prognostic accuracy was assessed for patients at high risk (greater than 20 per cent) of postoperative AKI. Results In the validation cohorts, 12.9 per cent of patients (661 of 5106) in IMAGINE and 14.7 per cent (106 of 719 patients) in Tayside developed 7-day postoperative AKI. Using the OAKS model, 558 patients (9.6 per cent) were classified as high risk. Less than 10 per cent of patients classified as low-risk developed AKI in either cohort (negative predictive value greater than 0.9). Upon external validation, the OAKS model retained an area under the receiver operating characteristic (AUC) curve of range 0.655–0.681 (Tayside 95 per cent c.i. 0.596 to 0.714; IMAGINE 95 per cent c.i. 0.659 to 0.703), sensitivity values range 0.323–0.352 (IMAGINE 95 per cent c.i. 0.281 to 0.368; Tayside 95 per cent c.i. 0.253 to 0.461), and specificity range 0.881–0.890 (Tayside 95 per cent c.i. 0.853 to 0.905; IMAGINE 95 per cent c.i. 0.881 to 0.899). Conclusion The OAKS prognostic model can identify patients who are not at high risk of postoperative AKI after gastrointestinal surgery with high specificity. Presented to Association of Surgeons in Training (ASiT) International Conference 2018 (Edinburgh, UK), European Society of Coloproctology (ESCP) International Conference 2018 (Nice, France), SARS (Society of Academic and Research Surgery) 2020 (Virtual, UK).

In this research, I analyze the dynamic effects of undervaluation on the economic growth per captai of Latin American countries with a period 1980-2018. To estimate these effects, I use a Panel Vector Autoregressive (PVAR) whose estimator is System GMM. The undervaluation variable is created from different measures of the real exchange rate and I also use various measures of GDP per capita to calculate economic growth per capita. I include as control variables macroeconomic and human capital variables to control the different channels of spread of undervaluation on economic growth per capita. The results show that there is a positive effect depending on the definition of the real exchange rate used to calculate the undervaluation. In the results I include the Granger causality test, stability test and impulse response graphs in which I project the response of per capita economic growth to an undervaluation shock.