Armando Sánchez
National Autonomous University of Mexico
7 Papers
26 Citations
Armando Sánchez is an academic researcher from National Autonomous University of Mexico. The author has contributed to research in topics: Exchange rate & Biogas. The author has an hindex of 6, co-authored 7 publications.
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Papers
Microalgal-Biotechnology As a Platform for an Integral Biogas Upgrading and Nutrient Removal from Anaerobic Effluents
TL;DR: This proof of concept study confirmed that algal-bacterial photobioreactors can support an integral upgrading without biogas contamination, with a net negative CO2 footprint, energy production, and a reduction of the eutrophication potential of the residual anaerobic effluents.
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Global and hemispheric temperatures revisited
TL;DR: In this paper, the authors present an analysis of the time series properties of global and hemispheric temperatures using modern econometric techniques and show that the temperature series can be better described as trend-stationary processes with a one-time permanent shock which cannot be interpreted as part of the natural variability.
New evidence on the monetary approach of exchange rate determination in Mexico 1994-2007: A cointegrated SVAR model
TL;DR: In this article, empirical evidence supporting the validity of both short and long run versions of the Monetary Approach of Exchange Rate determination for the Mexican peso-U.S. dollar exchange rate from 1994 to 2007 using a cointegrated SVAR model was provided.
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MACROECONOMIC LINKAGES IN MEXICO: Macroeconomic Linkages in Mexico
TL;DR: In this article, the authors study the effects of selected macro variables on output in Mexico, following a Kaleckian framework, and using a probabilistic approach to econometrics.
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A reply to “Does temperature contain a stochastic trend? Evaluating conflicting statistical results” by R. K. Kaufmann et al
TL;DR: In this article, Gay et al. argued that the cointegration methodology may not be adequate for modeling global and hemispheric temperature series and that the best forecast is not necessarily produced by the model that best describes the underlying data generating process.
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