Instituto Tecnológico Autónomo de México

About: Instituto Tecnológico Autónomo de México is a education organization based out in Mexico City, Mexico. It is known for research contribution in the topics: Politics & Population. The organization has 1098 authors who have published 2532 publications receiving 39083 citations. The organization is also known as: Instituto Tecnologico Autonomo de Mexico & Mexico Autonomous Institute of Technology.

Estimating Trends with Percentage of Smoothness Chosen by the User

Abstract: Summary This work presents a method for estimating trends of economic time series that allows the user to fix at the outset the desired percentage of smoothness for the trend. The calculations are based on the Hodrick-Prescott (HP) filter usually employed in business cycle analysis. The situation considered here is not related to that kind of analysis, but with describing the dynamic behaviour of the series by way of a smooth curve. To apply the filter, the user has to specify a smoothing constant that determines the dynamic behaviour of the trend. A new method that formalizes the concept of trend smoothness is proposed here to choose that constant. Smoothness of the trend is measured in percentage terms with the aid of an index related to the underlying statistical model of the HP filter. Empirical illustrations are provided using data on Mexico's GDP.

La identificación partidista en las elecciones presidenciales de 2000 y 2006 en México

Abstract: In this article, we analyze the patterns of change in party identification observed in the2000 and 2006 presidential elections in Mexico. Based primarily on national exit poll data, we focuson three observed and interrelated phenomena: first, there was a slight decline in the level of partisanshipobserved not only among voters who turned out, but also among the electorate at large. Secondly,party identification remains one of the most important explanatory variables of the vote inMexico, but partisan voting was slightly weaker in 2006, as evidenced by the levels of cross-overvoting and split-ticket voting in each election. Finally, a multivariate analysis based on these datashows significant changes in the social and ideological composition of party identification, providingevidence of how partisan realignment among segments of the Mexican electorate is taking place.From 2000 to 2006, formerly strong PRI identifiers, such as women and rural voters, adopted identificationwith either PAN or PRD. Our analysis also documents transformations such as leftist PAN voterschanging to PRD and highly educated voters becoming increasingly independent

On the solution of complementarity problems arising in American options pricing

Abstract: In the Black-Scholes-Merton model, as well as in more general stochastic models in finance, the price of an American option solves a parabolic variational inequality. When the variational inequality is discretized, one obtains a linear complementarity problem (LCP) that must be solved at each time step. This paper presents an algorithm for the solution of these types of LCPs that is significantly faster than the methods currently used in practice. The new algorithm is a two-phase method that combines the active-set identification properties of the projected successive over relaxation (SOR) iteration with the second-order acceleration of a (recursive) reduced-space phase. We show how to design the algorithm so that it exploits the structure of the LCPs arising in these financial applications and present numerical results that show the effectiveness of our approach.

Algorithmic targeting of social policies: fairness, accuracy, and distributed governance

Abstract: Targeted social policies are the main strategy for poverty alleviation across the developing world. These include targeted cash transfers (CTs), as well as targeted subsidies in health, education, housing, energy, childcare, and others. Due to the scale, diversity, and widespread relevance of targeted social policies like CTs, the algorithmic rules that decide who is eligible to benefit from them---and who is not---are among the most important algorithms operating in the world today. Here we report on a year-long engagement towards improving social targeting systems in a couple of developing countries. We demonstrate that a shift towards the use of AI methods in poverty-based targeting can substantially increase accuracy, extending the coverage of the poor by nearly a million people in two countries, without increasing expenditure. However, we also show that, absent explicit parity constraints, both status quo and AI-based systems induce disparities across population subgroups. Moreover, based on qualitative interviews with local social institutions, we find a lack of consensus on normative standards for prioritization and fairness criteria. Hence, we close by proposing a decision-support platform for distributed governance, which enables a diversity of institutions to customize the use of AI-based insights into their targeting decisions.