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Renan Xavier Cortes

Bio: Renan Xavier Cortes is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Statistical model & Bayesian inference. The author has an hindex of 4, co-authored 24 publications receiving 77 citations. Previous affiliations of Renan Xavier Cortes include Universidade Federal do Rio Grande do Sul & Foundation for Economic Education.

Papers
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Journal ArticleDOI
TL;DR: This study aimed to present and discuss alternative methods to multinomial logistic regression based upon robust Poisson regression and the log-binomial model, and showed that the proposed models have more precise and accurate estimates for the RR or PR than theMultinomiallogistic regression, as in the case of the binary outcome.
Abstract: Recent studies have emphasized that there is no justification for using the odds ratio (OR) as an approximation of the relative risk (RR) or prevalence ratio (PR). Erroneous interpretations of the OR as RR or PR must be avoided, as several studies have shown that the OR is not a good approximation for these measures when the outcome is common (> 10%). For multinomial outcomes it is usual to use the multinomial logistic regression. In this context, there are no studies showing the impact of the approximation of the OR in the estimates of RR or PR. This study aimed to present and discuss alternative methods to multinomial logistic regression based upon robust Poisson regression and the log-binomial model. The approaches were compared by simulating various possible scenarios. The results showed that the proposed models have more precise and accurate estimates for the RR or PR than the multinomial logistic regression, as in the case of the binary outcome. Thus also for multinomial outcomes the OR must not be used as an approximation of the RR or PR, since this may lead to incorrect conclusions.

24 citations

Journal ArticleDOI
TL;DR: PySAL as discussed by the authors is a library for geocomputation and spatial data science written in Python, which has a long history of supporting novel scholarship and broadening methodological impacts far afield of academic work.
Abstract: PySAL is a library for geocomputation and spatial data science. Written in Python, the library has a long history of supporting novel scholarship and broadening methodological impacts far afield of academic work. Recently, many new techniques, methods of analyses, and development modes have been implemented, making the library much larger and more encompassing than that previously discussed in the literature. As such, we provide an introduction to the library as it stands now, as well as the scientific and conceptual underpinnings of its core set of components. Finally, we provide a prospective look at the library's future evolution.

19 citations

Journal ArticleDOI
01 Apr 2020
TL;DR: An open-source Python package designed as a submodule for the Python Spatial Analysis Library, PySAL, tackles the problem of segregation point estimation for a wide variety of spatial and aspatial segregation indices, while providing a computationally based hypothesis testing framework that relies on simulations under the null hypothesis.
Abstract: In human geography and the urban social sciences, the segregation literature typically engages with five conceptual dimensions along which a given society may be considered segregated: evenness, isolation, clustering, concentration and centralization (all of which can incorporate or omit spatial context). Over the last several decades, dozens of segregation indices have been proposed and studied in the literature, each of which is designed to focus on the nuances of a particular dimension, or correct an oversight in earlier work. Despite their increasing proliferation, however, few of these indices remain used in practice beyond their original conception, due in part to complex formulae and data requirements, particularly for indices that incorporate spatial context. Furthermore, existing segregation software typically fails to provide inferential frameworks for either single-value or comparative hypothesis testing. To fill this gap, we develop an open-source Python package designed as a submodule for the Python Spatial Analysis Library, PySAL. This new module tackles the problem of segregation point estimation for a wide variety of spatial and aspatial segregation indices, while providing a computationally based hypothesis testing framework that relies on simulations under the null hypothesis. We illustrate the use of this new library using tract-level census data in two American cities.

12 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a methodology for measuring criminal activity in the municipalities of Rio Grande do Sul combining several types of criminal occurrences in a historical series ranging from 2002 to 2015.
Abstract: The Economics of Crime is dedicated to study illegal human activity and measuring it properly is a major challenge in the literature. This article proposes a methodology for measuring criminal activity in the municipalities of Rio Grande do Sul combining several types of criminal occurrences in a historical series ranging from 2002 to 2015. Using a refined Bayesian space-time model with the Integrated Nested Laplace Approximation approach, the results measure general criminal activity avoiding problems of estimates such as high volatility and rarity of occurrences in low population cities. In addition, the proposed indexes contemplate the severity of each crime and represent a simple interpretability.

6 citations


Cited by
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Journal ArticleDOI
01 Jun 1949
TL;DR: Acemoglu et al. as mentioned in this paper showed that business cycles are both less volatile and more synchronized with the world cycle in rich countries than in poor ones, and they developed two alternative explanations based on the idea that comparative advantage causes rich countries to specialize in industries that use new technologies operated by skilled workers, while poor countries specialize in traditional technologies operate by unskilled workers.
Abstract: Business cycles are both less volatile and more synchronized with the world cycle in rich countries than in poor ones. We develop two alternative explanations based on the idea that comparative advantage causes rich countries to specialize in industries that use new technologies operated by skilled workers, while poor countries specialize in industries that use traditional technologies operated by unskilled workers. Since new technologies are difficult to imitate, the industries of rich countries enjoy more market power and face more inelastic product demands than those of poor countries. Since skilled workers are less likely to exit employment as a result of changes in economic conditions, industries in rich countries face more inelastic labour supplies than those of poor countries. We show that either asymmetry in industry characteristics can generate cross-country differences in business cycles that resemble those we observe in the data. We are grateful to Daron Acemoglu and Fabrizio Perri for useful comments. The views expressed here are the authors' and do not necessarily reflect those of The World Bank. Business cycles are not the same in rich and poor countries. A first difference is that fluctuations in per capita income growth are smaller in rich countries than in poor ones, in the top panel of Figure 1 , we plot the standard deviation of per capita income growth against the level of (log) per capita income for a large sample of countries. We refer to this relationship as the volatility graph and note that it slopes downwards. A second difference is that fluctuations in per capita income growth are more synchronized with the world cycle in rich countries than in poor ones. In the bottom panel of Figure 1 , we plot the correlation of per capita income growth rates with world average per capita income growth, excluding the country in question, against the level of (log) per capita income for the same set of countries. We refer to this relationship as the comovement graph and note that it slopes upwards. Table 1 , which is self-explanatory, shows that these facts apply within different sub-samples of countries and years. 1 Why are business cycles less volatile and more synchronized with the world cycle in rich countries than in poor ones? Part of the answer must be that poor countries exhibit more political and policy instability, they are less open or more distant from the geographical center, and they also have a higher share of their economy devoted to the production of agricultural products and the extraction of minerals. Table 1 shows that, in a statistical sense, these factors explain a substantial fraction of the variation in the volatility of income growth, although they do not explain much of the variation in the comovement of income growth. More important for our purposes, the strong relationship between income and the properties of business cycles reported in Table 1 is still present after we control for these variables. In short, there must be other factors behind the strong patterns depicted in Figure 1 beyond differences in political instability, remoteness and the importance of natural resources. With the exception that the comovement graph seems to be driven by differences between rich and poor countries and not within each group. Acemoglu and Zilibotti (1997) also present the volatility graph. They provide an explanation for it based on the observation that rich countries have more diversified production structures. We are unaware of any previous reference to the comovement graph. In this paper, we develop two alternative but non-competing explanations for why business cycles are less volatile and more synchronized with the world in rich countries than in poor ones. Both explanations rely on the idea that comparative advantage causes rich countries to specialize in industries that require new technologies operated by skilled workers, while poor countries specialize in industries that require traditional technologies operated by unskilled workers. This pattern of specialization opens up the possibility that cross-country differences in business cycles are the result of asymmetries between these types of industries. In particular, both of the explanations advanced here predict that industries that use traditional technologies operated by unskilled workers will be more sensitive to country-specific shocks. Ceteris paribus, these industries will not only be more volatile but also less synchronized with the world cycle since the relative importance of global shocks is lower. To the extent that the business cycles of countries reflect those of their industries, differences in industrial structure could potentially explain the patterns in Figure 1 . One explanation of why industries react differently to shocks is based on the idea that firms using new technologies face more inelastic product demands than those using traditional technologies. New technologies are difficult to imitate quickly for technical reasons and also because of legal patents. This difficulty confers a cost advantage on technological leaders that shelters them from potential entrants and gives them monopoly power in world markets. Traditional technologies are easier to imitate because enough time has passed since their adoption and also because patents have expired or have been circumvented. This implies that incumbent firms face tough competition from potential entrants and enjoy little or no monopoly power in world markets. The price-elasticity of product demand affects how industries react to shocks. Consider, for instance, the effects of country-specific shocks that encourage production in all industries. In industries that use new technologies, firms have monopoly power and face inelastic demands for their products. As a result, fluctuations in supply lead to opposing changes in prices that tend to stabilize industry income. In industries that use traditional technologies, firms face stiff competition from abroad and therefore face elastic demands for their products. As a result, fluctuations in supply have little or no effect on their prices and industry income is more volatile. To the extent that this asymmetry in the degree of product-market competition is important, incomes of industries that use new technologies are likely to be less sensitive to country-specific shocks than those of industries that use traditional technologies. Another explanation for why industries react differently to shocks is based on the idea that the supply of unskilled workers is more elastic than the supply of skilled workers. A first reason for this asymmetry is that non-market activities are relatively more attractive to unskilled workers whose market wage is lower than that of skilled ones. Changes in labour demand might induce some unskilled workers to enter or abandon the labour force, but are not likely to affect the participation of skilled workers. A second reason for the asymmetry in labour supply across skill categories is the imposition of a minimum wage. Changes in labour demand might force some unskilled workers in and out of unemployment, but are not likely to affect the employment of skilled workers. The wage-elasticity of the labour supply also has implications for how industries react to shocks. Consider again the effects of country-specific shocks that encourage production in all industries and therefore raise the labour demand. Since the supply of unskilled workers is elastic, these shocks lead to large fluctuations in employment of unskilled workers. In industries that use them, fluctuations in supply are therefore magnified by increases in employment that make industry income more volatile. Since the supply of skilled workers is inelastic, the same shocks have little or no effects on the employment of skilled workers. In industries that use them, fluctuations in supply are not magnified and industry income is less volatile. To the extent that this asymmetry in the elasticity of labour supply is important, incomes of industries that use unskilled workers are likely to be more sensitive to country-specific shocks than those of industries that use skilled workers To study these hypotheses we construct a stylized world equilibrium model of the cross-section of business cycles. Inspired by the work of Davis (1995), we consider in section one a world in which differences in both factor endowments a la Heckscher-Ohlin and industry technologies a la Ricardo combine to determine a country's comparative advantage and, therefore, the patterns of specialization and trade. To generate business cycles, we subject this world economy to the sort of productivity fluctuations that have been emphasized by Kydland and Prescott (1982). 2 In section two, we characterize the cross-section of business cycles and show how asymmetries in the elasticity of product demand and/or labour supply can be used to explain the evidence in Figure 1 . Using available microeconomic estimates of the key parameters, we calibrate the model and find that: (i) The model exhibits slightly less than two-thirds and one-third of the observed cross-country variation in volatility and comovement, respectively; and (ii) The asymmetry in the elasticity of product demand seems to have a quantitatively stronger effect on the slopes of the volatility and comovement graphs, than the elasticity in the labour supply. We explore these results further in sections three and four. In section three, we extend the model to allow for monetary shocks that have real effects since firms face cash-in-advance constraints. We use the model to study how cross-country variation in monetary policy and financial development affect the cross-section of business cycles. Once these factors are considered, the calibrated version of the model exhibits roughly the same cross-country variation in volatility and about 40 percent of the variation in comovement as the data. In section four, we show th

742 citations

Posted Content
01 Jan 1997
TL;DR: In this paper, the additive model is fitted by local polynomial regression and sufficient conditions guaranteeing the existence of unique estimators for the bivariate additive model are given, and asymptotic approximations to the bias and the variance of a homoscedastic bivariate model with local POlynomial terms of odd and even degree are computed.
Abstract: While the additive model is a popular nonparametric regression method, many of its theoretical properties are not well understood, especially when the backfitting algorithm is used for computation of the estimators. This article explores those properties when the additive model is fitted by local polynomial regression. Sufficient conditions guaranteeing the asymptotic existence of unique estimators for the bivariate additive model are given. Asymptotic approximations to the bias and the variance of a homoscedastic bivariate additive model with local polynomial terms of odd and even degree are computed. This model is shown to have the same rate of convergence as that of univariate local polynomial regression.

227 citations

Journal ArticleDOI
27 Apr 2021-PLOS ONE
TL;DR: In this paper, a cross-sectional analysis from a household survey of 3646 adults aged 18 years or older was conducted in 8 districts of Bangladesh, from December 12, 2020, to January 7, 2021.
Abstract: BACKGROUND: Although the approved COVID-19 vaccine has been shown to be safe and effective, mass vaccination in Bangladeshi people remains a challenge. As a vaccination effort, the study provided an empirical evidence on willingness to vaccinate by sociodemographic, clinical and regional differences in Bangladeshi adults. METHODS: This cross-sectional analysis from a household survey of 3646 adults aged 18 years or older was conducted in 8 districts of Bangladesh, from December 12, 2020, to January 7, 2021. Multinomial regression examined the impact of socio-demographic, clinical and healthcare-releated factors on hesitancy and reluctance of vaccination for COVID-19. RESULTS: Of the 3646 respondents (2212 men [60.7%]; mean [sd] age, 37.4 [13.9] years), 74.6% reported their willingness to vaccinate against COVID-19 when a safe and effective vaccine is available without a fee, while 8.5% were reluctant to vaccinate. With a minimum fee, 46.5% of the respondents showed intent to vaccinate. Among the respondents, 16.8% reported adequate adherence to health safety regulations, and 35.5% reported high confidence in the country's healthcare system. The COVID-19 vaccine refusal was significantly high in elderly, rural, semi-urban, and slum communities, farmers, day-laborers, homemakers, low-educated group, and those who had low confidence in the country's healthcare system. Also, the prevalence of vaccine hesitancy was high in the elderly population, low-educated group, day-laborers, people with chronic diseases, and people with low confidence in the country's healthcare system. CONCLUSION: A high prevalence of vaccine refusal and hesitancy was observed in rural people and slum dwellers in Bangladesh. The rural community and slum dwellers had a low literacy level, low adherence to health safety regulations and low confidence in healthcare system. The ongoing app-based registration for vaccination increased hesitancy and reluctancy in low-educated group. For rural, semi-urban, and slum people, outreach centers for vaccination can be established to ensure the vaccine's nearby availability and limit associated travel costs. In rural areas, community health workers, valued community-leaders, and non-governmental organizations can be utilized to motivate and educate people for vaccination against COVID-19. Further, emphasis should be given to the elderly and diseased people with tailored health messages and assurance from healthcare professionals. The media may play a responsible role with the vaccine education program and eliminate the social stigma about the vaccination. Finally, vaccination should be continued without a fee and thus Bangladesh's COVID vaccination program can become a model for other low and middle-income countries.

129 citations