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Outnumbered : from Facebook and Google to fake news and filter-bubbles - the algorithms that control our lives
\"In this book, David Sumpter takes an algorithm-strewn journey to the dark side of mathematics. He investigates the equations that analyse us., influence us and will (maybe) become like us, answering questions such as: Are Google algorithms racist and sexist? ; Why do election predictions fall so drastically? ; What does the future hold as we relinquish our decision-making to machines? Featuring interviews with those working at the cutting edge of algorithm research, along with a healthy dose of mathematical self-experiment, Outnumbered will explain how mathematics and statistics work in the real world, and what we should and shouldn't worry about.\"--from book cover
Book
Words are the New Numbers: A Newsy Coincident Index of the Business Cycle
I construct a daily business cycle index based on quarterly GDP growth and textual information contained in a daily business newspaper. The newspaper data are decomposed into time series representing news topics, while the business cycle index is estimated using the topics and a time-varying dynamic factor model where dynamic sparsity is enforced upon the factor loadings using a latent threshold mechanism. The resulting index classifies the phases of the business cycle with almost perfect accuracy and provides broad-based high-frequency information about the type of news that drive or reflect economic fluctuations. In out-of-sample nowcasting experiments, the model is competitive with forecast combination systems and expert judgment, and produces forecasts with predictive power for future revisions in GDP. Thus, news reduces noise. Supplementary materials for this article are available online.
Journal Article
Transition Modeling and Econometric Convergence Tests
A new panel data model is proposed to represent the behavior of economies in transition, allowing for a wide range of possible time paths and individual heterogeneity. The model has both common and individual specific components, and is formulated as a nonlinear time varying factor model. When applied to a micro panel, the decomposition provides flexibility in idiosyncratic behavior over time and across section, while retaining some commonality across the panel by means of an unknown common growth component. This commonality means that when the heterogeneous time varying idiosyncratic components converge over time to a constant, a form of panel convergence holds, analogous to the concept of conditional sigma convergence. The paper provides a framework of asymptotic representations for the factor components that enables the development of econometric procedures of estimation and testing. In particular, a simple regression based convergence test is developed, whose asymptotic properties are analyzed under both null and local alternatives, and a new method of clustering panels into club convergence groups is constructed. These econometric methods are applied to analyze convergence in cost of living indices among 19 U.S. metropolitan cities.
Journal Article
CORE INFLATION AND TREND INFLATION
This paper examines empirically whether the measurement of trend inflation can be improved by using disaggregated data on sectoral inflation to construct indexes akin to core inflation but with a time-varying distributed lags of weights, where the sectoral weight depends on the time-varying volatility and persistence of the sectoral inflation series and on the comovement among sectors. The modeling framework is a dynamic factor model with time-varying coefficients and stochastic volatility as in Del Negro and Otrok (2008), and is estimated using U.S. data on seventeen components of the personal consumption expenditure inflation index.
Journal Article
DISAGREEMENT AMONG FORECASTERS IN G7 COUNTRIES
We investigate determinants of disagreement—cross-sectional dispersion of individual forecasts—about key economic indicators. Disagreement about economic activity, in particular about GDP growth, has a distinct dynamic from disagreement about prices: inflation and interest rates. Disagreement about GDP growth intensifies strongly during recessions. Disagreement about prices rises with their level, declines under independent central banks, and both its level and its sensitivity to macroeconomic variables are larger in countries where central banks became independent only around the mid-1990s. Our findings suggest that credible monetary policy contributes to anchoring of expectations about inflation and interest rates. Disagreement for both groups of indicators increases with uncertainty about the actual series.
Journal Article
Global raptor research and conservation priorities
Aim Raptors serve critical ecological functions, are particularly extinction‐prone and are often used as environmental indicators and flagship species. Yet, there is no global framework to prioritize research and conservation actions on them. We identify for the first time the factors driving extinction risk and scientific attention on raptors and develop a novel research and conservation priority index (RCPI) to identify global research and conservation priorities. Location Global. Methods We use random forest models based on ecological traits and extrinsic data to identify the drivers of risk and scientific attention in all raptors. We then map global research and conservation priorities. Lastly, we model where priorities fall relative to country‐level human social indicators. Results Raptors with small geographic ranges, scavengers, forest‐dependent species and those with slow life histories are particularly extinction‐prone. Research is extremely biased towards a small fraction of raptor species: 10 species (1.8% of all raptors) account for one‐third of all research, while one‐fifth of species have no publications. Species with small geographic ranges and those inhabiting less developed countries are greatly understudied. Regions of Latin America, Africa and Southeast Asia are identified as particularly high priority for raptor research and conservation. These priorities are highly concentrated in developing countries, indicating a global mismatch between priorities and capacity for research and conservation. Main conclusions A redistribution of scientific attention and conservation efforts towards developing tropical countries and the least‐studied, extinction‐prone species is critical to conserve raptors and their ecological functions worldwide. We identify clear taxonomic and geographic research and conservation priorities for all raptors, and our methodology can be applied across other taxa to prioritize scientific investment.
Journal Article
Measurement and Meaning in Information Systems and Organizational Research: Methodological and Philosophical Foundations
Despite renewed interest and many advances in methodology in recent years, information systems and organizational researchers face confusing and inconsistent guidance on how to choose amongst, implement, and interpret findings from the use of different measurement procedures. In this article, the related topics of measurement and construct validity are summarized and discussed, with particular focus on formative and reflective indicators and common method bias, and, where relevant, a number of allied issues are considered. The perspective taken is an eclectic and holistic one and attempts to address conceptual and philosophical essentials, raise salient questions, and pose plausible solutions to critical measurement dilemmas occurring in the managerial, behavioral, and social sciences.
Journal Article
Macroeconomic Uncertainty Through the Lens of Professional Forecasters
We analyze the evolution of macroeconomic uncertainty in the United States, based on the forecast errors of consensus survey forecasts of various economic indicators. Comprehensive information contained in the survey forecasts enables us to capture a real-time measure of uncertainty surrounding subjective forecasts in a simple framework. We jointly model and estimate macroeconomic (common) and indicator-specific uncertainties of four indicators, using a factor stochastic volatility model. Our macroeconomic uncertainty estimates have three major spikes has three major spikes aligned with the 1973-1975, 1980, and 2007-2009 recessions, while other recessions were characterized by increases in indicator-specific uncertainties. We also show that the selection of data vintages affects the estimates and relative size of jumps in estimated uncertainty series. Finally, our macroeconomic uncertainty has a persistent negative impact on real economic activity, rather than producing \"wait-and-see\" dynamics.
Journal Article
Nowcasting monthly GDP with big data
Gross domestic product (GDP) is the most comprehensive and authoritative measure of economic activity. The macroeconomic literature has focused on nowcasting and forecasting this measure at the monthly frequency, using related high-frequency indicators. We address the issue of estimating monthly GDP using a large-dimensional set of monthly indicators, by pooling the disaggregate estimates arising from simple and feasible bivariate models that consider one indicator at a time in conjunction to GDP. Our base model handles mixed-frequency data and raggededge data structure with any pattern of missingness. Our methodology enables to distil the common component of the available economic indicators, so that the monthly GDP estimates arise from the projection of the quarterly figures on the space spanned by the common component. The weights used for the combination reflect the ability to nowcast quarterly GDP and are obtained as a function of the regularized estimator of the high-dimensional covariance matrix of the nowcasting errors. A recursive nowcasting and forecasting experiment with data on euro area GDP illustrates that the optimal weights adapt to the information set available in real time and vary according to the phase of the business cycle.
Journal Article
Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil
Mobile geolocation data is a valuable asset in the assessment of movement patterns of a population. Once a highly contagious disease takes place in a location the movement patterns aid in predicting the potential spatial spreading of the disease, hence mobile data becomes a crucial tool to epidemic models. In this work, based on millions of anonymized mobile visits data in Brazil, we investigate the most probable spreading patterns of the COVID-19 within states of Brazil. The study is intended to help public administrators in action plans and resources allocation, whilst studying how mobile geolocation data may be employed as a measure of population mobility during an epidemic. This study focuses on the states of São Paulo and Rio de Janeiro during the period of March 2020, when the disease first started to spread in these states. Metapopulation models for the disease spread were simulated in order to evaluate the risk of infection of each city within the states, by ranking them according to the time the disease will take to infect each city. We observed that, although the high-risk regions are those closer to the capital cities, where the outbreak has started, there are also cities in the countryside with great risk. The mathematical framework developed in this paper is quite general and may be applied to locations around the world to evaluate the risk of infection by diseases, in special the COVID-19, when geolocation data is available.
Journal Article