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Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers’ index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers.

We rank the quality of German macroeconomic forecasts using various methods for 17 regular annual German economic forecasts from 14 different institutions for the period from 1993 to 2019. Using data for just one year, rankings based on different methods correlate only weakly with each other. Correlations of rankings calculated for two consecutive years and a given method are often relatively low and statistically insignificant. For the total sample, rank correlations between institutions are generally relatively high among different criteria. We report substantial long-run differences in forecasting quality, which are mostly due to distinct average forecast horizons. In the long-run, choosing the criterion to rank the forecasters is of minor importance. Rankings based on recession years and normal periods are similar. The same does hold for rankings based on real-time vs revised data.

This contribution focuses on the utilization of very-high-resolution (VHR) images to identify construction areas and their temporal changes aiming to estimate the investment in construction as a basis for economic forecasts. Triggered by the need to improve macroeconomic forecasts and reduce their time intervals, the idea arose to use frequently available information derived from satellite imagery. For the improvement of macroeconomic forecasts, the period to detect changes between two points in time needs to be rather short because early identification of such investments is beneficial. Therefore, in this study, it is of interest to identify and quantify new construction areas, which will turn into build-up areas later. A multiresolution segmentation followed by a kNN classification is applied to WorldView images from an area around the southern part of Berlin, Germany. Specific material compositions of construction areas result in typical classification patterns different from other land cover classes. A GIS-based analysis follows to extract specific temporal “patterns of life” in construction areas. With the early identification of such patterns of life, it is possible to predict construction areas that will turn into real estate later. This information serves as an input for macroeconomic forecasts to support quicker forecasts in future.

Public attitudes and policies toward LGBTQ individuals have improved substantially in recent decades. Economists are actively shaping the discourse around these policies and contributing to our understanding of the economic lives of LGBTQ individuals. In this paper, we present the most up-to-date estimates of the size, location, demographic characteristics, and family structures of LGBTQ individuals in the United States. We describe an emerging literature on the effects of legal access to same-sex marriage on family and socioeconomic outcomes. We also summarize what is known about the size, direction, and sources of wage differentials related to variation in sexual orientation and gender identity. We conclude by describing a range of open questions in LGBTQ economics.

Several recent theories emphasize the negative effects of an aging population on economic growth, either because of the lower labor force participation and productivity of older workers or because aging will create an excess of savings over desired investment, leading to secular stagnation. We show that there is no such negative relationship in the data. If anything, countries experiencing more rapid aging have grown more in recent decades. We suggest that this counterintuitive finding might reflect the more rapid adoption of automation technologies in countries undergoing more pronounced demographic changes and provide evidence and theoretical underpinnings for this argument.

In this paper, we examine the ability of Fisher effect to describe the subjective behaviour of monetary policy responses for nations constrained by global factors. We developed and estimated a simple DSGE model for appraising the consequence of an integrated financial market predictor on national monetary policy response in Africa's largest economies - Nigeria and South Africa. The paper integrated the theoretical intuition of the famous Fisher effect on the New Keynesian DSGE model with global predictors to describe national monetary policy response as it influence domestic financial variables and macroeconomic fundamentals. Simulations show that the existence of global factors threatens the abilities of national monetary policy to predict financial variables and macroeconomic fundamentals in their economies.

This paper argues that contemporary processes of extended urbanisation, which include suburbanisation, post-suburbanisation and peri-urbanisation, may result in increased vulnerability to infectious disease spread. Through a review of existing literature at the nexus of urbanisation and infectious disease, we consider how this (potential) increased vulnerability to infectious diseases in peri– or suburban areas is in fact dialectically related to socio-material transformations on the metropolitan edge. In particular, we highlight three key factors influencing the spread of infectious disease that have been identified in the literature: demographic change, infrastructure and governance. These have been chosen given both the prominence of these themes and their role in shaping the spread of disease on the urban edge. Further, we suggest how a landscape political ecology framework can be useful for examining the role of socioecological transformations in generating increased risk of infectious disease in peri–and suburban areas. To illustrate our arguments we will draw upon examples from various re-emerging infectious disease events and outbreaks around the world to reveal how extended urbanisation in the broadest sense has amplified the conditions necessary for the spread of infectious diseases. We thus call for future research on the spatialities of health and disease to pay attention to how variegated patterns of extended urbanisation may influence possible outbreaks and the mechanisms through which such risks can be alleviated. 本文认为，当代的扩展城市化进程，包括郊区化、后郊区化和周边城市化，可能会导致传染病传播方面脆弱性的增加。通过回顾现有的关于城市化和传染病之间关系的文献，我们研究这种（潜在的 ）周边或郊区传染病脆弱性的增加与大都市边缘的社会物质转变之间是如何辩证相关的。特别是，我们强调了文献中确定的影响传染病传播的三个关键因素：人口变化、基础设施和治理。鉴于这些主题的突出性及其在城市边缘疾病传播中的作用，我们选择了这些主题。此外，我们提出了景观政治生态框架在研究社会生态转变对周边和郊区传染病风险增高的影响方面的作用。为了说明我们的论点，我们将使用世界各地各种重新出现的传染病事件和爆发的例子，揭示了最广以上的城市化扩展如何扩大了传染病传播的必要条件。因此，我们呼吁，在未来对健康和疾病的空间关系的研究中，应关注各种不同的扩展城市化模式对疾病爆发风险的影响，以及减轻这种风险的机制。

Business surveys indicators represent an important tool in economic analysis and forecasting practices. While there is wide consensus on the coincident properties of such data, there is mixed evidence on their ability to forecast macroeconomic developments in the short term. In this study we extend the previous research on business surveys predictive content by examining for the first time the leading properties of the main business survey indicators coming from the Italian survey on inflation and growth expectations (SIGE). To this end we provide a complete characterization of the business cycle leading/coincident properties of SIGE data (turning points, average duration, synchronization etc.) with respect to the National Accounts reference series using both non parametric approaches (i.e. Harding and Pagan in J Monet Econ 49(2):365–381, 2002) and econometric models (discrete and continuous dynamic single equation models). Overall the results indicate that in both the approaches SIGE business indicators are able to early detect turning points of their corresponding national account reference series in almost all cases. Overall, the average lead of troughs is found to be higher than the average lead of peaks.

We construct a matrix showing the share of the year 2000 population in every country that is descended from people in different source countries in the year 1500. Using the matrix to adjust indicators of early development so that they reflect the history of a population's ancestors rather than the history of the place they live today greatly improves the ability of those indicators to predict current GDP. The variance of the early development history of a country's inhabitants is a good predictor for current inequality, with ethnic groups originating in regions having longer histories of organized states tending to be at the upper end of a country's income distribution.

We estimate the effect of changes in demographic structure on long-term trends of key macroeconomic variables using a panel VAR for 21 OECD economies from 1970–2014. The panel data variation assists the identification of demographic effects, while the dynamic structure, incorporating multiple channels of influence, uncovers long-term effects. We propose a theoretical model, relating demographics, innovation, and growth, whose simulations match our empirical findings. The current trend of population aging and low fertility is projected to reduce output growth, investment, and real interest rates across OECD countries.