Friday, February 11, 2022
From a new report by Steven A. Altman and Caroline R. Bastian:
“The DHL Global Connectedness Index measures globalization based on international flows of trade, capital, information, and people. This update highlights key developments in these four areas for the world as a whole, with a focus on the Covid-19 crisis. Overall, globalization is emerging from the pandemic far stronger than many expected. The DHL Global Connectedness Index declined modestly in 2020, and there is clear evidence of a recovery underway in 2021. Nonetheless, the pandemic has also highlighted vulnerabilities that should be addressed in order to fortify and expand the benefits of global connectedness.”
From a new report by Steven A. Altman and Caroline R. Bastian:
“The DHL Global Connectedness Index measures globalization based on international flows of trade, capital, information, and people. This update highlights key developments in these four areas for the world as a whole, with a focus on the Covid-19 crisis. Overall, globalization is emerging from the pandemic far stronger than many expected. The DHL Global Connectedness Index declined modestly in 2020,
Posted by 6:31 AM
atLabels: Macro Demystified
On cross-country:
On the US:
On China
On other countries:
On cross-country:
On the US:
Posted by 5:00 AM
atLabels: Global Housing Watch
Tuesday, February 8, 2022
From a new paper by Monica Langella and Alan Manning:
“The UK has suffered from persistent spatial differences in unemployment rates for many decades. A low responsiveness of internal migration to unemployment is often argued to be an important cause of this problem. This paper uses UK census data to investigate how unemployment affects residential mobility using small areas as potential destinations and origins and four decades of data. It finds that both in- and out-migration are affected by local unemployment – but also that there is a very high ‘cost of distance’, so most moves are very local. We complement the study with individual longitudinal data to analyse individual heterogeneities in mobility. We show that elasticities to local unemployment are different across people with different characteristics. For instance, people who are better educated are more sensitive, the same applies to homeowners. Ethnic minorities are on average less sensitive to local unemployment rates and tend to end up in higher unemployment areas when moving.”
From a new paper by Monica Langella and Alan Manning:
“The UK has suffered from persistent spatial differences in unemployment rates for many decades. A low responsiveness of internal migration to unemployment is often argued to be an important cause of this problem. This paper uses UK census data to investigate how unemployment affects residential mobility using small areas as potential destinations and origins and four decades of data. It finds that both in- and out-migration are affected by local unemployment –
Posted by 11:13 AM
atLabels: Global Housing Watch
Sunday, February 6, 2022
New paper by YaojieZhang, M.I.M.Wahab & YudongWang in International Journal of Forecasting.
“This paper aims to improve the predictability of aggregate oil market volatility with a substantially large macroeconomic database, including 127 macro variables. To this end, we use machine learning from both the variable selection (VS) and common factor (i.e., dimension reduction) perspectives. We first use the lasso, elastic net (ENet), and two conventional supervised learning approaches based on the significance level of predictors’ regression coefficients and the incremental R-square to select useful predictors relevant to forecasting oil market volatility. We then rely on the principal component analysis (PCA) to extract a common factor from the selected predictors. Finally, we augment the autoregression (AR) benchmark model by including the supervised PCA common index. Our empirical results show that the supervised PCA regression model can successfully predict oil market volatility both in-sample and out-of sample. Also, the recommended models can yield forecasting gains in both statistical and economic perspectives. We further shed light on the nature of VS over time. In particular, option-implied volatility is always the most powerful predictor.”
Read more here.
New paper by YaojieZhang, M.I.M.Wahab & YudongWang in International Journal of Forecasting.
“This paper aims to improve the predictability of aggregate oil market volatility with a substantially large macroeconomic database, including 127 macro variables. To this end, we use machine learning from both the variable selection (VS) and common factor (i.e., dimension reduction) perspectives. We first use the lasso, elastic net (ENet), and two conventional supervised learning approaches based on the significance level of predictors’ regression coefficients and the incremental R-square to select useful predictors relevant to forecasting oil market volatility.
Posted by 9:54 AM
atLabels: Forecasting Forum
New post by Timothy Taylor.
“The Olympic Summer Games were held in Beijing in 2008. Now the Winter Games are being held there in 2022. For the sake of the athletes who will be inhaling and exhaling more frequently and deeply than usual in the next few weeks, how has the air quality changed? Michael Greenstone, Guojun He, and Ken Lee discuss the evidence in “The 2008 Olympics to the 2022 Olympics: China’s Fight to Win its War Against Pollution” (February 2022, Energy Policy Institute at the University of Chicago). They write:
In the years before the 2008 Beijing Summer Olympics, pollution in China had been sharply climbing. The government responded with quick reforms that temporarily reduced pollution during the games. The reforms, however, only managed to slow the climb in the long run. By 2013, pollution in China had reached record levels. The following year, the same year Beijing applied to host the 2022 Olympic Games, Chinese Premier Li Keqiang declared a “war against pollution” and vowed that China would tackle pollution with the same determination it used to tackle poverty. Seven years later, pollution has declined dramatically by about 40 percent. In Beijing, there is half as much pollution compared to both 2008 and 2013 levels. In most areas of China, pollution has fallen to levels not seen in more than two decades.”
Read more here.https://conversableeconomist.wpcomstaging.com/
New post by Timothy Taylor.
“The Olympic Summer Games were held in Beijing in 2008. Now the Winter Games are being held there in 2022. For the sake of the athletes who will be inhaling and exhaling more frequently and deeply than usual in the next few weeks, how has the air quality changed? Michael Greenstone, Guojun He, and Ken Lee discuss the evidence in “The 2008 Olympics to the 2022 Olympics: China’s Fight to Win its War Against Pollution” (February 2022,
Posted by 9:49 AM
atLabels: Energy & Climate Change
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