Sinem Hacioglu Hoke and Kerem Tuzcuoglu
We economists want to have our cake and eat it. We have far more data series at our disposal now than ever before. But using all of them in regressions would lead to wild “over-fitting” – finding random correlations in the data rather than explaining the true underlying relationships. Researchers using large data sets have historically experienced this dilemma – you can either throw away some of the information and retain clean, interpretable models; or keep most of the information but lose interpretability. This trade-off is particularly frustrating in a policy environment where understanding the identified relationships is crucial. However, in a recent working paper we show how to sidestep this trade-off by estimating a factor model with intuitive results.
Frank Eich and Jumana Saleheen.
Despite the fact that the financial crisis erupted nearly a decade ago, its legacy is still being felt today. Disappointingly weak growth and low interest rates are arguably part of that legacy (though other developments also matter), and policy makers are increasingly worried that these are no longer temporary phenomena but instead have become permanent features. This blog assesses what a prolonged period of weak growth and low interest rates (sometimes also referred to by “secular stagnation” or “low for long”) might mean for the viability of defined-benefit (DB) occupational pension schemes in the UK and what financial stability risks might arise as a result of a changing business environment.
Mauricio Armellini and Tim Pike.
This post highlights some of the possible economic implications of the so-called “Fourth Industrial Revolution” — whereby the use of new technologies and artificial intelligence (AI) threatens to transform entire industries and sectors. Some economists have argued that, like past technical change, this will not create large-scale unemployment, as labour gets reallocated. However, many technologists are less optimistic about the employment implications of AI. In this blog post we argue that the potential for simultaneous and rapid disruption, coupled with the breadth of human functions that AI might replicate, may have profound implications for labour markets. We conclude that economists should seriously consider the possibility that millions of people may be at risk of unemployment, should these technologies be widely adopted.
This blog discusses the impact of economic uncertainty on euro-area activity. To do that, we built on the methodology developed for the UK by Haddow et al. (2013). Our analysis suggests that elevated economic uncertainty has been an important driver of euro-area GDP during the financial and sovereign crisis, detracting (on average) around 0.5 pp from annual euro-area growth in the period between 2008Q3 and 2011Q3. As the shock unwound, GDP was boosted during the subsequent recovery. This analysis suggests that any further increase in uncertainty could have a materially negative impact on euro-area activity. Therefore, it needs to be carefully monitored by policy makers, particularly in the context of the upcoming political elections in a number of countries.
Real interest rates have fallen by around 5 percentage points since the 1980s. Many economists attribute this to “secular” trends such as a structural slowdown in global growth, changing demographics and a fall in the relative price of capital goods which will hold equilibrium rates low for a decade or more (Eggertsson et al., Summers, Rachel and Smith, and IMF). In this blog post, I argue this explanation is wrong because it’s at odds with pre-1980s experience. The 1980s were the anomaly (chart A). The decline in real rates over the 1990s and early 2000s simply reflected a return to historical norms from an unusually high starting point. Further falls since 2008 are far more plausibly related to the financial crisis than secular trends.
Philip Bunn, Jeanne Le Roux, Kate Reinold and Paolo Surico.
If you unexpectedly received £1000 of extra income this year, how much of it would you spend? All? Half? None? Now, by how much would you cut your spending if it had been an unexpected fall in income? Standard economic theory (for example the ‘permanent income hypothesis’) suggests that your answers should be symmetric. But there are good reasons to think that they might not be, for example in the face of limits on borrowing or uncertainty about future income. That is backed up by new survey evidence, which finds that an unanticipated fall in income leads to consumption changes which are significantly larger than the consumption changes associated with an income rise of the same size.
Empirical identification of the effects of monetary policy requires isolating exogenous shifts in the policy instrument that are distinct from the systematic response of the central bank to actual or foreseen changes in the economic outlook. Because the same tools are used to both induce changes in the economy, and to react to them, distinguishing between cause and effect is a far from trivial matter. One popular way is to use surprises in financial markets to proxy for the shock. In a recent paper, we argue that markets are not able to distinguish the shocks from the systematic component of policy if their forecasts do not align with those of the central bank. We thus develop a new measure of monetary shocks, based on market surprises but free of anticipatory effects and unpredictable by past information.
Philippe Bracke and Silvana Tenreyro.
When someone bought a house turns out to be an important factor in predicting whether the house will be sold again soon, and at what price. People who bought during a boom aim at achieving higher prices when they sell and, as a consequence, move less often. We explore whether this pattern is due to psychological anchoring (whereby the previous purchase price becomes an important reference point) or to the way the mortgage market works (for example, with homebuyers often using proceeds from house sales for down-payments on new properties).
Ian Billett and Patrick Schneider.
As time goes to infinity, the probability that a productivity analyst will wonder ‘which sectors are driving these trends?’ goes to one. We present an interactive sectoral productivity tool to help you explore this question without any fuss.
Paul Schmelzing, Harvard University.
Paul Schmelzing is a visiting scholar at the Bank from Harvard University, where he concentrates on 20th century financial history. In this guest post, he looks at the current bond market through the lens of nearly 800 years of economic history.
The economist Eugen von Böhm-Bawerk once opined that “the cultural level of a nation is mirrored by its interest rate: the higher a people’s intelligence and moral strength, the lower the rate of interest”. But as rates reached their lowest level ever in 2016, investors rather worried about the “biggest bond market bubble in history” coming to a violent end. The sharp sell-off in global bonds following the US election seems to confirm their fears. Looking back over eight centuries of data, I find that the 2016 bull market was indeed one of the largest ever recorded. History suggests this reversal will be driven by inflation fundamentals, and leave investors worse off than the 1994 “bond massacre”.