The Unassuming Economist

From https://voxeu.org/

By Elena Bobeica, Gabriel Pérez-Quirós, Gerhard Rünstler, Georg Strasserposted on 31 October 2021 

“The recent decade has shown that forecasters need to continuously adapt their tools to cope with increasing macroeconomic complexity. Just like the global crisis, the current Covid-19 pandemic highlights once again that forecasters cannot be content with just assessing the single most likely future outcome – such as a single number for future GDP growth in a certain year. Instead, a characterisation of all possible outcomes (i.e. the entire distribution) is necessary to understand the likelihood and nature of extreme events.

This is key for central bank forecasters as well, as pointed out by ECB Executive Board member Philip Lane in his opening remarks at the 11th Conference on Forecasting Techniques. Central banks rely heavily on forecasts to design their policy and need robust techniques to navigate through turbulent times. They not only ensure price stability and are thus directly interested in the most likely future inflation path, but in the process also contribute to the understanding, managing, and handling of macro-economic risks and thus need to grasp the likelihood of extreme events (see also the discussion in Greenspan 2004).”

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From a new CESifo working paper by Kajal Lahiri & Cheng Yang

“We forecast New York state tax revenues with a mixed-frequency model using a number of machine learning techniques. We found boosting with two dynamic factors extracted from a select list of New York and U.S. leading indicators did best in terms of correctly updating revenues for the fiscal year in direct multi-step out-of-sample forecasts. These forecasts were found to be informationally efficient over 18 monthly horizons. In addition to boosting with factors, we also studied the advisability of restricting boosting to select the most recent macro variables to capture abrupt structural changes. Since the COVID-19 pandemic upended all government budgets, our boosted forecasts were used to monitor revenues in real time for the fiscal year 2021. Our estimates showed a drastic year-over-year decline in real revenues by over 16% in May 2020, followed by several upward nowcast revisions that led to a recovery to -1% in March 2021, which was close to the actual annual value of -1.6%.”

From VoxEU.ORG

Expectations data indicate the US is entering recession about now

By Alex Bryson, David Blanchflower 21 October 2021

“With the mass rollout of COVID-19 vaccinations and the attendant decline in COVID-related deaths in most advanced economies, and with many economic indices turning positive, it looks like most economies are on the road to recovery although the data paint a confusing picture. 

For example, in the spring of 2020, wage growth jumped sharply at the same time unemployment was rising. This was in both the US and the UK. Since then, unemployment has been falling while wage growth remains high. This implies the wage curve slopes up, which seems unlikely. But other metrics are telling a different story, most notably those capturing consumer and business sentiment.

Two series – from The Conference Board on business conditions, employment and income six months hence, and from the University of Michigan on the financial situation in a year and business conditions a year and five years hence – tell the same story: sentiment peaked in spring or early summer. And it has been falling precipitously since (Blanchflower and Bryson 2021a). This is true for the US as a whole and for the eight largest states for which The Conference Board collect data.”

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From the Financial Times:

“The UK’s national weather service, the Met Office, is to get a £1.2bn computer to help with its forecasting activities. That is a lot of silicon. My instinctive response was: when do we economists get one?

People may grumble about the weather forecast, but in many places we take its accuracy for granted. When we ask our phones about tomorrow’s weather, we act as though we are gazing through a window into the future. Nobody treats the latest forecasts from the Bank of England or the IMF as a window into anything.

That is partly because politics gets in the way. On the issue of Brexit, for example, extreme forecasts from partisans attracted attention, while independent mainstream forecasters have proved to be pretty much on the money. Few people stopped to praise the economic bean-counters.

Economists might also protest that nobody asks them to forecast economic activity tomorrow or even next week; they are asked to describe the prospects for the next year or so. True, some almanacs offer long-range weather forecasts based on methods that are secret, arcane, or both — but the professionals regard such attempts as laughable.

Enough excuses; economists deserve few prizes for prediction. Prakash Loungani of the IMF has conducted several reviews of mainstream forecasts, finding them dismally likely to miss recessions. Economists are not very good at seeing into the future — to the extent that most argue forecasting is simply none of their business. The weather forecasters are good, and getting better all the time. Could we economists do as well with a couple of billion dollars’ worth of kit, or is something else lacking?

The question seemed worth exploring to me, so I picked up Andrew Blum’s recent book, The Weather Machine, to understand what meteorologists actually do and how they do it. I realised quickly that a weather forecast is intimately connected to a map in a way that an economic forecast is not.”

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From a new VOX post on long-term growth forecasts:

“Although long-term macroeconomic forecasts substantially affect the sustainability of government debt and the social security system, they cannot avoid significant uncertainty. This column assesses whether academic researchers in economics make accurate long-term growth forecasts, comparing ten-year growth forecasts made by Japanese economists in 2006–2007 with the realised figures. Even excluding the years affected by the Global Crisis, the results show that forecasts tend to be biased upwards and involve significant uncertainty, even for economics researchers specialising in macroeconomics or economic growth.”

“Figure 1 shows the means and medians of forecast errors. Forecasts by the researchers in economics have an optimistic bias, similar to the findings for the forecasts by the government agencies. Even after removing the two years affected by the Global Crisis to calculate annual growth rates, a non-negligible upward bias remains: about 0.5-0.6 percentage points for real GDP growth and about 1.3-1.4 percentage points for nominal GDP growth. ”