Tag: forecasting

A general election has been called in the UK for 12 December. Central to the debates between the parties will be their policy on Brexit.

They range from the Liberal Democrats’, Plaid Cymru’s and Sinn Féin’s policy of cancelling Brexit and remaining in the EU, to the Scottish Nationalists’ and Greens’ policy of halting Brexit while a People’s Vote (another referendum) is held, with the parties campaigning to stay in the EU, to the Conservative Party’s policy of supporting the Withdrawal Agreement and Political Declaration negotiated between the Boris Johnson government and the EU, to the DUP which supports Brexit but not a version which creates a border between Great Britain and Northern Ireland, to the Brexit Party and UKIP which support leaving the EU with no deal (what they call a ‘clean break’) and then negotiating individual trade deals on a country-by-country basis.

The Labour Party also supports a People’s Vote, but only after renegotiating the Withdrawal Agreement and Political Declaration, so that if Brexit took place, the UK would have a close relationship with the single market and remain in a customs union. Also, various laws and regulations on environmental protection and workers’ rights would be retained. The referendum would take place within six months of the election and would be a choice between this new deal and remain.

But what are the economic costs and benefits of these various alternatives? Prior to the June 2016 referendum, the Treasury costed various scenarios. After 15 years, a deal would make UK GDP between 3.4% and 7.8% lower than if it remained in the EU, depending on the nature of the deal. No deal would make GDP between 5.4% and 9.5% lower.

Then in November 2018, the Treasury published analysis of the original deal negotiated by Theresa May in July 2018 (the ‘Chequers deal’). It estimated that GDP would be up to 3.9% lower after 15 years than it would have been if the UK had remained in the EU. In the case of a no-deal Brexit, GDP would be up to 9.3% lower after 15 years.

When asked for Treasury forecasts of the effects of Boris Johnson’s deal, the Chancellor, Sajid Javid, said that the Treasury had not been asked to provide forecasts as the deal was “self-evidently in our economic interest“.

Other forecasters, however, have analysed the effects of the Johnson deal. The National Institute for Economic and Social Research (NIESR), the UK’s longest established independent economic research institute, has estimated the costs of various scenarios, including the Johnson deal, the May deal, a no-deal scenario and also a scenario of continuing uncertainty with no agreement over Brexit. The NIESR estimates that, under the Johnson deal, with a successful free-trade agreement with the EU, in 10 years’ time UK GDP will be 3.5% lower than it would be by remaining in the EU. This represents a cost of £70 billion. The costs would arise from less trade with the EU, lower inward investment, slower growth in productivity and labour shortages from lower migration. These would be offset somewhat by savings on budget contributions to the EU.

Under Theresa May’s deal UK GDP would be 3.0% lower (and thus slightly less costly than Boris Johnson’s deal). Continuing in the current situation with chronic uncertainty about whether the UK would leave or remain would leave the UK 2% worse off after 10 years. In other words, uncertainty would be less damaging than leaving. The costs from the various scenarios would be in addition to the costs that have already occurred – the NIESR estimates that GDP is already 2.5% smaller than it would have been as a result of the 2016 Brexit vote.

Another report also costs the various scenarios. In ‘The economic impact of Boris Johnson’s Brexit proposals’, Professors Anand Menon and Jonathan Portes and a team at The UK in a Changing Europe estimate the effects of a decline in trade, migration and productivity from the various scenarios – again, 10 years after new trading arrangements are in place. According to their analysis, UK GDP would be 4.9%, 6.4% and 8.1% lower with the May deal, the Johnson deal and no deal respectively than it would have been from remaining in the EU.

But how much reliance should we put on such forecasts? How realistic are their assumptions? What other factors could they have taken into account? Look at the two reports and at the articles discussing them and then consider the questions below which are concerned with the nature of economic forecasting.

Articles

Reports

Questions

  1. What are the arguments in favour of the assumptions and analysis of the two recent reports considered in this blog?
  2. What are the arguments against the assumptions and analysis of the two reports?
  3. How useful are forecasts like these, given the inevitable uncertainty surrounding (a) the outcome of negotiations post Brexit and (b) the strength of the global economy?
  4. If it could be demonstrated beyond doubt to everyone that each of the Brexit scenarios meant that UK GDP would be lower than if it remained in the EU, would this prove that the UK should remain in the EU? Explain.
  5. If economic forecasts turn out to be inaccurate, does this mean that economists should abandon forecasting?

There have been many analyses of the economic effects of Brexit, both before the referendum and at various times since, including analyses of the effects of the deal negotiated by Theresa May’s government and the EU. But with the prospect of a no-deal Brexit on 31 October under the new Boris Johnson government, attention has turned to the effects of leaving the EU without a deal.

There have been two major analyses recently of the likely effects of a no-deal Brexit – one by the International Monetary Fund (IMF) and one by the Office for Budget Responsibility (OBR).

IMF analysis

The first was in April by the IMF as part of its 6-monthly World Economic Outlook. In Scenario Box 1.1. ‘A No-Deal Brexit’ on page 28 of Chapter 1, the IMF looked at two possible scenarios.

Scenario A assumes no border disruptions and a relatively small increase in UK sovereign and corporate spreads. Scenario B incorporates significant border disruptions that increase import costs for UK firms and households (and to a lesser extent for the European Union) and a more severe tightening in financial conditions.

Under both scenarios, UK exports to the EU and UK imports from the EU revert to WTO rules. As a result, tariffs are imposed by mid-2020 or earlier. Non-tariff barriers rise at first but are gradually reduced over time. Most free-trade arrangements between the EU and other countries are initially unavailable to the UK (see the blog EU strikes major trade deals) but both scenarios assume that ‘new trade agreements are secured after two years, and on terms similar to those currently in place.’

Both scenarios also assume a reduction in net immigration from the EU of 25 000 per year until 2030. Both assume a rise in corporate and government bond rates, reflecting greater uncertainty, with the effect being greater in Scenario B. Both assume a relaxing of monetary and fiscal policy in response to downward pressures on the economy.

The IMF analysis shows a negative impact on UK GDP, with the economy falling into recession in late 2019 and in 2020. This is the result of higher trade costs and reduced business investment caused by a poorer economic outlook and increased uncertainty. By 2021, even under Scenario A, GDP is approximately 3.5% lower than it would have been if the UK had left the EU with the negotiated deal. For the rest of the EU, GDP is around 0.5% lower, although the effect varies considerably from country to country.

The IMF analysis makes optimistic assumptions, such as the UK being able to negotiate new trade deals with non-EU countries to replace those lost by leaving. More pessimistic assumptions would lead to greater costs.

OBR analysis

Building on the analysis of the IMF, the Office for Budget Responsibility considered the effect of a no-deal Brexit on the public finances in its biennial Fiscal risks report, published on 17 July 2019. This argues that, under the relatively benign Scenario A assumptions of the IMF, the lower GDP would result in annual public-sector net borrowing (PSNB) rising. By 2021/22, if the UK had left with the deal negotiated with the EU, PSNB would have been around £18bn. A no-deal Brexit would push this up to around £51bn.


According to the OBR, the contributors to this rise in public-sector net borrowing of around £33bn are:

  • A fall in income tax and national insurance receipts of around £16.5bn per year because of lower incomes.
  • A fall in corporation tax and expenditure taxes, such as VAT, excise duties and stamp duty of around £22.5bn per year because of lower expenditure.
  • A fall in capital taxes, such as inheritance tax and capital gains tax of around £10bn per year because of a fall in asset prices.
  • These are offset to a small degree by a rise in customs duties (around £10bn) because of the imposition of tariffs and by lower debt repayments (of around £6bn) because of the Bank of England having to reduce interest rates.

The rise in PSNB would constrain the government’s ability to use fiscal policy to boost the economy and to engage in the large-scale capital projects advocated by Boris Johnson while making the substantial tax cuts he is proposing. A less optimistic set of assumptions would, of course, lead to a bigger rise in PSNB, which would further constrain fiscal policy.

Articles

Video

Reports

Questions

  1. What are the assumptions of the IMF World Economic Outlook forecasts for the effects of a no-deal Brexit? Do you agree with these assumptions? Explain.
  2. What are the assumptions of the analysis of a no-deal Brexit on the public finances in the OBR’s Fiscal risks report? Do you agree with these assumptions? Explain.
  3. What is the difference between forecasts and analyses of outcomes?
  4. For what reasons might growth over the next few years be higher than in the IMF forecasts under either scenario?
  5. For what reasons might growth over the next few years be lower than in the IMF forecasts under either scenario?
  6. For what reasons might public-sector net borrowing (PSNB) over the next few years be lower than in the OBR forecast?
  7. For what reasons might PSNB over the next few years be higher than in the OBR forecast?

Today’s title is inspired from the British Special Air Service (SAS) famous catchphrase, ‘Who Dares Wins’ – similar variations of which have been adopted by several elite army units around the world. The motto is often credited to the founder of the SAS, Sir David Stirling (although similar phrases can be traced back to ancient Rome – including ‘qui audet adipiscitur’, which is Latin for ‘who dares wins’). The motto was used to inspire and remind soldiers that to successfully accomplish difficult missions, one has to take risks (Geraghty, 1980).

In the world of economics and finance, the concept of risk is endemic to investments and to making decisions in an uncertain world. The ‘no free lunch’ principle in finance, for instance, asserts that it is not possible to achieve exceptional returns over the long term without accepting substantial risk (Schachermayer, 2008).

Undoubtedly, one of the riskiest investment instruments you can currently get your hands on is cryptocurrencies. The most well-known of them is Bitcoin (BTC), and its price has varied spectacularly over the past ten years – more than any other asset I have laid my eyes on in my lifetime.

The first published exchange rate of BTC against the US dollar dates back to 5 October 2009 and it shows $1 to be exchangeable for 1309.03 BTC. On 15 December 2017, 1 BTC was traded for $17,900. But then, a year later the exchange rate was down to just over $1 = $3,500. Now, if this is not volatility I don’t know what is!

In such a market, wouldn’t it be wonderful if you could somehow predict changes in market sentiment and volatility trends? In a hot-off-the press article, Shen et al (2019) assert that it may be possible to predict changes in trading volumes and realised volatility of BTC by using the number of BTC-related tweets as a measure of attention. The authors source Twitter data on Bitcoin from BitInfoCharts.com and tick data from Bitstamp, one of the most popular and liquid BTC exchanges, over the period 4/9/2014 to 31/8/2018.

According to the authors:

This measure of investor attention should be more informed than that of Google Trends and therefore may reflect the attention Bitcoin is receiving from more informed investors. We find that the volume of tweets are significant drivers of realised [price] volatility (RV) and trading volume, which is supported by linear and nonlinear Granger causality tests.

They find that, according to Granger causality tests, for the period from 4/9/2014 to 8/10/2017, past days’ tweeting activity influences (or at least forecasts) trading volume. While from 9/10/2017 to 31/8/2018, previous tweets are significant drivers/forecasters of not only trading volume but also realised price volatility.

And before you reach out for your smartphone, let me clarify that, although previous days’ tweets are found in this paper to be good predictors of realised price volatility and trading volume, they have no significant effect on the returns of Bitcoin.

Article

References

Questions

  1. Explain how the number of tweets can be used to gauge investors’ intentions and how it can be linked to changes in trading volume.
  2. Using Google Scholar, make a list of articles that have used Twitter and Google Trends to predict returns, volatility and trading volume in financial markets. Present and discuss your findings.
  3. Would you invest in Bitcoin? Why yes? Why no?

It is impossible to make both precise and accurate forecasts of a country’s rate of economic growth, even a year ahead. And the same goes for other macroeconomic variables, such as the rate of unemployment or the balance of trade. The reason is that there are so many determinants of these variables, such as political decisions or events, which themselves are unpredictable. Economics examines the effects of human interactions – it is a social science, not a natural science. And human behaviour is hard to forecast.

Leading indicators

Nevertheless, economists do make forecasts. These are best estimates, taking into account a number of determinants that can be currently measured, such as tax or interest rate changes. These determinants, or ‘leading indicators’, have been found to be related to future outcomes. For example, surveys of consumer and business confidence give a good indication of future consumer expenditure and investment – key components of GDP.

Leading indicators do not have to be directly causal. They could, instead, be a symptom of underlying changes that are themselves likely to affect the economy in the future. For example, changes in stock market prices may reflect changes in confidence or changes in liquidity. It is these changes that are likely to have a direct or indirect causal effect on future output, employment, prices, etc.

Macroeconomic models show the relationships between variables. They show how changes in one variable (e.g. increased investment) affect other variables (e.g. real GDP or productivity). So when an indicator changes, such as a rise in interest rates, economists use these models to estimate the likely effect, assuming other things remain constant (ceteris paribus). The problem is that other things don’t remain constant. The economy is buffeted around by a huge range of events that can affect the outcome of the change in the indicator or the variable(s) it reflects.

Forecasting can never therefore be 100% accurate (except by chance). Nevertheless, by carefully studying leading indicators, economists can get a good idea of the likely course of the economy.

Leading indicators of the US economy

At the start of 2019, several leading indicators are suggesting the US economy is likely to slow and might even go into recession. The following are some of the main examples.

Political events. This is the most obvious leading indicator. If decisions are made that are likely to have an adverse effect on growth, a recession may follow. For example, decisions in the UK Parliament over Brexit will directly impact on UK growth.

As far as the USA is concerned, President Trump’s decision to put tariffs on steel and aluminium imports from a range of countries, including China, the EU and Canada, led these countries to retaliate with tariffs on US imports. A tariff war has a negative effect on growth. It is a negative sum game. Of course, there may be a settlement, with countries agreeing to reduce or eliminate these new tariffs, but the danger is that the trade war may continue long enough to do serious damage to global economic growth.

But just how damaging it is likely to be is impossible to predict. That depends on future political decisions, not just those of the recent past. Will there be a global rise in protectionism or will countries pull back from such a destructive scenario? On 29 December, President Trump tweeted, ‘Just had a long and very good call with President Xi of China. Deal is moving along very well. If made, it will be very comprehensive, covering all subjects, areas and points of dispute. Big progress being made!’ China said that it was willing to work with the USA over reaching a consensus on trade.

Rises in interest rates. If these are in response to a situation of excess demand, they can be seen as a means of bringing inflation down to the target level or of closing a positive output gap, where real national income is above its potential level. They would not signify an impending recession. But many commentators have interpreted rises in interest rates in the USA as being different from this.

The Fed is keen to raise interest rates above the historic low rates that were seen as an ’emergency’ response to the financial crisis of 2007–8. It is also keen to reverse the policy of quantitative easing and has begun what might be described as ‘quantitative tightening’: not buying new bonds when existing ones that it purchased during rounds of QE mature. It refers to this interest rate and money supply policy as ‘policy normalization‘. The Fed maintains that such policy is ‘consistent with sustained expansion of economic activity, strong labor market conditions, and inflation near the Committee’s symmetric 2 percent objective over the medium term’.

However, many commentators, including President Trump, have accused the Fed of going too fast in this process and of excessively dampening the economy. It has already raised the Federal Funds Rate nine times by 0.25 percentage points each time since December 2015 (click here for a PowerPoint file of the chart). What is more, announcing that the policy will continue makes such announcements themselves a leading indicator of future rises in interest rates, which are a leading indicator of subsequent effects on aggregate demand. The Fed has stated that it expects to make two more 0.25 percentage point rises during 2019.

Surveys of consumer and business confidence. These are some of the most significant leading indicators as consumer confidence affects consumer spending and business confidence affects investment. According to the Duke CFO Global Business Outlook, an influential survey of Chief Financial Officers, ‘Nearly half (48.6 per cent) of US CFOs believe that the US will be in recession by the end of 2019, and 82 per cent believe that a recession will have begun by the end of 2020’. Such surveys can become self-fulfilling, as a reported decline in confidence can itself undermine confidence as both firms and consumers ‘catch’ the mood of pessimism.

Stock market volatility. When stock markets exhibit large falls and rises, this is often a symptom of uncertainty; and uncertainty can undermine investment. Stock market volatility can thus be a leading indicator of an impending recession. One indicator of such volatility is the VIX index. This is a measure of ’30-day expected volatility of the US stock market, derived from real-time, mid-quote prices of S&P 500® Index (SPXSM) call and put options. On a global basis, it is one of the most recognized measures of volatility – widely reported by financial media and closely followed by a variety of market participants as a daily market indicator.’ The higher the index, the greater the volatility. Since 2004, it has averaged 18.4; from 17 to 28 December 2018, it averaged 28.8. From 13 to 24 December, the DOW Jones Industrial Average share index fell by 11.4 per cent, only to rise by 6.2 per cent by 27 December. On 26 December, the S&P 500 index rallied 5 per cent, its best gain since March 2009.

Not all cases of market volatility, however, signify an impending recession, but high levels of volatility are one more sign of investor nervousness.

Oil prices. When oil prices fall, this can be explained by changes on the demand and/or supply side of the oil market. Oil prices have fallen significantly over the past two months. Until October 2018, oil prices had been rising, with Brent Crude reaching $86 per barrel by early October. By the end of the year the price had fallen to just over $50 per barrel – a fall of 41 per cent. (Click here for a PowerPoint file of the chart.) Part of the explanation is a rise in supply, with shale oil production increasing and also increased output from Russia and Saudi Arabia, despite a commitment by the two countries to reduce supply. But the main reason is a fall in demand. This reflects both a fall in current demand and in anticipated future demand, with fears of oversupply causing oil companies to run down stocks.

Falling oil prices resulting from falling demand are thus an indicator of lack of confidence in the growth of future demand – a leading indicator of a slowing economy.

The yield curve. This depicts the yields on government debt with different lengths to maturity at a given point in time. Generally, the curve slopes upwards, showing higher rates of return on bonds with longer to maturity. This is illustrated by the blue line in the chart. (Click here for a PowerPoint file of the chart.) This is as you would expect, with people requiring a higher rate of return on long-term lending, where there is normally greater uncertainty. But, as the Bloomberg article, ‘Don’t take your eyes off the yield curve‘ states:

Occasionally, the curve flips, with yields on short-term debt exceeding those on longer bonds. That’s normally a sign investors believe economic growth will slow and interest rates will eventually fall. Research by the Federal Reserve Bank of San Francisco has shown that an inversion has preceded every US recession for the past 60 years.
 
The US economy is 37 quarters into what may prove to be its longest expansion on record. Analysts surveyed by Bloomberg expect gross domestic product growth to come in at 2.9 percent this year, up from 2.2 percent last year. Wages are rising as unfilled vacancies hover near all-time highs.
 
With times this good, the biggest betting game on Wall Street is when they’ll go bad. Barclays Plc, Goldman Sachs Group Inc., and other banks are predicting inversion will happen sometime in 2019. The conventional wisdom: Afterward it’s only a matter of time – anywhere from 6 to 24 months – before a recession starts.

As you can see from the chart, the yield curve on 24 December 2018 was still slightly upward sloping (expect between 6-month and 1-year bonds) – but possibly ready to ‘flip’.

However, despite the power of an ‘inverted’ yield in predicting previous recessions, it may be less reliable now. The Fed, as we saw above, has already signalled that it expects to increase short-term rates in 2019, probably at least twice. That alone could make the yield curve flatter or even downward sloping. Nevertheless, it is still generally thought that a downward sloping yield curve would signal belief in a likely slowdown, if not outright recession.

So, is the USA heading for recession?

The trouble with indicators is that they suggest what is likely – not what will definitely happen. Governments and central banks are powerful agents. If they believed that a recession was likely, then fiscal and monetary policy could be adjusted. For example, the Fed could halt its interest rate rises and quantitative tightening, or even reverse them. Also, worries about protectionism may subside if the USA strikes new trade deals with various countries, as it did with Canada and Mexico in USMCA.

Articles

Surveys and Data

Questions

  1. Define the term ‘recession’.
  2. Are periods of above-trend expansion necessarily followed by a recession?
  3. Give some examples of leading indicators other than those given above and discuss their likely reliability in predicting a recession.
  4. Find out what has been happening to confidence levels in the EU over the past 12 months. Does this provide evidence of an impending recession in the EU?
  5. For what reasons may there be lags between a change in an indicator and a change in the variables for which it is an indicator?
  6. Why has the shape of the yield curve previously been a good predictor of the future course of the economy? Is it likely to be at present?
  7. What is the relationship between interest rates, government bond prices (‘Treasuries’ in the USA) and the yield on such bonds?

The Winter Olympics are full on as athletes from all over the world compete against each other, hoping to set new world records, win medals and be known as Olympians. Pyeongchang, the South Korean county that hosts the 2018 Winter games, enjoys a large influx of tourists – estimated at 80,000 people a day. This is certainly an unusually large number of tourists for a region that has a regular winter-time population of no more than 45,000 people.

Having such a high number of visitors to the Winter Olympics, and even more to the larger Summer Olympics, is not an unusual occurrence, however, and it is often mentioned as one of the benefits of being a host to the Olympic Games.

Baade and Matheson (see link below) distinguish between three key benefits of hosting the Olympic Games: “the short-run benefits of tourist spending during the Games; the long-run benefits or the ‘Olympic legacy’, which might include improvements in infrastructure and increased trade, foreign investment, or tourism after the Games; and intangible benefits such as the ‘feel-good effect’ or civic pride”.

On these grounds, a number of studies have been authored, attempting to analyse some or all of these benefits, distinguishing between short-term and long-term effects. Müller (see link below), uses data from the 2014 Oympic Games in Sochi, Russia, to assess the net economic outcome for the host region. He concludes that any short-term economic benefits caused by the investment influx (before and during the games) could not offset the long-term costs, leading to an estimated net loss of $1.2 billion per year.

Zimbalist (2015) and Szymanski (2011) report similar results when analysing data from the London Games (2012) and past major sporting events (Games and FIFA World Cup). Kasimati (2003) points out the significant economic benefits that host regions tend to enjoy for years after hosting the games, but argues that the overall effect depends on a number of factors (including pre-existing infrastructure and location).

The jury is, therefore, still out on what is the overall economic effect of being host to this ancient institution. But I must now dash as women’s hockey is soon to start. “Let everyone shine”.

Articles

For the sake of the games, South Korea needs to show hosting an Olympics can be economically viable CNBC, Yen Nee Lee (15/2/18)
South Korea’s Olympic bet is unlikely to pay off, economics professor says CNBC, Andrew Wong and Andrew Zimbalist (12/2/18)
Going for the Gold: The Economics of the Olympics Journal of Economic Perspectives, Robert A. Baade and Victor A. Matheson (Spring 2016)
After Sochi 2014: Costs and Impacts of Russia’s Olympic Games Eurasian Geography and Economics, Martin Müller (9/4/15)
Circus Maximus: The Economic Gamble Behind Hosting the Olympics and the World Cup The Brookings Institution, Andrew Zimbalist (14/1/15)
About Winning: The Political Economy of Awarding the World Cup and the Olympic Games SAIS Review of International Affairs, Stefan Szymanski (Winter/Spring 2011)
Economic aspects and the Summer Olympics: a review of related research International Journal of Tourism Research, Evangelia Kasimati (4/11/03)
“Let Everyone Shine”: the song for the PyeongChang 2018 Torch Relay unveiled with 200 days to go Olympic Committee (24/7/17)

Video

The Olympic Winter Games PyeongChang 2018 Torch Relay Official Song PyeongChang 2018

Questions

  1. Using supply and demand diagrams, explain whether you would expect hotel room prices to change during the hosting of a major sports event, such as the Winter Olympics.
  2. List three economic (or economics-related) arguments in favour of and against the hosting of the Olympic games. Relate your answer to the empirical evidence presented in the literature.
  3. Why is it so difficult to estimate with accuracy the net economic effect of the Olympic Games?