Global surveys or hard data which are the fake news?

Transcription

1 by Gavyn Davies The Fulcrum global nowcasts continue to report very strong growth in global economic activity, especially in the advanced economies. These results contrast with weaker reports from official GDP-based hard data, with the largest discrepancy arising in the US. The Fulcrum economics team, led by Juan Antolin-Diaz, has released a detailed technical paper analyzing the differences between the influence of hard data and survey data on our nowcast models. They conclude that the inclusion of survey data in the models increases their accuracy and timeliness. They also argue that the sluggishness of hard data reports in the US may be explained by persistent seasonal adjustment problems in the official data releases. Overall, we expect official data and consensus GDP forecasts for 2017 to rise substantially in coming months. In this month s regular update on global economic activity, the Fulcrum nowcasts have once again identified extremely strong growth rates, especially in the advanced economies (see Figure 1). These results continue to suggest that the global economy is expanding at the fastest rate seen since 2010, with the implication that the expansion may be reaching escape velocity, where it is no longer in need of emergency support from the central banks or fiscal authorities. However, our models have been greatly affected in recent months by the remarkable strength in business and consumer surveys. Hard economic data have also improved, but have done so by less than the surveys. This has led to doubts about the reliability of the nowcasts, especially in the US, where the official real GDP growth rate in 2017 Q1 seems likely to be well below 2 per cent for the second successive quarter. The sluggishness of US growth based on the official GDP data is clearly influencing the Federal Reserve, which has made no upward revision to its growth forecasts in the past few months, despite the surge in the nowcasts. Furthermore, it may also have influenced the financial markets, which are starting to have doubts about the reflation trade in global markets. Given the extremely large difference between surveys and hard data at present, it is important to consider which of these sources of evidence is likely to be giving the correct signal on the current pace of the global expansion. Based on past evidence, we continue to give considerable weight to the buoyant surveys, even when they conflict with the relative weakness of hard data. A second question is whether the extremely buoyant growth rates identified by the nowcasts can be maintained into the future. The models expect these elevated growth rates to decline somewhat over the rest of 2017, but growth seems likely to remain well above trend in the AEs, with plenty of scope for upward revisions to consensus GDP forecasts. Correspondence: <research@fulcrumasset.com>, Department of Macroeconomic Research, Fulcrum Asset Management LLP, 66 Seymour Street, London W1H 5BT.

2 Figure 1: World (Latest Estimate = 4.4) Notes: The black dashed represents the model s estimate of long-run growth. The real-time estimate of underlying activity (blue line) considers only the data available at each point in time. The historical estimate (red line) uses all available data as of today, including revisions. Estimated using the model of Antolin-Diaz, Drechsel, and Petrella (2015). See note to Table 1 for the definition of the aggregates. My colleagues Juan Antolin-Diaz, Thomas Drechsel and Ivan Petrella have released a technical paper about the use of hard and soft data in nowcasting see the latest draft attached(antolin-diaz et al., 2017). Latest Nowcasts It is important to recognise the unusual strength in recent nowcasts, given the weak and patchy history of the recovery since The global economy is now estimated to be growing at 4.4 per cent, compared to an underlying trend rate of 3.7 per cent. This compares to a low point for global growth of only 2.5 per cent in February The improvement has been most pronounced in the advanced economies (AEs), which are now estimated to be expanding at a rate of 3.5 per cent, much higher than the 1.8 per cent underlying trend (see Figure 2). The US is the leading economy at present. The estimated monthly activity growth rate has risen to 4.3 per cent in March, and the model suggests that the quarterly growth rate for 2017 Q1 as a whole has been as high as 3.3 per cent. (Note that our model is not attempting to identify the first official estimate of quarterly official GDP growth, because we believe this is a noisy indicator of the true state of the business cycle see below.) All other AEs are also reporting growth rates well above their underlying trends: Eurozone 2.8 per cent; Japan 1.7 per cent; UK 2.3 per cent. None of the major AEs is showing any sign of slowing down at present, though the upward momentum in the growth rate seems to have flattened in February and March. 2

3 Figure 2: Estimate of Underlying Activity Growth (% MoM Ann.) Advanced Economies Emerging Markets Notes: The black dashed represents the model s estimate of long-run growth. The real-time estimate of underlying activity (blue line) considers only the data available at each point in time. The historical estimate (red line) uses all available data as of today, including revisions. Estimated using the model of Antolin-Diaz, Drechsel, and Petrella (2015). See note to Table 1 for the definition of the aggregates. In the emerging markets, activity growth is less impressive, but remains fairly solid at close to its 5 per cent trend rate. China is growing at 7.0 per cent, the high end of the government s official growth target for Hard data vs. soft data Although the Fulcrum nowcasts are currently giving the same buoyant message as alternative nowcasters, including J.P. Morgan and the New York Fed, many analysts have expressed caution because the results are driven in part by the recent strength of business and consumer surveys, especially in the US. 1 It has been suggested that the election of President Trump has produced a surge in small business and consumer confidence (among Republican supporters) and it is argued that this may not fully translate into a strengthening in the hard data that go into the official GDP calculations. Scepticism about the true state of the US economy has been increased by the weakness of the Atlanta Fed nowcast, which is a widely watched data tracker (rather than a true nowcast in the usual use of the term nowadays) that has tended to produce good signals for the first official GDP release each quarter. The latest reading of the Atlanta nowcast for 2017 Q1 is only 0.9 per cent, while many other GDP trackers are hovering around 1.5 per cent. 2 Because of these large discrepancies, my Fulcrum colleagues have just produced a formal analysis of the relative importance and recent behaviour of hard and soft data in our nowcasting models of the US economy. This demonstrates the following: The inclusion of survey or soft data in nowcasting models is crucial to improve the accuracy of the signals produced by these models in real time. This point is well established in the literature and should not be at all controversial. Nowcasts that are produced using hard data only are much more volatile than those using soft data only, though both models have similar characteristics over business cycle and 1 Nowcasting Report, Federal Reserve Bank of New York, 31 March GDPNow, Federal Reserve Bank of Atlanta, 31 March

4 longer frequencies. Soft data are published on a much more timely basis than hard data, which often lag the reference period by several weeks. Because soft data are almost always the earliest series to appear in any given month, they are particularly important for an early read on growth data. The nowcast model using only hard data is currently reporting US activity growth of around 2 per cent, while the version of the model using only soft data suggests around 4 per cent. This demonstrates that the widespread perception of a large gap between the two series at present is accurate (see graph below). Not only do the soft data improve the contemporaneous nowcast for overall economic activity, they also predict the future behaviour of the hard data when there is a difference between the two. This may indicate that the hard data will pick up in coming months. In the US, GDP growth data for the first quarter of the calendar year have been systematically understated, despite repeated attempts by the BEA to correct this problem. According to recent work by the Cleveland Fed (see Lunsford, 2017) and the St Louis Fed, this problem with seasonal adjustment reduces the reported growth rate by per cent in Q1, and this factor may account for much of the sluggishness seen in the official data in 2017 Q1. 3 The strength of activity growth identified in our latest US nowcasts is also seen in the nowcasts for all other AEs. The synchronised nature of the upswing in so many economies increases our confidence that the buoyancy of US growth is not just a consequence of fake survey news in the American economy. In conclusion, we believe that the hard data in the US are understating activity growth in that economy, and consequently in the official GDP series for the global aggregate. We expect to see this reflected in much stronger growth in US hard data and the official GDP series for 2017 Q2. Global activity growth is unlikely to be maintained quite at the elevated levels identified by our nowcasts in Q1 (because there is statistical mean reversion in the forecasts), but we expect consensus forecasts for global growth in the 2017 calendar year to be revised upwards, perhaps substantially Residual Seasonality: The Return of an Old First-Quarter Friend?, Kevin L. Kliesen, On the Economy Blog, 27 March 4

5 % Annualized Growth Global surveys or hard data which are the fake news? Figure 3: US GDP Nowcasts would be much lower without Soft Data) Note: The solid black line is the quarterly annualized GDP growth rate. The solid red line is the fitted growth rate from the model, i.e. the quarterly hard factor plus the long run growth rate of GDP. The blue bands represent the posterior predictive density for the quarterly GDP path. The dotted red line is the counterfactual fit of the model estimated under the assumption that the soft data observations since January 2017 are unavailable. 5

6 Methodological note The Fulcrum nowcasts are estimated using the model of Antolin-Diaz, Drechsel, and Petrella (2015). This model belongs to the class of Dynamic Factor Models in the spirit of Geweke (1977), Stock and Watson (2002) and Forni et al. (2009). They capture the idea that a small number of unobserved factors drives the comovement of a possibly large number of macroeconomic time series, each of which may be contaminated by measurement error or other sources of idiosyncratic variation. Giannone et al. (2008) and Banbura et al. (2012) have pioneered the use of DFMs to produce current-quarter forecasts ( nowcasts ) of GDP growth by exploiting more timely monthly indicators and the factor structure of the data. Antolin-Diaz, Drechsel, and Petrella (2015) modify the standard DFM framework to account for the possibility of secular changes in the long-run growth rate of GDP. This is particularly relevant given the current interest in secular stagnation hypothesis, and for emerging market economies, which undergo low-frequency changes in their rate of growth. Formally, they specify that y t, a (n 1) vector of observable time series, is driven by a latent common factor, f t. Ordering GDP growth first (therefore GDP growth is referred to as y 1,t ) they have y 1,t = α 1,t + f t + u 1,t, (1) y i,t = α i + λ i f t + u i,t, i = 2,..., n, (2) where u i,t is an idiosyncratic innovation specific to the i th series and λ i is its loading on the common factor. Unlike in the standard DFM, where all parameters are constant, in the ADP framework the intercept α 1,t is time-dependent in equation (1), allowing the mean growth rate of GDP to vary. The laws of motion for the factor and idiosyncratic components are, respectively, Φ(L)f t = ε t, (3) ρ i (L)u i,t = η i,t, i = 1,..., n, (4) where Φ(L) and ρ i (L) denote polynomials in the lag operator of orders p and q, respectively. Both (3) and (4) are covariance stationary processes. The disturbances are distributed as ε t iid N(0, σ 2 ε,t) and η i,t iid N(0, σ 2 η i,t ). By allowing for time-variation in σ 2 ε,t and σ 2 η i,t, the model also introduces stochastic volatility in the innovations to the factor and to the idiosyncratic components of all series, which is critical to provide a realistic assessment of the risks to the nowcasts. 6

7 Figure 4: Underlying Activity Growth (% MoM Ann.) for Global Aggregates World Advanced Economies Emerging Market Economies Notes: The black dashed line represents the model s estimate of long-run growth. The solid blue line represents the estimate of underlying activity using only the data available at each point in time (real-time estimate). The dark and the light blue areas represent respectively the 68% and the 90% confidence bands. See note to Table 1 for the definition of the aggregates. 7

8 Figure 5: Underlying Activity Growth (% MoM Ann.) for Main Economies United States Euro Area China Japan Note: Euro Area is the PPP-weighted average of Germany, France, Italy and Spain. See note to Figure 4 for a guide to graphs interpretation. 8

9 Global surveys or hard data which are the fake news? Figure 6: Underlying Activity Growth (% MoM Ann.) for Other Economies United Kingdom Germany France Italy Spain Canada Sweden Norway Brazil India Russia Korea Mexico Note: See note to Figure 4 for a guide to graphs interpretation. 9

10 Table 1: Real GDP Growth (% QoQ Ann.) Q Q Q Q Q Long Run World Adv. Economies USA Euro Area Germany France Italy Spain UK Japan Canada Sweden Norway Em. Markets BRICs China Brazil India Russia Korea Mexico Note: World is the PPP-weighted average of US, Euro Area, UK, Japan, Canada, Sweden, Norway, Brazil, Russia, India, China, Korea and Mexico. Advanced economies is the PPP-weighted average of US, Euro Area, UK, Japan, Canada, Sweden and Norway. Emerging markets is the PPP-weighted average of Brazil, Russia, India, China, Korea and Mexico. Euro Area is the PPP-weighted average of Germany, France, Italy and Spain. 10

11 Figure 7: Global Aggregates: Industrial Production (% MoM Ann., 3M moving average) World Advanced Economies Emerging Market Economies Notes: Nowcasts are in red. World is the PPP-weighted average of US, Euro Area, UK, Japan, Canada, Sweden, Norway, Brazil, Russia, India, China, Korea and Mexico. Advanced economies is the PPP-weighted average of US, Euro Area, UK, Japan, Canada, Sweden and Norway. Emerging markets is the PPP-weighted average of Brazil, Russia, India, China, Korea and Mexico. 11

12 Figure 8: Regional Aggregates: Industrial Production (% MoM Ann., 3M moving average) North America Europe Asia Notes: Nowcasts are in red. North America is the PPP-weighted average of US, Canada and Mexico. Europe is the PPP-weighted average of Euro Area, UK, Sweden and Norway. Asia is the PPP-weighted average of Japan, India, China and Korea. 12

13 Figure 9: Regional Aggregates: Industrial Production (% MoM Ann., 3M moving average) BRICs Latin America Emerging Asia Notes: Nowcasts are in red. BRICs is the PPP-weighted average of Brazil, Russia, India and China. Latin America is the PPP-weighted average of Brazil and Mexico. Emerging Asia is the PPP-weighted average of India, China and Korea. 13

14 Figure 10: Main Economies: Industrial Production (% MoM Ann., 3M moving average) United States Euro Area China Japan Note: Nowcasts are in red. Euro Area is the PPP-weighted average of Germany, France, Italy and Spain. 14

15 Global surveys or hard data which are the fake news? Figure 11: Other Economies: Industrial Production (% MoM Ann., 3M moving average) United Kingdom Germany France Italy Spain Canada Sweden Norway Brazil India Russia Korea Mexico Note: Nowcasts are in red. 15

16 Figure 12: Global Aggregates: Manufacturing Production (% MoM Ann., 3M moving average) World Advanced Economies Emerging Market Economies Notes: Nowcasts are in red. World is the PPP-weighted average of US, Euro Area, UK, Japan, Canada, Sweden, Norway, Brazil, Russia, India, China, Korea and Mexico. Advanced economies is the PPP-weighted average of US, Euro Area, UK, Japan, Canada, Sweden and Norway. Emerging markets is the PPP-weighted average of Brazil, Russia, India, China, Korea and Mexico. Industrial Production data are used for China. 16

17 Figure 13: Regional Aggregates: Manufacturing Production (% MoM Ann., 3M moving average) North America Europe Asia Notes: Nowcasts are in red. North America is the PPP-weighted average of US, Canada and Mexico. Europe is the PPP-weighted average of Euro Area, UK, Sweden and Norway. Asia is the PPP-weighted average of Japan, India, China and Korea. Industrial Production data are used for China. 17

18 Figure 14: Regional Aggregates: Manufacturing Production (% MoM Ann., 3M moving average) BRICs Latin America Emerging Asia Notes: Nowcasts are in red. BRICs is the PPP-weighted average of Brazil, Russia, India and China. Latin America is the PPP-weighted average of Brazil and Mexico. Emerging Asia is the PPP-weighted average of India, China and Korea. Industrial Production data are used for China. 18

19 Figure 15: Main Economies: Manufacturing Production (% MoM Ann., 3M moving average) United States Euro Area China Japan Note: Nowcasts are in red. Euro Area is the PPP-weighted average of Germany, France, Italy and Spain. Industrial Production data are used for China. 19

20 Global surveys or hard data which are the fake news? Figure 16: Other Economies: Manufacturing Production (% MoM Ann., 3M moving average) United Kingdom Germany France Italy Spain Canada Sweden Norway Brazil India Russia Korea Mexico Note: Nowcasts are in red. 20

21 References Antolin-Diaz, J., T. Drechel, and I. Petrella (2017): Hard vs. Soft in Nowcasting GDP: A Comment, [Online; posted 02-April- 2017]. Antolin-Diaz, J., T. Drechsel, and I. Petrella (2015): Following the Trend: Tracking GDP when long-run growth is uncertain, CEPR Discussion Papers 10272, C.E.P.R. Discussion Papers. Banbura, M., D. Giannone, M. Modugno, and L. Reichlin (2012): Now-Casting and the Real- Time Data Flow, Working Papers ECARES , ULB Universite Libre de Bruxelles. Forni, M., D. Giannone, M. Lippi, and L. Reichlin (2009): Opening The Black Box: Structural Factor Models With Large Cross Sections, Econometric Theory, 25, Geweke, J. (1977): The Dynamic Factor Analysis of Economic Time Series, in Latent Variables in Socio-Economic Models, North-Holland. Giannone, D., L. Reichlin, and D. Small (2008): Nowcasting: The real-time informational content of macroeconomic data, Journal of Monetary Economics, 55, Lunsford, K. (2017): Lingering Residual Seasonality in GDP Growth, Federal Reserve Bank of Cleveland: Economic Commentary. Stock, J. H. and M. W. Watson (2002): Forecasting Using Principal Components From a Large Number of Predictors, Journal of the American Statistical Association, 97,

22 Disclaimer Source: This note is based partly on material which appeared in an article by Gavyn Davies published in the Financial Times on April 2nd. This material is for your information only and is not intended to be used by anyone other than you. It is directed at professional clients and eligible counterparties only and is not intended for retail clients. The information contained herein should not be regarded as an offer to sell or as a solicitation of an offer to buy any financial products, including an interest in a fund, or an official confirmation of any transaction. Any such offer or solicitation will be made to qualified investors only by means of an offering memorandum and related subscription agreement. The material is intended only to facilitate your discussions with Fulcrum Asset Management as to the opportunities available to our clients. The given material is subject to change and, although based upon information which we consider reliable, it is not guaranteed as to accuracy or completeness and it should not be relied upon as such. The material is not intended to be used as a general guide to investing, or as a source of any specific investment recommendations, and makes no implied or express recommendations concerning the manner in which any client s account should or would be handled, as appropriate investment strategies depend upon client s investment objectives. Funds managed by Fulcrum Asset Management LLP are in general managed using quantitative models though, where this is the case, Fulcrum Asset Management LLP can and do make discretionary decisions on a frequent basis and reserves the right to do so at any point. Past performance is not a guide to future performance. Future returns are not guaranteed and a loss of principal may occur. Fulcrum Asset Management LLP is authorised and regulated by the Financial Conduct Authority of the United Kingdom (No: ) and incorporated as a Limited Liability Partnership in England and Wales (No: OC306401) with its registered office at Marble Arch House, 66 Seymour Street, London, W1H 5BT. Fulcrum Asset Management LP is a wholly owned subsidiary of Fulcrum Asset Management LLP incorporated in the State of Delaware, operating from 350 Park Avenue, 13th Floor New York, NY c 2017 Fulcrum Asset Management LLP. All rights reserved. 22