April 6, Table of contents. Global Inflation Outlook

Transcription

1 Global Inflation Outlook Global Inflation Outlook April 6, 2018 This document contains a selection of charts that are the output of Fulcrum s quantitative toolkit for monitoring global inflation trends. All of our forecasts and projections are the output of formal econometric models that have been developed in the academic literature, and therefore contain little input from direct judgement. All charts and models are up to date with available data up to April 6, a Table of contents Inflation 2 Consumer Prices Inflation Forecasts Producer and Import Prices Inflation Forecasts Stock and Watson Consumer Prices Trend Inflation United States: PCE Trend Inflation by Components United States: Wage Trend Inflation Market Measures of Inflation Expectations Global Oil Market 23 a More details of the specific models, and intra-month updates, are available upon request. April 6,

2 Inflation Consumer Prices Inflation Forecasts Fulcrum short-term inflation forecasting models The following charts present the equal weighted average of the inflation forecasts estimated using two models: a Bayesian Vector Autoregressive Model (see following paragraph) and an Unobserved Components model (see next section). For each country, the BVAR model includes: headline inflation, core inflation, the oil price, the trade weighted exchange rate, import prices inflation, producer prices inflation and non-energy commodity prices inflation. We use Minnesota priors, as in Doan, Litterman, and Sims (1983), to shrink the coefficients of the BVAR towards a more parsimonious specification. When available, the charts present the market forecast as implied by the inflation swaps. For the UK and the US, inflation swaps are linked respectively to RPI and CPI inflation. Therefore, we compute market implied forecasts for CPI and PCE inflation conditioning on the paths for RPI and CPI implied by the inflation swaps. How to read the graphs The following charts display the predictive distribution of inflation for the next two years in the form of fan charts. The solid red line represents the most recent median forecast of inflation and the dark and the light red areas around it represent respectively the 68% and the 90% confidence bands. The dashed black line shows the market forecast. The blue dots represent the forecasts published by the policy makers. Whenever these are expressed as annual average forecasts, the comparable annual numbers produced by our models are displayed as red dots. Notes and data definitions Headline inflation: USA: PCE deflator; Euro Area: HICP inflation; Canada: CPI; Japan: CPI excluding fresh food; Sweden: CPI; UK: CPI. Core inflation: USA: PCE excluding food and energy; Euro Area: HICP excluding Energy, Food, Alcohol and Tobacco; Canada: CPI excluding food and energy; Japan: CPI excluding food, beverages and energy; Sweden: CPI with fixed interest rates excluding energy; UK: CPI excluding energy, food, alcoholic beverages and tobacco. The USA model includes CPI and core CPI inflation as additional indicators. The UK model includes RPI inflation as an additional indicator. We exclude VAT increases by assuming full and immediate pass-through on the month of implementation. Sources: Fulcrum calculations based on Haver Analytics data. April 6,

3 Inflation Forecasts from Bayesian Model Averaging (% 12 month change) Note: Advanced economies is the PPP weighted average of US (PCE), Euro Area, UK, Japan, Canada, Sweden and Norway. World is the PPP weighted average of the former economies and China. April 6,

4 Inflation Forecasts from Bayesian Model Averaging (% 12 month change) Note: The blue dots represent the forecasts published by the policy makers. The dashed black line shows the market forecast. April 6,

5 Inflation Forecasts from Bayesian Model Averaging (% 12 month change) Note: The blue dots represent the forecasts published by the policy makers. The dashed black line shows the market forecast. April 6,

6 Inflation Forecasts from Bayesian Model Averaging (% 12 month change) Note: The blue dots represent the forecasts published by the policy makers. The dashed black line shows the market forecast. April 6,

7 Producer and Import Prices Inflation Forecastss Fulcrum short-term inflation forecasting models The following charts present the inflation forecasts estimated using the Bayesian Vector Autoregressive Model described in the previous section. How to read the graphs The following charts display the predictive distribution of inflation for the next two years in the form of fan charts. The solid red line represents the most recent median forecast of inflation and the dark and the light red areas around it represent respectively the 68% and the 90% confidence bands. Sources: Fulcrum calculations based on Haver Analytics data. April 6,

8 Inflation Forecasts from a Bayesian Vector Autoregressive Model (% 12 month change) Note: Advanced economies is the PPP weighted average of US, Euro Area, UK, Japan, Canada, Sweden and Norway. World is the PPP weighted average of the former economies and China. April 6,

9 Inflation Forecasts from a Bayesian Vector Autoregressive Model (% 12 month change) April 6,

10 Inflation Forecasts from a Bayesian Vector Autoregressive Model (% 12 month change) April 6,

11 Inflation Forecasts from a Bayesian Vector Autoregressive Model (% 12 month change) April 6,

12 Stock and Watson Consumer Prices Trend Inflation Fulcrum trend inflation models The following charts present our estimates of trend inflation. The model splits inflation into a persistent component ( trend ) and a purely transitory component ( noise ), which reverts within one month. Therefore, the trend captures long-term as well as cyclical influences and can be thought of as a measure of core inflation derived from the time series behaviour of inflation. The estimates are produced using a version of the Unobserved Components model with Stochastic Volatility and Outlier detection (UCSVO) introduced in Stock and Watson (2007, 2016). The main difference with Stock and Watson (2016) lies in the modelling of outliers. Rather than using discrete probabilities of outliers, we use a fat-tailed distribution for the variance of the noise component, in line with Jacquier et al. (2004). When possible, we use the multivariate version of the model (M-UCSVT), which models the main subcomponents of inflation independently and aggregates the trends using the official weights. Otherwise, we use the univariate model (UCSVT), which models directly the headline inflation series. How to read the graphs In the following graphs, the grey line represents the monthly inflation rate (% MoM Ann.). The solid red line represents the estimate of trend inflation. The red area represents the 68% confidence band around it. Data definition USA: PCE deflator; Euro Area: HICP inflation; China: CPI; Japan: CPI excluding fresh food; UK: CPI; Canada: CPI; Sweden: CPI; Norway: CPI; Switzerland: CPI. Sources: Fulcrum calculations based on Haver Analytics data. April 6,

13 Stock and Watson trend inflation April 6,

14 Stock and Watson trend inflation April 6,

15 United States: PCE Trend Inflation by Components Fulcrum trend inflation models The following charts present our estimates of trend inflation for the four main subcomponents of PCE inflation in the United States. The estimates are produced using the statistical model described in the previous section (UCSVT). How to read the graphs In the following graphs, the grey line represents the monthly inflation rate (% MoM Ann.). The solid red line represents the estimate of trend inflation. The red area represents the 68% confidence band around it. Sources: Fulcrum calculations based on Haver Analytics data. April 6,

16 US PCE trend inflation April 6,

17 United States: Wage Trend Inflation Fulcrum wage inflation model The following charts present our estimates of wage trend inflation in the United States. The estimates are produced using the statistical model described in the previous section (UCSVT). How to read the graphs In the following graphs, the grey line represents the monthly wage inflation rate (% MoM Ann.). The solid red line represents the estimate of wage trend inflation. The red area represents the 68% confidence band around it. Sources: Fulcrum calculations based on Haver Analytics data. April 6,

18 US Wage Trend Inflation April 6,

19 US Wage Trend Inflation April 6,

20 US Wage Trend Inflation April 6,

21 Market Measures of Inflation Expectations Definitions Five year inflation-linked swap rates The swap rates are linked to CPI inflation in the US, HICP inflation in the Euro Area and RPI inflation in the UK. The horizontal dashed line represents the policy maker s target. In the case of the US and the UK we have added 0.5% and 0.6% to reflect the average bias of US CPI and UK RPI over the inflation measures targeted by the Federal Reserve and the Bank of England (respectively PCE inflation and CPI inflation). Inflation protection The inflation protection is an annual inflation cap of 4% over 5 years. Deflation protection The deflation protection is an annual inflation floor of 0% over 5 years. Developments should be interpreted with caution due to limited market liquidity. Sources: Fulcrum calculations based on Bloomberg data. April 6,

22 Market Inflation Expectations (derived from swaps) April 6,

23 Global Oil Market The following charts present forecasts of the oil price and its drivers estimated using a modified version of the VAR model of Kilian and Murphy (2014). This VAR uses four variables: the real price of oil, global crude oil production, global crude oil inventories and the index of real economic activity developed in Kilian (2009). For our version, we use Minnesota priors as in Doan et al. (1983) to shrink the coefficients of the BVAR towards a more parsimonious specification. How to read the graphs: The first chart shows the predictive distribution of Brent crude oil in the form of a fan chart. The solid black line represents the actual and forecasted Brent crude oil price. The dark and the light grey areas around it represent respectively the 68% and the 90% confidence bands. We also show the evolution of WTI crude oil prices and the Brent-WTI spread. The dashed lines show the futures prices. The remaining charts show the evolution of the three additional variables included in the model. a Notes and data definitions: Crude oil prices: Nominal spot Brent crude oil prices. Prior to 1985 we use spot oil price for West Texas Intermediate (WTI). To deflate the nominal spot oil price, we use the U.S. consumer price index for all urban consumers. Whenever necessary, we use the forecast of the CPI from the previous section. Crude oil production: Monthly world oil production data measured in thousands of barrels of oil per day were obtained from the U.S. Energy Information Administration s (EIA) Monthly Energy Review. For the figure, we plot petroleum (i.e. crude oil and refined products) production from EIA, Energy Intelligence, and OPEC. Crude oil inventories: We obtain an estimate for global inventories as in Kilian and Murphy (2012) by multiplying the U.S. crude oil inventories by the ratio of OECD inventories of crude petroleum and petroleum products to U.S. inventories of petroleum and petroleum products. Index of real economic activity: Cost of international shipping deflated by the U.S. CPI and then reported in deviations from a linear trend, as in Kilian (2009). b Sources: Fulcrum calculations based on Haver Analytics and Bloomberg data. a We actually show various estimates of global petroleum production, as opposed to global crude oil production. b This is downloaded from Lutz Kilian s website: lkilian/paperlinks.html. To nowcast the most recent values, we deflate and de-trend the Baltic Dry Index. April 6,

24 Oil Market Note: The dashed lines in the first chart show the futures prices. April 6,

25 References Doan, T., R. B. Litterman, and C. A. Sims (1983): Forecasting and Conditional Projection Using Realistic Prior Distributions, NBER Working Papers 1202, National Bureau of Economic Research, Inc. Jacquier, E., N. G. Polson, and P. E. Rossi (2004): Bayesian analysis of stochastic volatility models with fat-tails and correlated errors, Journal of Econometrics, 122, Kilian, L. (2009): Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market, American Economic Review, 99, Kilian, L. and D. P. Murphy (2014): The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil, Journal of Applied Econometrics, 29, Stock, J. H. and M. W. Watson (2007): Why Has U.S. Inflation Become Harder to Forecast? Journal of Money, Credit and Banking, 39, (2016): Core Inflation and Trend Inflation, Review of Economics and Statistics, 98, April 6,

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