Asymmetric Quantile Persistence and Predictability: the Case of US Inflation

This article investigates the evidence of time‐variation and asymmetry in the persistence of US inflation. We compare the out‐of‐sample performance of different forecasting models and find that quantile forecasts from an Auto‐Regressive (AR) model with level‐dependent volatility are at least as accurate as the forecasts of the Quantile Auto‐Regressive model, in particular for the core inflation measures. Our results indicate that the persistence of core inflation has been relatively constant and high, but it declined for the headline inflation measures. We also find that the asymmetric persistence of inflation shocks can be mostly attributed to the positive relation between inflation level and its volatility.     

Forecasting inflation in Latin America with core measures

We explore the ability of core inflation to predict headline CPI annual inflation for a sample of eight developing economies in Latin America over the period January 1995–May 2017. Our in-sample and out-of-sample results are roughly consistent in providing robust evidence of predictability in four of the countries in our sample. Mixed evidence is found for the other four countries. The bulk of the out-of-sample evidence of predictability concentrates on the short horizons of one and six months. In contrast, at the longest horizon of 24 months, we only find out-of-sample evidence of predictability for two countries: Chile and Colombia, with robust results only for the latter. This is both important and challenging, given that the monetary authorities in our sample of developing countries are currently implementing or are taking steps toward the future implementation of inflation targeting regimes, which are based heavily on long-run inflation forecasts.     

Forecasting GDP growth with NIPA aggregates: In search of core GDP

In addition to GDP, which is measured using expenditure data, the U.S. national income and product accounts (NIPAs) provide a variety of measures of economic activity, including gross domestic income and other aggregates that exclude one or more of the components that make up GDP. Similarly to the way in which economists have attempted to use core inflation—which excludes volatile energy and food prices—to predict headline inflation, the omission of GDP components may be useful in extracting a signal as to where GDP is going. We investigate the extent to which these NIPA aggregates constitute “core GDP.” In an out-of-sample forecasting exercise using a novel real-time dataset of NIPA aggregates, we find that consumption growth and the growth of GDP excluding inventories and trade have historically outperformed a canonical univariate benchmark for forecasting GDP growth, suggesting that these are promising measures of core GDP growth.     

UK INFLATION: DOWN BUT NOT OUT

The release of the December retail prices index showed a rise in both the headline and the underling rate of inflation. That was not altogether unexpected with higher fuel and tobacco duties feeding through from the Budget. But it provides a reminder that inflation can go up as well as down. Looking ahead, inflation can go up as well as down. Looking ahead, inflation will be subject to conflicting pressures over the remainder of this year. Worldwide, inflationary pressures are weak. But, on the domestic scene, a Mortgage rate cuts which have helped to bring down the headline rate of retail price inflation will be less helpful. Indirect tax increases will push up the RPI directly. And employees may look for some compensation for direct tax increases in the next pay round. This assessment looks at the likely impact of these forces on inflation and the future course of interest rates.     

Do global output gaps help forecast domestic inflation? Evidence from Phillips curves for Poland

This study confronts domestic and global views on inflation through the use of the Hybrid New Keynesian Phillips Curve (HNKPC) models estimated for headline and core inflation in Poland. We analyse the roles of the global vs. domestic output gaps in affecting price changes. We ensure that our conclusions are robust by taking into consideration various proxies for inflation expectations, imported inflation, the domestic output gap and the global output gap.Our results suggest that the global demand conditions are statistically insignificant in the majority of the estimated global versions of HNKPC, independently of the measure of them that is considered. In terms of empirical fit, and especially of the out-of-sample forecasting accuracy, the specifications of the Phillips curve with the domestic and global output gaps among the explanatory variables are not superior to traditional Phillips curves. Interestingly, the relative importance of the global output gap is much smaller in models that are estimated in terms of core inflation, excluding foodstuffs and energy, than in CPI inflation models. This suggests that global demand conditions affect the inflation in Poland indirectly, mainly through the prices of food and energy raw materials.The main conclusion from our study is that external factors that are already considered in the traditional hybrid versions of the new Keynesian Phillips curve are sufficient to account for global influences on prices in the domestic economy. The concept of the global output gap improves neither the explanatory nor the predictive power of HNKPC models.     

Input–Output Based Measures of Underlying Domestic Inflation: Empirical Evidence from Denmark 1903–2002

The paper presents input–output based time-series data for the underlying domestic inflation in Denmark 1903–2002 and analyses the inflationary development during the last century. More conceptual issues in relation to the interpretation and use of input–output based domestic inflation measures are also discussed. The purpose of such inflation measures is to track the development in the domestic market-determined inflation, which is closely related to the price of gross value added in the domestic market-based private business sector. A price index for value added often displays a different short-term development than the headline CPI. Such differences can be useful in an assessment of the current inflationary environment and in relation to an interpretation of the historical inflation development.     

Extracting Nonlinear Signals from Several Economic Indicators

We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully nonlinear multivariate specification (one‐step approach) with the ‘shortcut’ of using a linear factor model to obtain a coincident indicator, which is then used to compute the Markov switching probabilities (two‐step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, especially in the vicinity of turning points, although its gains diminish as the quality of the indicators increases. Additionally, we also obtain decreasing returns of adding more indicators with similar signal‐to‐noise ratios. Using the four constituent series of the Stock–Watson coincident index, we illustrate these results for US data. Copyright © 2014 John Wiley & Sons, Ltd.     

Forecasting economic time series using score-driven dynamic models with mixed-data sampling

We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of low-frequency time series variables through the use of timely information from high-frequency variables. We verify the in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation and GDP growth. In particular, we forecast monthly headline inflation using daily oil prices and quarterly GDP growth using a measure of financial risk. The forecasting results and other findings are promising. Our proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of both point and density forecasts.     

Prices and wages

The early part of 2014 has seen a further slowdown in inflation, with the CPI rate falling to 1.6% in March, the slowest pace since late‐2009. Sliding petrol and energy prices have played a significant role in the decline, as has the cooling in food price inflation. The impact of higher university fees has also eased relative to a year ago, further pulling down on the headline rate…     

Heterogeneous response of disaggregate inflation to monetary policy regime change: The role of price stickiness

This paper explores how a monetary regime change affects headline inflation via differential effects on various sectors in the economy. Using disaggregated CPI data for Canada, we find that the response to the adoption of inflation targeting (IT) was quite heterogeneous across sectors. While sticky-price sectors experienced a notable change in inflation dynamics following IT adoption, little structural change was observed in flexible price sectors. Our analysis based on a common factor model suggests that the structural changes in the sticky price sectors are driven by a decline in their responses to common aggregate shocks, including a monetary shock.     

The transmission of foreign shocks to South Eastern European economies: A Bayesian VAR approach

The paper analyses the macroeconomic effects of foreign shocks in three South-East European (SEE) economies: Croatia, Macedonia and Bulgaria. In this regard, we investigate the transmission of several eurozone shocks (output gap, money market rates and inflation) on various macroeconomic variables in the aforementioned countries (output, inflation, money market rates and budget deficits). We trace the effects of foreign shocks on the basis of impulse response functions obtained from the Bayesian Vector Auto Regressions (VARs) separately for each country. The main findings from our study are: first, economic expansion in the eurozone has strong output and inflation effects on SEE economies, implying some degree of synchronization of business cycles; second, eurozone inflation is instantly and to a great extent transmitted to domestic inflation, suggesting that inflation in the SEE economies is mostly driven by foreign inflation; third, domestic money market rates are not closely linked with eurozone money markets; fourth, monetary policy in the SEE countries does not seem to be responsive to eurozone inflation shocks; and fifth, the fiscal authorities attempt to offset the spillover effects from both economic expansion and monetary tightening in the eurozone.     

Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks

We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific price changes.     

Combination schemes for turning point predictions

We propose new forecast combination schemes for predicting turning points of business cycles. The proposed combination schemes are based on the forecasting performances of a given set of models with the aim to provide better turning point predictions. In particular, we consider predictions generated by autoregressive (AR) and Markov-switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach for both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and the Euro area business cycles.     

The global monetary boom - no cause for panic

• ▀ Global monetary growth has been its fastest for decades over recent months, but we continue to believe inflation risks are lower than many think. A modest inflation overshoot in the coming years is possible but would not be very damaging.
• ▀ While headline money growth figures still look strong, heavy precautionary borrowing by firms in March-April is already starting to unwind in the US and UK. About 80% of the rise in borrowing by large UK firms has been repaid.
• ▀ In addition, tightened lending standards at banks are likely to weigh on future corporate borrowing and money growth. A net 70% of US banks tightened corporate credit standards in the latest Fed survey. Rising loan defaults risk exacerbating this.
• ▀ Heavy government borrowing and accompanying central bank QE have been key drivers of monetary growth and are likely to remain so, notwithstanding a slowdown in the pace of central bank bond purchases. This is the main risk factor those who fear inflation cite.
• ▀ But if credit to the private sector starts to shrink, deficit financing of this sort may be essential to prevent long-term weakness in money, credit, and economic growth. Japan's experience in the 1990s and 2000s is relevant here.
• ▀ Inflation also has room to overshoot current targets, if necessary, given the substantial undershoots of the last decade. This consideration in part explains the recent shift in Federal Reserve thinking towards targeting an average inflation rate over time

     

Monitoring recessions: A Bayesian sequential quickest detection method

Monitoring business cycles faces two potentially conflicting objectives: accuracy and timeliness. To strike a balance between these dual objectives, we propose a Bayesian sequential quickest detection method to identify turning points in real time with a sequential stopping time as a solution. Using four monthly indexes of real economic activity in the United States, we evaluated the method’s real-time ability to date the past five recessions. The proposed method identified similar turning-point dates as the National Bureau of Economic Research (NBER), with no false alarms, but on average, it dated peaks four months faster and troughs 10 months faster relative to the NBER announcement. The timeliness of our method is also notable compared to the dynamic factor Markov-switching model: the average lead time was about five months when dating peaks and two months when dating troughs.     

中国实时金融状况指数的构建

金融状况指数(FCI)综合了多个金融变量的信息,能够比单个指标更全面、直观地反映金融市场的整体运行情况。基于动态因子模型直接利用混频数据测算我国的实时FCI,突破了传统方法要求数据频度一致的限制,从而大大增强了FCI的时效性,使FCI具备金融市场流动性指示器的功能。实证研究表明,改进后的FCI能够揭示近年来我国货币政策的松紧程度,其走势比CPI月同比领先7个月,对通胀的预测效果优于其构成变量。     

Predicting turning points in the housing market

We identify leading indicators of changes in the housing market and compare their performance in predicting turning points. Being able to predict turning points is of importance to the home building industry, homeowners, and makers of housing policy. Our leading indicators include the Wells Fargo/NAHB Housing Market Index, two of its forward looking components, and an index of consumer sentiment regarding purchasing a home. Our comparison tests include Granger causality and a Bayesian predictor of the probability of a turning point. We find that the measure of consumer sentiment performs relatively well compared to the HMI in predicting home permits, housing starts, and new home sales.     

THE ECONOMY AT A TURNING POINT

In this Forecast Release we assess the evidence from the economic indicators and the latest official statistics for the view that the economy is at a turning point. We conclude that, while last year's rapid decline in UK output is coming to an end, a broadly-based recovery of the UK economy is unlikely until the world economy starts to grow again. The business surveys suggest that the upturn in European economy is less well established than the UK recovery. It may also be delayed further by high interest rates. The world turning point is thus likely to be some months later than die UK turning point, despite the strong upswing in the US. This implies that the UK recovery could be somewhat hesitant in the coming months.     

What is the globalisation of inflation?

This paper studies the globalisation of CPI inflation by analysing core, energy and food components, testing for structural breaks in the relationships between domestic inflation and a corresponding country-specific foreign inflation series at the monthly frequency for OECD countries. The iterative methodology employed separates coefficient and variance breaks, while also taking account of outliers. We find that the overall pattern of globalisation in aggregate inflation is largely driven by convergence of the mean levels of the core component from the early 1990s, compatible with the introduction of inflation targeting in many countries of our sample. There is less evidence of increased synchronisation of shortrun movements in core than aggregate inflation, but an increased role for shortrun foreign energy inflation often contributes to the globalisation effect.     

Bagged neural networks for forecasting Polish (low) inflation

Accurate inflation forecasts lie at the heart of effective monetary policy. This paper utilizes a thick modelling approach in order to investigate the quality of the out-of-sample short-term headline inflation forecasts generated by a combination of bagged single hidden-layer feed-forward artificial neural networks. The model’s accuracy rises during the period of consistently falling and persistently low inflation in the emerging economy of Poland, and it statistically outperforms some of the popular benchmarks more frequently, especially at longer horizons. However, dispensing with data preprocessing and bootstrap aggregation compromises its forecasting ability severely. Combining linear and non-linear univariate and multivariate approaches with diverse underlying model assumptions delivers further gains in predictive accuracy and statistically outperforms a panel of benchmarks in a number of cases. While the vague interpretability of the model poses a considerable hurdle for policy makers, its inclusion in the forecasting toolbox should increase the accuracy of the ensemble of models, especially in periods of structural change.