Was the recent downturn in US real GDP predictable?

This article uses a small set of variables – real GDP, the inflation rate and the short-term interest rate – and a rich set of models – atheoretical (time series) and theoretical (structural), linear and nonlinear, as well as classical and Bayesian models – to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance of the models to the benchmark random-walk model by root mean-square errors, the two structural (theoretical) models, especially the nonlinear model, perform well on average across all forecast horizons in our ex post, out-of-sample forecasts, although at specific forecast horizons certain nonlinear atheoretical models perform the best. The nonlinear theoretical model also dominates in our ex ante, out-of-sample forecast of the Great Recession, suggesting that developing forward-looking, microfounded, nonlinear, dynamic stochastic general equilibrium models of the economy may prove crucial in forecasting turning points.     

Short-term inflation forecasting models for Turkey and a forecast combination analysis

In this paper, we produce short term forecasts for the inflation in Turkey, using a large number of econometric models. In particular, we employ univariate models, decomposition based approaches (both in frequency and time domain), a Phillips curve motivated time varying parameter model, a suite of VAR and Bayesian VAR models and dynamic factor models. Our findings suggest that the models which incorporate more economic information outperform the benchmark random walk, and the relative performance of forecasts are on average 30% better for the first two quarters ahead. We further combine our forecasts by means of several weighting schemes. Results reveal that, the forecast combination leads to a reduction in forecast error compared to most of the models, although some of the individual models perform alike in certain horizons.     

Does a factor Phillips curve help? An evaluation of the predictive power for U.S. inflation

This article evaluates various models’ predictive power for U.S. inflation rate using a simulated out-of-sample forecasting framework. The starting point is the traditional unemployment Phillips curve. We show that a factor Phillips curve model is superior to the traditional Phillips curve, and its performance is comparable to other factor models. We find that a factor AR model is superior to the factor Phillips curve model, and is the best bivariate or factor model at longer horizons. Finally, we investigate a New Keynesian Phillips curve model, and find that its forecasting performance dominates all other models at the longer horizons.     

Globalization and inflation: A threshold investigation

We use a threshold methodology to investigate the importance of non-linear effects in the analysis of the inflation globalization hypothesis. Accounting for potential non-linearities in the Phillips Curve, we show that trade openness is not rejected as a threshold variable for the effects of domestic and foreign slack on inflation in many advanced economies, and we find a switch of the output gap slopes from one regime to the other that is consistent with the key predictions of the inflation globalization hypothesis. For some countries the threshold Phillips Curve model also leads to improvements in out-of-sample forecast over the linear Phillips models, especially at longer horizons. Contrary to most of the previous literature which ignores such non-linearities, our new approach provides some interesting empirical evidence supportive of the effect globalization has on a country’s inflation dynamics.     

Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation

The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African inflation by means of non-linear models and using a long historical dataset of seasonally adjusted monthly inflation rates spanning from 1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can be a salient feature of such long data, hence the relevance of evaluating non-linear models’ forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a protracted rising trend in South African inflation data, we estimate the models for an in-sample period of 1921:02–1966:09 and evaluate 1, 4, 12, and 24 step-ahead forecasts over an out-of-sample period of 1966:10–2013:01. In addition, using a weighted loss function specification, we evaluate the forecast performance of different non-linear models across various extreme economic environments and forecast horizons. In general, we find that no competing model consistently and significantly beats the LoLiMoT’s performance in forecasting South African inflation.     

Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging

Motivated by economic-theory concepts – the Fisher hypothesis and the theory of the term structure – we consider a small set of simple bivariate closed-loop time-series models for the prediction of price inflation and of long- and short-term interest rates. The set includes vector autoregressions (VAR) in levels and in differences, a cointegrated VAR and a non-linear VAR with threshold cointegration based on data from Germany, Japan, UK and the US. Following a traditional comparative evaluation of predictive accuracy, we subject all structures to a mutual validation using parametric bootstrapping. Ultimately, we utilize the recently developed technique of Mallows model averaging to explore the potential of improving upon the predictions through combinations. While the simulations confirm the traded wisdom that VARs in differences optimize one-step prediction and that error correction helps at larger horizons, the model-averaging experiments point at problems in allotting an adequate penalty for the complexity of candidate models.     

Forecasting inflation with an uncertain output gap

The output gap is a crucial concept in the monetary policy framework, indicating demand pressure that generates inflation. However, its definition and estimation raise a number of theoretical and empirical questions. This paper evaluates a series of univariate and multivariate methods for extracting the output gap in Norway, and compares their value added in predicting inflation. We find that models including the output gap have better predictive power than models based on alternative indicators, and they forecast significantly better than simple benchmark models. Furthermore multivariate measures of the output gap perform better than the univariate gaps. Comments from two anonymous referees, Q. Farooq Akram, Tommy Sveen, Ken West, Fredrik Wulfsberg and seminar participants in Norges Bank are gratefully acknowledged. All mistakes remain our own. The views expressed are those of the authors and do not necessarily represent those of Norges Bank.     

Globalization, the output-inflation tradeoff and inflation

This paper provides comprehensive evidence on the relation between inflation and globalization, defined here as trade and financial openness, using a large cross-section of 91 countries over the period 1985-2004. We establish two main empirical regularities: both higher trade and financial openness (i) reduce central banks’ inflation bias, yielding lower average inflation and (ii) are associated with a larger output-inflation tradeoff. This evidence is at odds with the standard Barro-Gordon framework, which would require globalization to have a negative effect on the output-inflation tradeoff to yield lower equilibrium inflation, but it is consistent with a recent strand of new Keynesian models emphasizing the role of imperfect competition and nominal rigidities. Our findings also support the relevance of the time-inconsistency hypothesis, which underlies the theoretical models predicting a relation between globalization and inflation. For the OECD subsample, however, we do not find an effect of openness on inflation (the output-inflation tradeoff), suggesting that these countries have created an institutional framework for central banks that eliminates distortions due to the time-inconsistency problem.     

Forecasting China's inflation in a data-rich environment

Inflation is one of the most recent critical issues facing China. To improve inflation forecasts within China, this study investigates the predictive ability of three dimension reduction techniques used in a data-rich environment: Principal Components Analysis (PCA), Sliced Inverse Regression (SIR), and Partial Least Squares (PLS) applied in the Factor-Augmented Autoregression (FAAR) model proposed by Stock and Watson (2005). Varied macroeconomic data from China between January 1998 and December 2009 are obtained to construct factors for use by three different techniques. The performance of different dimension reduction methods depends on forecasting horizons, the number of factors chosen, and the number of slices for SIR. The empirical study finds that the FAAR model with an optimal number of PCA factors outperforms the other model in out-of-sample inflation forecasting in China.     

Relative price and inflation variability in a simple consumer search model

There is a large empirical literature on the effect of aggregate inflation on both price-level dispersion (relative price variability, RPV) and inflation rate dispersion (relative inflation variability, RIV) across goods or locations. Early empirical work of RIV has an explicit theoretical foundation in signal-extraction models. However, recent empirical work on RPV has produced results inconsistent with signal-extraction models. In particular, while RIV is increasing in the absolute value of inflation shocks, RPV is a negative monotonic function of inflation shocks. We show that consumer search theory offers a potential explanation for these apparently contradictory observations.     

Interpreting euro area inflation at high and low frequencies

Several authors have recently interpreted the European Central Bank's (ECB's) two-pillar framework as separate approaches to forecast and analyse inflation at different time horizons or frequency bands. The ECB has publicly supported this understanding of the framework. This paper presents further evidence on the behaviour of euro area inflation using band spectrum regressions, which allow for a natural definition of the short and long run in terms of specific frequency bands, and causality tests in the frequency domain. The main finding is that variations in inflation are well explained by low-frequency movements of money and real output growth and high-frequency fluctuations of the output gap.     

Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach*

ABSTRACT

In this article, the multihorizon predictive power of the Hybrid New Keynesian Phillips Curve (HNKPC) is analysed by making use of several close- and open-economy specifications for the headline inflation of six developed countries. The key element is the use of direct measures of inflation expectations – Consensus Forecast – embedded in a compact-scale Global VAR (GVAR) environment, becoming the baseline open-economy HNKPC (OE-HNKPC) specification. These OE-HNKPC point forecasts are evaluated using the Root Mean Squared Forecast Error (RMSFE) statistic and statistically compared with several benchmarks, including traditional atheoretical models. Several OE-HNKPC as well as a closed-economy HNKPC (CE-HNKPC) specifications are also analysed. The results indicate that in four out of six countries, the CE-HNKPC is the best forecasting model, whereas for the same countries, a parsimonious OE-HNKPC is the second-best alternative, and in most cases, outperforming traditional statistical benchmarks. The RMSFE is obviously affected by the unanticipated effects of the Great Financial Crisis (GFC), spoiling out the performance of a number of competing forecasts. However, when considering an evaluation sample just before the crisis, both the CE-HNKPC and the parsimonious OE-HNKPC still come out as the best forecasting models. Furthermore, these preferred models also do an excellent job tracking inflation better than the best atheoretical models during the GFC.     

The Yugoslav inflation: Some competing hypotheses

Various hypotheses about wage and price inflation in Yugoslavia are presented and tested empirically with quarterly data from the 1962–1972 period. Both theoretical literature and empirical evidence on the behavior of the self-managed firm are used to derive different models of wage determination. The wage-equation results indicate that labor-market conditions, inflationary expectations; and labor-productivity variables are significant determinants of the rate of growth of wages. The price equations, based on a modified cost-markup model consistent with the practices of Yugoslav firms, identify labor costs and aggregate demand as significant determinants of the rate of growth of prices. University of California, Berkeley.     

Forecasting UK consumer price inflation using inflation forecasts

The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modeling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modeling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.     

The enlargement of the euro area: differences in relative inflation

This paper investigates the structural determinants of relative inflation (i.e. the inflation of non‐tradables vs tradables) in the context of overall inflation differentials in the EU. The analysis is based on the Bergstrand theoretical model. This framework incorporates three alternative hypotheses of relative inflation (Harrod–Balassa–Samuelson, relative factors endowment, and demand effects). Due to the lack of reliable data on capital stocks only a curtailed version of the model is tested here empirically. The various specifications of the model are estimated for the majority of EU countries, using the Pedroni panel group mean FMOLS estimator. In general, relative labour productivity and demand factors turn out to be significant and correctly signed, though evidence in favour of the latter effect seems to be less robust. In addition, differences in the determination of relative prices between the new and old EU Member States are found. They seem to be consistent with theoretical considerations and the transition phenomenon. The estimation results are very sensitive to the definition of non‐tradables. The paper also discusses policy implications for overall inflation, stemming from relative price models. It questions the usefulness of relative inflation models for the analysis of overall inflation differentials and practical policy decisions.     

Inflation and inflation volatility in Thailand

Quarterly data for Thailand are used in this article for the period 1965q3–2013q4 to investigate both the relationship between inflation and inflation volatility, and the impact of inflation volatility on economic growth. Inflation volatility is estimated by deploying the generalized autoregressive conditional heteroscedastic (GARCH) technique. A Granger causality test is then conducted to examine the causality between inflation and inflation volatility. The empirical results obtained are consistent with a number of theoretical propositions. First, the results are consistent with the Friedman–Ball proposition, which states that a rise in inflation raises inflation volatility. Second, there is evidence supporting the Holland proposition that inflation volatility lowers the rate of inflation. This is consistent with the view that central banks attempt to stabilize inflation with the rise in inflation volatility. Third, empirical results obtained by asymmetric GARCH models suggest that inflation shocks have an asymmetric impact on inflation volatility (i.e. a positive inflation shock has a larger impact on inflation volatility – as measured by the logarithm of the conditional variance of inflation – than a negative inflation shock). Fourth, inflation volatility has an adverse impact on economic growth. Finally, given the fixed/pegged or managed float exchange rate system, US inflation has been found to have a positive impact on inflation and its volatility in Thailand. This article discusses the implications of empirical findings on the design and enactment of monetary policy for price stability in Thailand.     

The inflation and inflation uncertainty relationship for Turkey: a dynamic framework

This article assesses the interaction between inflation and inflation uncertainty in a dynamic framework for Turkey by using monthly data for the time period 1984–2009. The bulk of previous studies investigating the link between inflation and inflation uncertainty employ Autoregressive Conditional Heteroskedasticity (ARCH)-type models, which consider inflation uncertainty as a predetermined function of innovations to inflation specification. The stochastic volatility in mean (SVM) models that we use allow for gathering innovations to inflation uncertainty and assess the effect of inflation volatility shocks on inflation over time. When we assess the interaction between inflation and its volatility, the empirical findings indicate that response of inflation to inflation volatility is positive and statistically significant. However, the response of inflation volatility to inflation is negative but not statistically significant.     

The convergence of inflation rates in the EU-12 area: A distribution dynamics approach

This paper provides an assessment of Euro area inflation dynamics based on the distribution dynamics approach. It is found that raw series and trends have converged, although the convergence process has not been constant over time. Inflation cycles still lack of synchronization over short time horizons. In search for an economic explanation for cyclical inflation dynamics, the paper suggests that country-specific labor market institutions are likely to affect inflation outcomes above all in high inflation countries. Moreover, the cyclical inflation divergence arises from output fluctuations.     

Robust estimation and inflation forecasting

This paper considers various models emerging from the Fisher effect and/or the term structure of interest rates for inflation forecasting. This paper, it is believed, makes a contribution to the literature on estimation of the models by using a procedure that is robust for non-normal errors, improving the efficiency of the estimates considerably. The Consumer Price Index series, 90 days and 180 days Australian bank-accepted bill rates, covering the sample period 1968Q1 to 1998Q4 were used in this study. Contrary to earlier findings, strong evidence was documented supporting the Fisher effect in the presence of a structural break with the break-point being at 1980Q1. The overall results suggest that the error correction model of the Fisher effect, the term structure of interest rates and short-run dynamics produce superior forecasts, in particular when the models were estimated using the robust method. These findings have important implications for economic policy analysis.     

Rational expectations and fixed-event forecasts: An application to UK inflation

This paper tests a version of the rational expectations hypothesis using ‘fixed-event’ inflation forecasts for the UK. Fixed-event forecasts consist of a panel of forecasts for a set of outturns of a series at varying horizons prior to each outturn. The forecasts are the prediction of fund managers surveyed by Merrill Lynch. Fixed-event forecasts allow tests for whether expectations are unbiased in a similar fashion to the rest of the literature. But they also permit the conduct of particular tests of forecast efficiency - whether the forecasts make best use of available information - that are not possible with rolling-event data. We find evidence of a positive bias in inflation expectations. Evidence for inefficiency is much less clear cut.First version received: June 2002/ Final version received: November 2003We would like two anonymous referees and an editor for comments that have significantly improved the paper. The views expressed in this paper are those of the authors and do not necessarily reflect those of the Bank of England.