How do oil price forecast errors impact inflation forecast errors? An empirical analysis from US,French and UK inflation forecasts

This paper proposes an empirical investigation of the impact of oil price forecast errors on inflation forecast errors for three different sets of recent forecast data: the median of SPF inflation forecasts for the United States and the Central Bank inflation forecasts for France and the United Kingdom. Mainly two salient points emerge from our results. First, there is a significant contribution of oil price forecast errors to the explanation of inflation forecast errors, whatever the country or the period considered. Second, the pass-through of oil price forecast errors to inflation forecast errors is typically multiplied by around 2 when the oil price volatility is large.     

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.     

Rationality of inflation–output forecasts of MMS survey: international evidence

We analyse the individual rationality of inflation and output forecasts from Money Market Survey (MMS) for a group of G7 countries and EU under asymmetric univariate Linlin and Linex loss functions. We also test for joint rationality of inflation–output forecasts using the forecast rationality test under multivariate asymmetric loss functions proposed by Ulu (2013). Our results indicate that rationality is often rejected under symmetric loss, and results improve towards rationality when asymmetric loss functions are assumed. The assumption of multivariate asymmetric loss compared to univariate asymmetric loss provides further evidence towards rationality. We also analyse directional forecast accuracy of the inflation and output forecasts and find that the inflation–output forecasts of MMS are valuable when considered both jointly and separately.     

Measuring inflation expectations: a survey data approach

Measures of expected inflation from consumer surveys are derived using a modification of the Carlson-Parkin probability approach, which does not assume unbiasedness of expectations, makes use of survey data on expected future as well as perceived past price developments and allows for time varying response thresholds. We apply this method to the normal, central-t and noncentral-t distributions, thereby investigating the effects of nonnormal peakedness and asymmetry. We find that the effects on expected inflation of the former are small and of the latter are substantial, without increasing the accuracy of the expectations, however. Expected and actual inflation show substantial persistence, and, for most of our expectations measures, are cointegrated. Furthermore, the forecast error is stationary, implying weak-form rationality. The co-movement of currently observed expected inflation measures and the unobserved 12-months-ahead inflation rate is of interest for policy makers, for example in the direct inflation targeting strategy. Notwithstanding this, caution is warranted in using them as information variables because the inflation expected by consumers is no causal determinant of actual future inflation.     

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.     

A two factor model to combine US inflation forecasts

The combination of individual forecasts is often a useful tool to improve forecast accuracy. The most commonly used technique for forecast combination is the mean, and it has frequently proved hard to surpass. This study considers factor analysis to combine US inflation forecasts showing that just one factor is not enough to beat the mean and that the second one is necessary. The first factor is usually a weighted mean of the variables and it can be interpreted as a consensus forecast, while the second factor generally provides the differences among the variables and, since the observations are forecasts, it may be related with the dispersion in forecasting expectations and, in a sense, with its uncertainty. Within this approach, the study also revisits Friedman's hypothesis relating the level of inflation with expectations uncertainty at the beginning of the twenty-first century.     

Forecasting inflation through a bottom-up approach: How bottom is bottom?

The aim of this paper is to assess inflation forecast accuracy over the short-term horizon, using Consumer Price Index (CPI) disaggregated data, through a bottom-up approach. That is, aggregating forecasts is compared with aggregate forecasting. A new dimension to the question of to bottom-up or not is introduced by considering different levels of data disaggregation, namely a higher disaggregation level than the one considered up to now. This raises modelling issues that one has to cope with. In particular, it is suggested the use of a new strand of models, the Factor-Augmented SARIMA models. Considering as case-study the Portuguese one, we find an inverse relationship between the forecast horizon and the amount of information underlying the forecast, when minimizing the RMSFE.     

Cost-based Phillips Curve forecasts of inflation

It is a well-established idea that prices are a function of marginal cost, yet estimating a reliable measure of marginal cost is difficult to do. Stock and Watson (1999) use the Phillips Curve to forecast inflation for a variety of existing activity variables that researchers commonly use to proxy for marginal cost. This paper uses a similar type of approach to examine the performance of a new candidate for the activity variable, which is marginal cost measured following the theoretical methodology of Bils (1987), which we find to be simple yet powerful when implemented empirically. We then use the Phillips Curve to conduct pseudo out-of-sample inflation forecasts for the US using: output, unemployment, hours, the labor share, the capacity utilization rate, and the new measure of marginal cost. For almost all cases, forecast errors are lowest in the regressions with the new marginal cost variable, indicating that this new measure is an improvement over previous attempts to proxy for marginal cost.     

Comparative accuracy of forecasts of inflation: a Canadian study

An inaccurate forecast of inflation is costlier to economic agents when the inflation rate is high and volatile. In this situation, the use of more sophisticated and information-oriented forecasting models become economically efficient. We test this hypothesis by analysing the forecasting accuracy of vector auto-regressive (VAR), auto-regressive integrated moving average (ARIMA) and static expectation models. We use Canadian data and divide the post-sample forecasting period into four sub-periods, based on high/low and volatile/stable inflation. Prediction errors are compared for both short-term and long-term forecasts. Finally, the paper proposes a portfolio approach for obtaining a more accurate forecast of inflation.     

Does inflation uncertainty increase with the level of inflation?

This paper presents evidence that if agents forecast inflation rationally, using an estimate of the reduced form equation which generated the data, then the size of their forecast errors is positively correlated with the level of inflation. Forecast errors are measured first as the residuals from a full sample OLS regression, and secondly from one period ahead, outside sample, forecasts using a regression estimated from only data available at the time of the forecast. Thus, agents who form rational expectations about the variance, as well as the mean, of inflation should form conditional variances dependent on the level of inflation, at the date of the forecast.     

Multi-horizon inflation forecasts using disaggregated data

In this paper we use multi-horizon evaluation techniques to produce monthly inflation forecasts for up to twelve months ahead. The forecasts are based on individual seasonal time series models that consider both, deterministic and stochastic seasonality, and on disaggregated Consumer Price Index (CPI) data. After selecting the best forecasting model for each index, we compare the individual forecasts to forecasts produced using two methods that aggregate hierarchical time series, the bottom-up method and an optimal combination approach. Applying these techniques to 16 indices of the Mexican CPI, we find that the best forecasts for headline inflation are able to compete with those taken from surveys of experts.     

Evaluating FOMC forecast ranges: an interval data approach

The Federal Open Market Committee (FOMC) of the U.S. Federal Reserve publishes the range of members’ forecasts for key macroeconomic variables, but not the distribution of forecasts within this range. To evaluate these projections, previous papers compare the midpoint of the range with the realized outcome. This paper proposes an alternative approach to forecast evaluation that takes account of the interval nature of projections. It is shown that using the conventional Mincer–Zarnowitz approach to evaluate FOMC forecasts misses important information contained in the width of the forecast interval. This additional information plays a minor role at short forecast horizons but turns out to be of sometimes crucial importance for longer-horizon forecasts. For 18-month-ahead forecasts, the variation of members’ projections contains information that is more relevant for explaining future inflation than information embodied in the midpoint. Likewise, when longer-range forecasts for real GDP growth and the unemployment rate are considered, the width of the forecast interval comprises information over and above the one given by the midpoint alone.     

The empirical validity of the New Keynesian Phillips curve using survey forecasts of inflation

This paper investigates the performance of the New Keynesian Phillips curve when survey forecasts of inflation are used to proxy for inflation expectations. Previous authors such as Brissimis and Magginas (2008) have applied survey measures of inflation expectations to the NKPC, and have concluded that these estimates are superior to those estimated using actual data on future inflation. However this approach employs the use of the labor income share as the proxy for real marginal cost, something which is highly problematic once we consider the countercyclicality of this variable. This paper develops and tests a procyclical marginal cost variable alongside various survey measures of inflation forecasts in the NKPC, while recognizing the problem of weak instruments that occurs when estimating the model using conventional GMM. We find that the NKPC produces a counter-intuitive negative and significant coefficient on procyclical marginal cost when surveys of inflation forecasts are used, which casts serious doubt on the empirical viability of the NKPC model, even when estimated with survey inflation forecasts.     

Do disaggregated CPI data improve the accuracy of inflation forecasts?

In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcomponents of the CPI at the highest degree of disaggregation. The data set contains 54 macroeconomic series and 243 CPI subcomponents from 1992 to 2009 for Mexico. We find that the factor models that include disaggregated data outperform the benchmark autoregressive model and the factor models containing alternative groups of macroeconomic variables. We provide evidence that using disaggregated price data improves forecasting performance. The forecasts of the factor models that extract the information from the CPI disaggregated data are as accurate as the forecasts from the survey of experts.     

Evaluating the survey of professional forecasters probability distributions of expected inflation based on derived event probability forecasts

Techniques are proposed for evaluating forecast probabilities of events. The tools are especially useful when, as in the case of the Survey of Professional Forecasters (SPF) expected probability distributions of inflation, recourse cannot be made to the method of construction in the evaluation of the forecasts. The tests of efficiency and conditional efficiency are applied to the forecast probabilities of events of interest derived from the SPF distributions, and supplement a whole-density evaluation of the SPF distributions based on the probability integral transform approach.

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A common trends model of UK core inflation

In this paper the long-run trend in RPI inflation (core inflation) for the UK over the 1961–1997 period is estimated within the framework of a multivariate common trends model which extends the bivariate VAR approach of Quah and Vahey (1995). In this context core inflation is directly linked to money and wage growth and interpreted as the long-run forecast of inflation from a small-scale, cointegrated macroeconomic system. First version received: September 1999/Final version received: October 2001 RID="*" ID="*"  We thank two anonymous referees for many helpful comments and suggestions. Work on this paper was partially conducted when C. Morana was at Heriot-Watt University.     

Long memory,economic policy uncertainty and forecasting US inflation: a Bayesian VARFIMA approach

We compare inflation forecasts of a vector autoregressive fractionally integrated moving average (VARFIMA) model against standard forecasting models. U.S. inflation forecasts improve when controlling for persistence and economic policy uncertainty (EPU). Importantly, the VARFIMA model, comprising of inflation and EPU, outperforms commonly used inflation forecast models.     

Forecasting demand for a newly introduced product using reservation price data and Bayesian updating

Forecasting demand during the early stages of a product's life cycle is a difficult but essential task for the purposes of marketing and policymaking. This paper introduces a procedure to derive accurate forecasts for newly introduced products for which limited data are available. We begin with the assumption that the consumer reservation price is related to the timing with which the consumer adopts the product. The model is estimated using reservation price data derived through a consumer survey, and the forecast is updated with sales data as they become available using Bayes's rule. The proposed model's forecasting performance is compared with that of benchmark models (i.e., Bass model, logistic growth model, and a Bayesian model based on analogy) using 23 quarters' worth of data on South Korea's broadband Internet services market. The proposed model outperforms all benchmark models in both prelaunch and postlaunch forecasting tests, supporting the thesis that consumer reservation price can be used to forecast demand for a new product before or shortly after product launch.     

Forecasting inflation in China

This paper forecasts inflation in China over a 12-month horizon. The analysis runs 15 alternative models and finds that only those considering many predictors via a principal component display a better relative forecasting performance than the univariate benchmark.