Forecasting annual inflation with seasonal monthly data: using levels versus logs of the underlying price index

This paper investigates whether using natural logarithms (logs) of price indices for forecasting inflation rates is preferable to employing the original series. Univariate forecasts for annual inflation rates for a number of European countries and the USA based on monthly seasonal consumer price indices are considered. Stochastic seasonality and deterministic seasonality models are used. In many cases, the forecasts based on the original variables result in substantially smaller root mean squared errors than models based on logs. In turn, if forecasts based on logs are superior, the gains are typically small. This outcome sheds doubt on the common practice in the academic literature to forecast inflation rates based on differences of logs.

Improving real-time estimates of output and inflation gaps with multiple-vintage models

Real-time estimates of output gaps and inflation gaps differ from the values that are obtained using data available long after the event. Part of the problem is that the data on which the real-time estimates are based is subsequently revised. We show that vector-autoregressive models of data vintages provide forecasts of post-revision values of future observations and of already-released observations capable of improving estimates of output and inflation gaps in real time. Our findings indicate that annual revisions to output and inflation data are in part predictable based on their past vintages.

Research practices that can prevent an inflation of false-positive rates

Recent studies have indicated that research practices in psychology may be susceptible to factors that increase false-positive rates, raising concerns about the possible prevalence of false-positive findings. The present article discusses several practices that may run counter to the inflation of false-positive rates. Taking these practices into account would lead to a more balanced view on the false-positive issue. Specifically, we argue that an inflation of false-positive rates would diminish, sometimes to a substantial degree, when researchers (a) have explicit a priori theoretical hypotheses, (b) include multiple replication studies in a single paper, and (c) collect additional data based on observed results. We report findings from simulation studies and statistical evidence that support these arguments. Being aware of these preventive factors allows researchers not to overestimate the pervasiveness of false-positives in psychology and to gauge the susceptibility of a paper to possible false-positives in practical and fair ways.

Evaluating the Bank of England density forecasts of inflation

We consider evaluating the UK Monetary Policy Committee's inflation density forecasts using probability integral transform goodness-of-fit tests. These tests evaluate the whole forecast density. We also consider whether the probabilities assigned to inflation being in certain ranges are well calibrated, where the ranges are chosen to be those of particular relevance to the MPC, given its remit of maintaining inflation rates in a band around per annum. Finally, we discuss the decision-based approach to forecast evaluation in relation to the MPC forecasts

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.

Forecasting with vector autoregressive models of data vintages: US output growth and inflation

Vintage-based vector autoregressive models of a single macroeconomic variable are shown to be a useful vehicle for obtaining forecasts of different maturities of future and past observations, including estimates of post-revision values. The forecasting performance of models which include information on annual revisions is superior to that of models which only include the first two data releases. However, the empirical results indicate that a model which reflects the seasonal nature of data releases more closely does not offer much improvement over an unrestricted vintage-based model which includes three rounds of annual revisions.

Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions

We examine how the accuracy of real-time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest-available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple-vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.

Probability distributions or point predictions? Survey forecasts of US output growth and inflation

We consider whether survey respondents’ probability distributions, reported as histograms, provide reliable and coherent point predictions, when viewed through the lens of a Bayesian learning model. We argue that a role remains for eliciting directly-reported point predictions in surveys of professional forecasters.

US inflation expectations and heterogeneous loss functions, 1968-2010

Recent literature has suggested that macroeconomic forecasters may have asymmetric loss functions, and that there may be heterogeneity across forecasters in the degree to which they weigh under- and over-predictions. Using an individual-level analysis that exploits the Survey of Professional Forecasters respondents’ histogram forecasts, we find little evidence of asymmetric loss for the inflation forecasters

Type-1 error inflation in the traditional by-participant analysis to metamemory accuracy: a generalized mixed-effects model perspective

In order to examine metacognitive accuracy (i.e., the relationship between metacognitive judgment and memory performance), researchers often rely on by-participant analysis, where metacognitive accuracy (e.g., resolution, as measured by the gamma coefficient or signal detection measures) is computed for each participant and the computed values are entered into group-level statistical tests such as the t-test. In the current work, we argue that the by-participant analysis, regardless of the accuracy measurements used, would produce a substantial inflation of Type-1 error rates, when a random item effect is present. A mixed-effects model is proposed as a way to effectively address the issue, and our simulation studies examining Type-1 error rates indeed showed superior performance of mixed-effects model analysis as compared to the conventional by-participant analysis. We also present real data applications to illustrate further strengths of mixed-effects model analysis. Our findings imply that caution is needed when using the by-participant analysis, and recommend the mixed-effects model analysis.

Forecast uncertainty—ex Ante and ex Post: U.S. inflation and output growth

Survey respondents who make point predictions and histogram forecasts of macro-variables reveal both how uncertain they believe the future to be, ex ante, as well as their ex post performance. Macroeconomic forecasters tend to be overconfident at horizons of a year or more, but overestimate (i.e., are underconfident regarding) the uncertainty surrounding their predictions at short horizons. Ex ante uncertainty remains at a high level compared to the ex post measure as the forecast horizon shortens. There is little evidence of a link between individuals’ ex post forecast accuracy and their ex ante subjective assessments.

Meta-analysis to integrate effect sizes within a paper: Possible misuse and Type-1 error inflation

In recent years an increasing number of papers have employed meta-analysis to integrate effect sizes of researchers’ own series of studies within a single paper (“internal meta-analysis”). Although this approach has the obvious advantage of obtaining narrower confidence intervals, we show that it could inadvertently inflate false-positive rates if researchers are motivated to use internal meta-analysis in order to obtain a significant overall effect. Specifically, if one decides whether to stop or continue a further replication experiment depending on the significance of the results in an internal meta-analysis, false-positive rates would increase beyond the nominal level. We conducted a set of Monte-Carlo simulations to demonstrate our argument, and provided a literature review to gauge awareness and prevalence of this issue. Furthermore, we made several recommendations when using internal meta-analysis to make a judgment on statistical significance.

The impact of the elderly on inflation rates in developed countries

What explains the cross-national variation in inflation rates in developed countries? Previous literature has emphasised the role of ideas and institutions, and to a lesser extent interest groups, while leaving the role of electoral politics comparatively unexplored. This paper seeks to redress this neglect by focusing on one case where electoral politics matters for inflation: the share of the population above 65 years old in a country. I argue that countries with a larger share of elderly have lower inflation because older people are both more inflation averse and politically powerful, forcing governments to pursue lower inflation. I test my argument in three steps. First, logistic regression analysis of survey data confirms older people are more inflation averse. Second, panel data regression analysis of party manifesto data reveals that European countries with more old people have more economically orthodox political parties. Third, time series cross-section regression analyses demonstrate that the share of the elderly is negatively correlated with inflation in both a sample of 21 advanced OECD economies and a larger sample of 175 countries. Ageing may therefore push governments to adopt a low inflation regime.

CMB and LSS constraints on a single-field model of inflation

A new inflationary scenario whose exponential potential V (Phi) has a quadratic dependence on the field Phi in addition to the standard linear term is confronted with the five-year observations of the Wilkinson-Microwave Anisotropy Probe and the Sloan Digital Sky Survey data. The number of e-folds (N), the ratio of tensor-to-scalar perturbations (r), the spectral scalar index of the primordial power spectrum (n(s)) and its running (dn(s)/d ln k) depend on the dimensionless parameter a multiplying the quadratic term in the potential. In the limit a. 0 all the results of the exponential potential are fully recovered. For values of alpha not equal 0, we find that the model predictions are in good agreement with the current observations of the Cosmic Microwave Background (CMB) anisotropies and Large-Scale Structure (LSS) in the Universe. Copyright (C) EPLA, 2008.