Attitudinal and demographic determinants of diet quality and implications for policy targeting

Background:  Poor diet quality is a major public health concern that has prompted governments to introduce a range of measures to promote healthy eating. For these measures to be effective, they should target segments of the population with messages relevant to their needs, aspirations and circumstances. The present study investigates the extent to which attitudes and constraints influence healthy eating, as well as how these vary by demographic characteristics of the UK population. It further considers how such information may be used in segmented diet and health policy messages. Methods:  A survey of 250 UK adults elicited information on conformity to dietary guidelines, attitudes towards healthy eating, constraints to healthy eating and demographic characteristics. Ordered logit regressions were estimated to determine the importance of attitudes and constraints in determining how closely respondents follow healthy eating guidelines. Further regressions explored the demographic characteristics associated with the attitudinal and constraint variables. Results:  People who attach high importance to their own health and appearance eat more healthily than those who do not. Risk-averse people and those able to resist temptation also eat more healthily. Shortage of time is considered an important barrier to healthy eating, although the cost of a healthy diet is not. These variables are associated with a number of demographic characteristics of the population; for example, young adults are more motivated to eat healthily by concerns over their appearance than their health. Conclusions:  The approach employed in the present study could be used to inform future healthy eating campaigns. For example, messages to encourage the young to eat more healthily could focus on the impact of diets on their appearance rather than health.

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.

New direct payments scheme: targeting and redistribution in the future CAP

The Commission has proposed that a revised version of the present regime of direct payments should be rolled forward into the post-2013 CAP. There would be a limited redistribution of funds between Member States. Thirty per cent of the budget would be allocated to a new greening component, which would be problematic in the WTO. Non-active farmers would not qualify for aid; and payments would be capped. Special schemes would be introduced for small farmers, for young new entrants, and for disadvantaged regions.

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.