Predicting Early Data Revisions to U.S. GDP and the Effects of Releases on Equity Markets

The effects of data uncertainty on real-time decision-making can be reduced by predicting data revisions to U.S. GDP growth. We show that survey forecasts efficiently predict the revision implicit in the second estimate of GDP growth, but that forecasting models incorporating monthly economic indicators and daily equity returns provide superior forecasts of the data revision implied by the release of the third estimate. We use forecasting models to measure the impact of surprises in GDP announcements on equity markets, and to analyze the effects of anticipated future revisions on announcement-day returns. We show that the publication of better than expected third-release GDP figures provides a boost to equity markets, and if future upward revisions are expected, the effects are enhanced during recessions.     

Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box–Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically estimated model of data revisions for U.S. output growth is used to investigate small-sample properties.     

Migration and students' performance: detecting geographical differences following a curves clustering approach

Students'' migration mobility is the new form of migration: students migrate to improve their skills and become more valued for the job market. The data regard the migration of Italian Bachelors who enrolled at Master Degree level, moving typically from poor to rich areas. This paper investigates the migration and other possible determinants on the Master Degree students'' performance. The Clustering of Effects approach for Quantile Regression Coefficients Modelling has been used to cluster the effects of some variables on the students'' performance for three Italian macro-areas. Results show evidence of similarity between Southern and Centre students, with respect to the Northern ones.     

Forecasting data revisions of GDP: a mixed frequency approach

Releases of GDP data undergo a series of revisions over time. These revisions have an impact on the results of macroeconometric models documented by the growing literature on real-time data applications. Revisions of U.S. GDP data can be explained and are partly predictable according to Faust et al. (J. Money Credit Bank. 37(3):403–419, 2005) or Fixler and Grimm (J. Product. Anal. 25:213–229, 2006). This analysis proposes the inclusion of mixed frequency data for forecasting GDP revisions. Thereby, the information set available around the first data vintage can be better exploited than the pure quarterly data. In-sample and out-of-sample results suggest that forecasts of GDP revisions can be improved by using mixed frequency data.     

INDEX TO VOLUME 5 (1987)

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. This article has online supplementary materials.     

Rational Economic Data Revisions

The efficient handling of provisional economic time series data is considered. Procedures are justified in terms of a testable hypothesis regarding the nature of data revisions. The hypothesis leads to explicit and practical variance expressions for measurement errors. Testing and estimation issues are dealt with. An efficient dual filter is developed for recursive signal estimation. Techniques are applied to the Canadian index of production data.     

A Bayesian semiparametric model for non negative semicontinuous data

When the target variable exhibits a semicontinuous behavior (a point mass in a single value and a continuous distribution elsewhere), parametric “two-part models” have been extensively used and investigated. The applications have mainly been related to non negative variables with a point mass in zero (zero-inflated data). In this article, a semiparametric Bayesian two-part model for dealing with such variables is proposed. The model allows a semiparametric expression for the two parts of the model by using Dirichlet processes. A motivating example, based on grape wine production in Tuscany (an Italian region), is used to show the capabilities of the model. Finally, two simulation experiments evaluate the model. Results show a satisfactory performance of the suggested approach for modeling and predicting semicontinuous data when parametric assumptions are not reasonable.     

Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty

A popular account for the demise of the U.K.’s monetary targeting regime in the 1980s blames the fluctuating predictive relationships between broad money and inflation and real output growth. Yet ex post policy analysis based on heavily revised data suggests no fluctuations in the predictive content of money. In this paper, we investigate the predictive relationships for inflation and output growth using both real-time and heavily revised data. We consider a large set of recursively estimated vector autoregressive (VAR) and vector error correction models (VECM). These models differ in terms of lag length and the number of cointegrating relationships. We use Bayesian model averaging (BMA) to demonstrate that real-time monetary policymakers faced considerable model uncertainty. The in-sample predictive content of money fluctuated during the 1980s as a result of data revisions in the presence of model uncertainty. This feature is only apparent with real-time data as heavily revised data obscure these fluctuations. Out-of-sample predictive evaluations rarely suggest that money matters for either inflation or real output. We conclude that both data revisions and model uncertainty contributed to the demise of the U.K.’s monetary targeting regime.     

Optimal cut-off for rare events and unbalanced misclassification costs

This paper develops a method for handling two-class classification problems with highly unbalanced class sizes and misclassification costs. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional classification methods tend to strongly favour the majority class, resulting in very low detection of the minority class. A method is proposed to determine the optimal cut-off for asymmetric misclassification costs and for unbalanced class sizes. Monte Carlo simulations show that this proposal performs better than the method based on the notion of classification accuracy. Finally, the proposed method is applied to empirical data on Italian small and medium enterprises to classify them into default and non-default groups.     

Place of living and health inequality: a study for elderly Italians

The aim of this study is to explore if the context matters in explaining socioeconomic inequality in the self-rated health of Italian elderly people. Our hypothesis is that health status perception is associated with existing huge imbalances among Italian areas. A multilevel approach is applied to account for the natural hierarchical structure, as individuals nested in geographical regions. Multilevel logistic regression models are performed including both individual and contextual variables, using data from 2005 Italian Health survey. We prove that individual factors (compositional effect), even representing the most important correlates of health, do not completely explain intra-regional heterogeneity, confirming the existence of an autonomous contextual effect. These territorial differences are present among both Regions and large areas, two geographical aggregations relevant in the domain of health. Moreover, for some Regions, the account for contextual factors explains variations in perceived health, leading to an overthrow of the initial situation: these Regions perform better than expected in the field of health. For other Regions, the contextual elements introduced do not catch the milieu heterogeneity. In this regard, we expect, and solicit, a major effort toward data availability, qualitatively and quantitatively, that might help in explaining residual territorial heterogeneity in health perception, a fundamental starting point for targeting specific policy interventions.     

Quis custiodet ipsos custodes? How to detect and correct teacher cheating in Italian student data

The increasing diffusion of standardized assessments of students’ competences has been accompanied by an increasing need to make reliable data available to all stakeholders of the educational system (policy makers, teachers, researchers, families and students). In this light, we propose a multistep approach to detect and correct teacher cheating, which decreases the quality of student data offered by the Italian Institute for the Educational Evaluation of Instruction and Training. Our method integrates the “mechanistic” logic of the fuzzy clustering technique with a statistical model-based approach, and it aims to improve the detection of cheating and to correct test scores at both the class and student level. The results show a normalization of the scores and a stronger correction on data for Southern regions, where the propensity to cheat appears to be highest.     

Real-Time Forecasts of the Real Price of Oil

We construct a monthly real-time dataset consisting of vintages for 1991.1–2010.12 that is suitable for generating forecasts of the real price of oil from a variety of models. We document that revisions of the data typically represent news, and we introduce backcasting and nowcasting techniques to fill gaps in the real-time data. We show that real-time forecasts of the real price of oil can be more accurate than the no-change forecast at horizons up to 1 year. In some cases, real-time mean squared prediction error (MSPE) reductions may be as high as 25% 1 month ahead and 24% 3 months ahead. This result is in striking contrast to related results in the literature for asset prices. In particular, recursive vector autoregressive (VAR) forecasts based on global oil market variables tend to have lower MSPE at short horizons than forecasts based on oil futures prices, forecasts based on autoregressive (AR) and autoregressive moving average (ARMA) models, and the no-change forecast. In addition, these VAR models have consistently higher directional accuracy.     

Bayesian hierarchical model for the prediction of football results

The problem of modelling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. We propose a Bayesian hierarchical model to fulfil both these aims and test its predictive strength based on data about the Italian Serie A 1991–1992 championship. To overcome the issue of overshrinkage produced by the Bayesian hierarchical model, we specify a more complex mixture model that results in a better fit to the observed data. We test its performance using an example of the Italian Serie A 2007–2008 championship.     

A State Space Approach to Extracting the Signal From Uncertain Data

Most macroeconomic data are uncertain—they are estimates rather than perfect measures of underlying economic variables. One symptom of that uncertainty is the propensity of statistical agencies to revise their estimates in the light of new information or methodological advances. This paper sets out an approach for extracting the signal from uncertain data. It describes a two-step estimation procedure in which the history of past revisions is first used to estimate the parameters of a measurement equation describing the official published estimates. These parameters are then imposed in a maximum likelihood estimation of a state space model for the macroeconomic variable.     

Preliminary Data Errors and Their Impact on the Forecast Error of Simultaneous-Equations Models

Econometric models, especially when designed for forecasting purposes, tend to use updated economic series, the last figures(s) of which embed first-published or preliminary data errors. This article identifies and assesses the contribution of errors in preliminary data to the forecast error and to the forecast error variance of linear dynamic simultaneous equation models. The effect of preliminary data errors is shown to be pervasive, although not necessarily weighty. The suggested decomposition of the forecast error is applied to a small macroeconometric model of the Italian economy.     

Smooth estimation of mean and dispersion function in extended generalized additive models with application to Italian induced abortion data

We analyse data on abortion rate (AR) in Italy with a particular focus on different behaviours in different regions in Italy. The aim is to try to reveal the relationship between the AR and several covariates that describe in some way the modernity of the region and the condition of the women there. The data are mostly underdispersed and the degree of underdispersion also varies with the covariates. To analyse these data, recent techniques for flexible modelling of a mean and dispersion function in a double exponential family framework are further developed now in a generalized additive model context for dealing with the multivariate set-up. The appealing unified framework and approach even allow to semi-parametric modelling of the covariates without any additional efforts. The methodology is illustrated on ozone-level data and leads to interesting findings in the Italian abortion data.     

面向信息使用者的经济统计数据修订

经济统计数据修订是官方统计工作的重要内容之一,因为相关的修订会影响到统计数据使用者的决策,所以科学规范的修订程序会提升官方统计的权威性和公信力。归纳了国际货币基金组织对统计数据修订的四种类型,从信息使用者角度阐述统计修订工作的时效性、准确性、一致性、可获得性与约束性内涵,结合西方发达国家的经济统计数据修订实践和历史经验,特别是美国国内生产总值的修订经验,为中国经济统计数据修订工作实现规范化提供借鉴。     

Heterogeneity,school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model

With the aim of assessing the extent of the differences in the context of Italian educational system, the paper applies multilevel modeling to a new administrative dataset, containing detailed information for more than 500,000 students at grade 6 in the year 2011/2012, provided by the Italian Institute for the Evaluation of Educational System. Data are grouped by classes, schools and geographical areas. Different models for each area are fitted, in order to properly address the heteroscedasticity of the phenomenon. The results show that it is possible to estimate statistically significant “school effects”, i.e., the positive/negative association of attending a specific school and the student’s test score, after a case-mix adjustment. Therefore, the paper’s most important message is that school effects are different in terms of magnitude and types in the three geographical macro areas (Northern, Central and Southern Italy) and are dependent on specific students’ and schools’ characteristics.     

A spatial analysis of health and pharmaceutical firm survival

The presence of knowledge spillovers and shared human capital is at the heart of the Marhall–Arrow–Romer externalities hypothesis. Most of the earlier empirical contributions on knowledge externalities; however, considered data aggregated at a regional level so that conclusions are based on the arbitrary definition of jurisdictional spatial units: this is the essence of the so-called modifiable areal unit problem. A second limitation of these studies is constituted by the fact that, somewhat surprisingly, while concentrating on the effects of agglomeration on firm creation and growth, the literature has, conversely, largely ignored its effects on firm survival. The present paper aims at contributing to the existing literature by answering to some of the open methodological questions reconciling the literature of Cox proportional hazards model with that on point pattern and thus capturing the true nature of spatial information. We also present some empirical results based on Italian firm demography data collected and managed by the Italian National Institute of Statistics (ISTAT).     

Markov random-field models for estimating local labour markets

Summary. This work is motivated by data on daily travel-to-work flows observed between pairs of elemental territorial units of an Italian region. The data were collected during the 1991 population census. The aim of the analysis is to partition the region into local labour markets. We present a new method for this which is inspired by the Bayesian texture segmentation approach. We introduce a novel Markov random-field model for the distribution of the variables that label the local labour markets for each territorial unit. Inference is performed by means of Markov chain Monte Carlo methods. The issue of model hyperparameter estimation is also addressed. We compare the results with those obtained by applying a classical method. The methodology can be applied with minor modifications to other data sets.