Forecasting with preliminary data: a comparison of two methods

This study examines two alternate methods, a vector autoregression error correction model and a state space model, to forecast revised United States trade balance figures. Both these methods incorporate preliminary and revised trade data. The results obtained from these methods were compared to the benchmark forecasts generated by revised-data-only models. This Study finds that the state space model performs worse than the benchmark. The vector autoregression model performs better than the benchmark only in the one-step forecast. These results indicate that incorporating preliminary data may not be useful in forecasting the revised data.     

Optimal monetary policy revisited: does considering US real-time data change things?

The primary goal of this article is to investigate whether properly modelling real-time data and optimal real-time decision-making of a monetary planner provides new insights into monetary policy behaviour and outcomes. This article extends a variant of the asymmetric preference model suggested by Ruge-Murcia to investigate the use of real-time data available to policymakers when making their decisions and revised data which more accurately measure economic performance, but is only available much later. In our extended model, the central banker targets a weighted average of revised and real-time inflation together with a weighted average of revised and real-time output. Moreover, we allow for an asymmetric central bank response to real-time data depending on whether the unemployment rate is high or low. Our model identifies several new potential sources of inflation bias due to data revisions. Our empirical results suggest that the Federal Reserve Bank focuses on targeting revised inflation during low unemployment periods, but it weighs heavily real-time inflation during high unemployment periods. The inflation bias due to data revisions is comparable in magnitude to the bias from asymmetric central banker preferences with the bias being somewhat larger during high unemployment.     

The business cycle and the equity risk premium in real time

Building on the stochastic discount factor model, we estimated a multivariate exponential GARCH-in-mean model to analyze the link between the business cycle and the equity risk premium in the United States. In order to measure the business cycle, we used revised and real-time monthly data on industrial production for the period from 1965 to 2008. The main result of our empirical analysis is that estimates of the equity risk premium based on real-time macroeconomic data may significantly differ from estimates of the equity risk premium based on revised macroeconomic data.     

MOSES: Model for studying the economy of Sweden

MOSES is an aggregate econometric model for Sweden, estimated on quarterly data, and intended for policy simulations and short-term forecasting. After a presentation of qualitative model properties, the econometric methodology is summarized. The model properties, within sample simulations, and forecast evaluations are presented. We also address methodology and practical issues relating to building and maintaining a macro model of this type. The detailed econometric equations are reported in Appendix A.“I think it should be generally agreed that a model that does not generate many properties of actual data cannot be claimed to have any ‘policy implications’…”Clive.W. J. Granger (1992, p. 4).     

DATA REVISIONS AND FORECASTING ACCURACY: AN ECONOMETRIC ANALYSIS BASED ON PRELIMINARY AND REVISED NATIONAL ACCOUNTING ESTIMATES*

This paper entails an investigation of the effects of data revisions on forecasting accuracy, through use of preliminary and revised national accounting data compiled by the United Nations. A small model was estimated for each of fourteen countries and ex post“forecasts” generated for each country and each year of the period 1957–1964, using first preliminary and then revised data. A prior analysis of the data revisions indicated a strong and widespread tendency for the preliminary estimates to understate both levels and year-to-year changes. This is consistent with the findings of other studies. Two sets of forecasts obtained from the reduced form of the model were considered in relation to “actual” levels and changes, obtained from the revised data, and also in relation to each other. A strong downward bias was observed in the forecasts of levels based on preliminary data, and a weaker one in the forecasts of changes. The forecast discrepancies for different variables were found to be significantly correlated. The results suggest that a tendency toward understatement in preliminary data may account in part for the general tendency toward understatement in forecasts noted in other studies.     

通货膨胀实时预测及菲利普斯曲线的适用性

本文从实时分析的视角,基于多种退势方法的产出缺口最终估计、准最终估计和实时估计序列,分别构建了四类预测模型对我国通货膨胀率进行预测,分析了产出缺口修正效应和滞后阶数变化效应对通胀预测的影响,并进一步考察了产出缺口在通胀预测中的作用及菲利普斯曲线在通胀预测中的适用性。研究结论表明,通胀率的实时预测效果要明显比基于最终数据的差,其中滞后阶数变化效应对实时预测精度的影响大于产出缺口修正效应;尤为重要的是,尽管在最终数据的预测分析中,产出缺口的引入能够提高通胀率的预测精度,但是在实时预测中,产出缺口没有提供有价值的信息,因此"产出—通胀"型菲利普斯曲线在我国通胀实时预测中并不适用。     

Real-Time Fiscal Forecasting Using Mixed-Frequency Data*

The sovereign debt crisis has increased the importance of monitoring budgetary execution. We employ real-time data using a mixed data sampling (MiDaS) methodology to demonstrate how budgetary slippages can be detected early on. We show that in spite of using real-time data, the year-end forecast errors diminish significantly when incorporating intra-annual information. Our results show the benefits of forecasting aggregates via subcomponents, in this case total government revenue and expenditure. Our methodology could significantly improve fiscal surveillance and could therefore be an important part of the European Commission's model toolkit.     

The reliability of real-time estimates of the euro area output gap

This paper provides evidence on the reliability of euro area real-time output gap estimates. A genuine real-time data set for the euro area is used, including vintages of several sets of euro area output gap estimates available from 1999 to 2010. It turns out that real-time estimates of the output gap tend to be characterised by a high degree of uncertainty, much higher than that resulting from model and estimation uncertainty only. In particular, the evidence indicates that both the magnitude and the sign of the real-time estimates of the euro area output gap are very uncertain. The uncertainty is mostly due to parameter instability and model uncertainty, while data revisions seem to play a minor role. Some euro area real-time measures, based on multivariate components models and capacity utilisation, are relatively less uncertain, but do not appear to be fully reliable along some dimensions. To benchmark our results, we repeat the analysis for the US over the same sample. It turns out that US real-time estimates tend to be revised to a lesser extent than euro area estimates. However, euro area real-time output gap estimates tend to display a higher correlation with the final estimates and the sign of the level of US real-time estimates tends to be revised more often compared to the corresponding euro area estimates. In addition, the data revision component of the revision error is larger for US estimates than for the euro area. Overall, the unreliability in real-time of the US output gap measures detected in earlier studies is confirmed in the more recent period.     

The Implications for Econometric Modelling of Forecast Failure

To reconcile forecast failure with building congruent empirical models, we analyze the sources of mis-prediction. This reveals that ex ante forecast failure is purely a function of forecast-period events, not determinable from in-sample information. The primary causes are unmodelled shifts in deterministic factors, rather than model mis-specification, collinearity, or a lack of parsimony. We examine the effects of deterministic breaks on equilibrium-correction mechanisms, and consider the role of causal variables. Throughout, Monte Carlo simulation and empirical models illustrate the analysis, and support a progressive research strategy based on learning from past failures.     

‘Ex-ante’ Taylor rules – Newly discovered evidence from the G7 countries

This paper addresses the question of whether financial market participants apply the framework of Taylor-type rules in their forecasts for the G7 countries. To this end, we use the Consensus Economic Forecast poll providing us a unique data set of inflation rate, interest rate and growth rate forecasts for the time period 1989-2008. We provide empirical evidence that financial market participants incorporate Taylor-type rules in their forecasts. Thus, the paper uses ex-ante data for the estimation of Taylor rules. This is a new approach, because so far only ex-post (revised) or real-time data have been applied.     

The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey

This paper introduces a new nowcasting model of the French quarterly real GDP growth rate (MIBA), developed at the Banque de France and based on monthly business surveys. The model is designed to target initial announcements of GDP in a mixed-frequency framework. The selected equations for each forecast horizon are consistent with the time frame of real-time nowcasting exercises: the first one includes mainly information on the expected evolution of economic activity, while the second and third equations rely more on information on observed business outcomes. The predictive accuracy of the model increases over the forecast horizon, consistent with the gradual increase in available information. Furthermore, the model outperforms a wide set of alternatives, such as its previous version and MIDAS regressions, although not a specification including also hard data. Further research should evaluate the performance of the MIBA model with respect to promising alternative approaches for nowcasting GDP (e.g. mixed-frequency factor models with targeted predictors), and consider forecast combinations and density forecasts.     

Evaluating the joint efficiency of German trade forecasts - a nonparametric multivariate approach

ABSTRACT

I analyse the joint efficiency of export and import forecasts by leading economic research institutes for the years 1970 to 2017 for Germany in a multivariate setting. To this end, I compute, in a first step, multivariate random forests in order to model links between forecast errors and a forecaster’s information set, consisting of several trade and other macroeconomic predictor variables. I use the Mahalanobis distance as performance criterion and, in a second step, permutation tests to check whether the Mahalanobis distance between the predicted forecast errors for the trade forecasts and actual forecast errors is significantly smaller than under the null hypothesis of forecast efficiency. I find evidence for joint forecast inefficiency for two forecasters, however, for one forecaster I cannot reject joint forecast efficiency. For the other forecasters, joint forecast efficiency depends on the examined forecast horizon. I find evidence that real macroeconomic variables as opposed to trade variables are inefficiently included in the analysed trade forecasts. Finally, I compile a joint efficiency ranking of the forecasters.     

FORECASTING THE YIELD CURVE USING PRIORS FROM NO‐ARBITRAGE AFFINE TERM STRUCTURE MODELS*

I propose a strategy for forecasting the term structure of interest rates that may produce significant gains in predictive accuracy. The key idea is to use the restrictions implied by Gaussian, no‐arbitrage, affine term structure models on a vector autoregression as prior information instead of imposing the restrictions dogmatically. This allows us to account for possible model misspecification. We use the proposed method to forecast a system of five U.S. yields up to 12 months ahead, and we find it provides significant gains in forecast accuracy.     

成品油终端配送预测模型研究与应用

以一个成品油配送局部系统为研究基础,建立预测模型,模拟预测下一日需求量和下一日配车需求,为成品油销售系统实现主动配送提供了模型研究基础和实际应用例示。     

Inaccurate forecasts of the logistic growth model for Nobel Prizes

Modis [Technol. Forecast. Soc. Change 34 (1988) 95] reports that a logistic growth (LG) model of the number of U.S. Nobel Prize recipients provides an excellent fit for the period 1901-1987. This model forecasts that approximately 235 Americans will receive a Nobel Prize by year-end 2002 and that a total of 283 Americans will eventually receive a Nobel Prize. We use recent data (1901-2002) on prize recipients to provide a revised test of this model. The results of extensive holdout forecasting and nonlinear least-squares fits to the data provide convincing evidence that the LG model systematically underpredicts the number of Nobel Prizes awarded to Americans. For instance, the cumulative number of American recipients as of year-end 2002 is 270, significantly larger than the LG forecast of 235. We argue that other approaches to forecasting the number of future Nobel awards should be considered.     

‘Time-series’ versus ‘econometric’ forecasts : A non-linear regression counterexample

A non-linear regression model is presented in which a deterministic, model-based forecast of the dependent variable may have greater mean square error than a purely extrapolative forecast, in contrast to the result on relative forecast efficiency in linear models.     

Temporary financial equilibrium

Summary. In a two-period pure exchange economy with financial assets, a temporary financial equilibrium is an equilibrium of the current spot and security markets given forecast functions of future prices and payoffs. The temporary equilibrium model can then be interpreted as an Arrow-Debreu economy where preferences depend on prices. This identification implies, among other consequences, the existence and the generic determinateness of the financial temporary equilibria associated with given forecast functions. Received: December 29, 1999; revised version: December 20, 2001     

基于门限分位点回归的条件VaR风险度量

门限分位点回归模型是线性分位点回归模型的改进,是一种更加客观实际的非线性估计方法。利用该模型实证分析了单只股票(民生银行)的条件VaR。选择一种流动性风险指标作为条件,经过分析发现,由门限分位点回归模型得到结果能够更好地描述市场,也能更好地预测市场风险。     

Real-time forecasts of economic activity for Latin American economies

Macroeconomic policy decisions in real-time are based on the assessment of current and future economic conditions. Crucially, these assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released infrequently with a (sometimes substantial) lag. This paper evaluates nowcasts and forecasts of real GDP growth using five models for ten Latin American countries. The results indicate the flow of monthly data helps to improve forecast accuracy, and the dynamic factor model consistently produces more accurate nowcasts and forecasts relative to other model specifications, across most of the countries we consider.