GDP trend deviations and the yield spread: the case of eight E.U. countries

Several studies have established the predictive power of the yield curve in terms of real economic activity. In this paper we use data for a variety of E.U. countries: both EMU (Germany, France, Italy, Portugal and Spain) and non-EMU members (Norway, Sweden and the U.K.). The data used range from 1991:Q1 to 2009:Q3. For each country, we extract the long run trend and the cyclical component of real economic activity, while the corresponding ECB euro area government benchmark bond interest rates of long and short term maturities are used for the calculation of the yield spreads. We also augment the models tested with non monetary policy variables: the respective unemployment rates and stock indices. The methodology employed in the effort to forecast real output, is a probit model of the inverse cumulative distribution function of the standard distribution, using several formal forecasting and goodness of fit evaluation criteria. The results show that the yield curve augmented with the non-monetary variables has significant forecasting power in terms of real economic activity but the results differ qualitatively between the individual economies examined raising non-trivial policy implications.     

Influence of earnings management on forecasting corporate failure

This paper studies the relationship between corporate failure forecasting and earnings management variables. Using a new threshold model approach that separates samples into different regimes according to a threshold variable, the authors examine regimes to evaluate the prediction capacities of earnings management variables. By proposing this threshold model and applying it innovatively, this research reveals boundaries within which earnings management variables can yield superior corporate failure forecasting. The inclusion of earnings management variables in corporate failure models improves failure prediction capacities for firms that manipulate substantial earnings. Furthermore, an accruals-based variable improves predictions of failed firms, but the real activities-based variable improves predictions of non-failed firms. These findings highlight the importance of indicators of the magnitude of earnings management and the tools used to improve the performance of corporate failure models. The proposed model can determine the predictive power of particular explanatory variables to forecast corporate failure.     

Predicting recessions with a composite real-time dynamic probit model

In this paper we propose a composite indicator for real-time recession forecasting based on alternative dynamic probit models. For this purpose, we use a large set of monthly macroeconomic and financial leading indicators from the German and US economies. Alternative dynamic probit regressions are specified through automated general-to-specific and specific-to-general lag selection procedures on the basis of slightly different initial sets. The resulting recession probability forecasts are then combined in order to decrease the volatility of the forecast errors and increase their forecasting accuracy. This procedure features not only good in-sample forecast statistics, but also good out-of-sample performances, as is illustrated using a real-time evaluation exercise.     

Predicting relative forecasting performance: An empirical investigation

The relative performances of forecasting models change over time. This empirical observation raises two questions. First, is the relative performance itself predictable? Second, if so, can it be exploited in order to improve the forecast accuracy? We address these questions by evaluating the predictive abilities of a wide range of economic variables for two key US macroeconomic aggregates, namely industrial production and inflation, relative to simple benchmarks. We find that business cycle indicators, financial conditions, uncertainty and measures of past relative performances are generally useful for explaining the models’ relative forecasting performances. In addition, we conduct a pseudo-real-time forecasting exercise, where we use the information about the conditional performance for model selection and model averaging. The newly proposed strategies deliver sizable improvements over competitive benchmark models and commonly-used combination schemes. The gains are larger when model selection and averaging are based on both financial conditions and past performances measured at the forecast origin date.     

Predicting stock market crises using daily stock market valuation and investor sentiment indicators

The purpose of this paper is to develop a daily early warning system for stock market crises using daily stock market valuation and investor sentiment indicators. To achieve this goal, we use principal components analysis to propose a comprehensive index of daily market indicators that reflects stock market valuation and investor sentiment. Based on the comprehensive index, we employ a logit model with Ensemble Empirical Mode Decomposition to develop a daily early warning system for stock market crises. Finally, we apply the proposed system to the early warning for stock market crises in China. The in-sample forecasting results show that investor sentiment and the forecast horizon by Ensemble Empirical Mode Decomposition improve the forecasting performance of conventional early warning systems. The out-of-sample forecasting results indicate that the proposed warning system still has a good performance.     

Leading Indicators for Euro‐area Inflation and GDP Growth*

In this paper, we evaluate the role of a set of variables as leading indicators for Euro‐area inflation and GDP growth. Our leading indicators are taken from the variables in the European Central Bank's (ECB) Euro‐area‐wide model database, plus a set of similar variables for the US. We compare the forecasting performance of each indicator ex post with that of purely autoregressive models. We also analyse three different approaches to combining the information from several indicators. First, ex post, we discuss the use as indicators of the estimated factors from a dynamic factor model for all the indicators. Secondly, within an ex ante framework, an automated model selection procedure is applied to models with a large set of indicators. No future information is used, future values of the regressors are forecast, and the choice of the indicators is based on their past forecasting records. Finally, we consider the forecasting performance of groups of indicators and factors and methods of pooling the ex ante single‐indicator or factor‐based forecasts. Some sensitivity analyses are also undertaken for different forecasting horizons and weighting schemes of forecasts to assess the robustness of the results.     

Forecasting the New York State economy: The coincident and leading indicators approach

Currently there are no reliable summary indicators of the economic and fiscal condition of states and localities. This deficiency has hampered the efforts of policy makers at the sub-national level to monitor changes in the economic environment and predict how those changes will impact the fiscal health of governments. This paper attempts to fill this analytical vacuum by providing summary indicators of economic and fiscal health for New York State. The models developed are based on the single-index methodology developed by Stock and Watson [(1991). A probability model of the coincident economic indicators. In K. Lahiri and G. H. Moore (eds.), Leading economic indicators: new approaches and forecasting records (pp. 63–85). New York: Cambridge University Press]. This approach allows us to date New York business cycles and compare local cyclical behavior with the nation as a whole. We develop a leading index of economic indicators which predicts future movements in the coincident indicator. The Stock and Watson approach is used to create a fiscal indicator which acts as a summary indicator of revenue performance for New York. In addition, we explore the ability of our economic indicator series to predict future changes in state revenues. We find that changes in the leading indicator series have significant predictive power in forecasting changes in our revenue index.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

Forecasting temperature to price CME temperature derivatives

This paper seeks to forecast temperatures in US cities in order to price temperature derivatives on the Chicago Mercantile Exchange (CME). The CME defines the average daily temperature underlying its contracts as the average of the maximum and minimum daily temperatures, yet all published work on temperature forecasting for pricing purposes has ignored this peculiar definition of the average and sought to model the average temperature directly. This paper is the first to look at the average temperature forecasting problem as an analysis of extreme values. The theory of extreme values guides model selection for temperature maxima and minima, and a forecast distribution for the CME’s daily average temperature is found through convolution. While univariate time series AR-GARCH and regression models generally yield superior point forecasts of temperatures, our extreme-value-based model consistently outperforms these models in density forecasting, the most important risk management tool.     

The impact of institutional change on forecast accuracy: A case study of budget forecasting in Washington State

This paper explores the relationship between institutional change and forecast accuracy via an analysis of the entitlement caseload forecasting process in Washington State. This research extends the politics of forecasting literature beyond the current area of government revenue forecasting to include expenditure forecasting and introduces an in-depth longitudinal study to the existing set of cross-sectional studies. Employing a fixed-effects model and ordinary least squares regression analysis, this paper concludes that the establishment of an independent forecasting agency and subsequent formation of technical workgroups improve forecast accuracy. Additionally, this study finds that more frequent forecast revisions and structured domain knowledge improve forecast accuracy.     

Real-time squared: A real-time data set for real-time GDP forecasting

This paper uses real-time data to mimic real-time GDP forecasting activity. Through automatic searches for the best indicators for predicting GDP one and four steps ahead, we compare the out-of-sample forecasting performance of adaptive models using different data vintages, and produce three main findings. First, despite data revisions, the forecasting performance of models with indicators is better, but this advantage tends to vanish over longer forecasting horizons. Second, the practice of using fully updated datasets at the time the forecast is made (i.e., taking the best available measures of today's economic situation) does not appear to bring any effective improvement in forecasting ability: the first GDP release is predicted equally well by models using real-time data as by models using the latest available data. Third, although the first release is a rational forecast of GDP data after all statistical revisions have taken place, the forecast based on the latest available GDP data (i.e. the “temporarily best” measures) may be improved by combining preliminary official releases with one-step-ahead forecasts.     

Google data in bridge equation models for German GDP

Interest in the use of “big data” when it comes to forecasting macroeconomic time series such as private consumption or unemployment has increased; however, applications to the forecasting of GDP remain rather rare. This paper incorporates Google search data into a bridge equation model, a version of which usually belongs to the suite of forecasting models at central banks. We show how such big data information can be integrated, with an emphasis on the appeal of the underlying model in this respect. As the decision as to which Google search terms should be added to which equation is crucial —- both for the forecasting performance itself and for the economic consistency of the implied relationships —- we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo real time out-of-sample forecast performances for GDP, various GDP components and monthly activity indicators. We find that sizeable gains can indeed be obtained by using Google search data, where the best-performing Google variable selection approach varies according to the target variable. Thus, assigning the selection methods flexibly to the targets leads to the most robust outcomes overall in all layers of the system.     

基于多级模糊综合评价法的房地产企业财务预警研究

随着国内经济形势的不断变化和国家房地产相关政策的出台,房地产企业在经营中各种不确定因素不断增加,财务风险也与日俱增。房地产企业的财务风险具有模糊性和复杂性,它受到财务和非财务等多种因素的影响。以财务指标为变量建立的预警模型无法全面预测企业的财务风险。为提高财务预警系统的有效性,本文以房地产企业这一微观群体为视角,在以财务指标为主体的财务预警系统基础上,结合房地产企业的特征选取非财务指标,对影响房地产企业财务预警的非财务指标进行分层研究,运用多级模糊综合评价法构建房地产企业的财务预警模型。这将对房地产企业构建财务预警体系大有裨益。     

Economic, organizational, and political influences on biases in forecasting state sales tax receipts

This paper investigates factors influencing fixed bias in forecasting state sales taxes revenues. By extending an existing model used to explain forecast accuracy to include a series of complex interactions related to the potential political and policy use of revenue forecasts, the paper extends our understanding of the forecasting process in government. Exploratory empirical analysis based on survey data is used to provide evidence that bias in forecasting results, at least in part, from political and policy manipulation. There is also evidence that institutional reforms associated with ‘good management’ practices affect forecast bias, but in complex ways depending upon the extent to which political competition exists within the state.     

Are financial spreads useful indicators of future inflation and output growth in EU countries?

This paper seeks to address the policy issue of the usefulness of financial spreads as indicators of future inflation and output growth in the countries of the European Union, placing a particular focus on out-of-sample forecasting performance. Such analysis is of considerable relevance to monetary authorities, given the breakdown of the money/income relation in a number of countries and following increased emphasis of domestic monetary policy on control of inflation following the broadening of the ERM bands. The results confirm that for some countries, financial spread variables do contain some information about future output growth and inflation, with the yield curve and the reverse yield gap performing best. However, the relatively poor out-of-sample forecasting performance and/or parameter instability suggests that the need for caution in using spread variables for forecasting in EU countries. Only a small number of spreads contain information, and improve forecasting in a manner which is stable over time. © 1997 John Wiley & Sons, Ltd.     

Forecasting the term structure of government bond yields

Despite powerful advances in yield curve modeling in the last 20 years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do so. We use neither the no-arbitrage approach nor the equilibrium approach. Instead, we use variations on the Nelson–Siegel exponential components framework to model the entire yield curve, period-by-period, as a three-dimensional parameter evolving dynamically. We show that the three time-varying parameters may be interpreted as factors corresponding to level, slope and curvature, and that they may be estimated with high efficiency. We propose and estimate autoregressive models for the factors, and we show that our models are consistent with a variety of stylized facts regarding the yield curve. We use our models to produce term-structure forecasts at both short and long horizons, with encouraging results. In particular, our forecasts appear much more accurate at long horizons than various standard benchmark forecasts.     

Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data

This paper discusses a factor model for short-term forecasting of GDP growth using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM algorithm, combined with a principal components estimator. We discuss some in-sample properties of the estimator in a real-time environment and propose alternative methods for forecasting quarterly GDP with monthly factors. In the empirical application, we use a novel real-time dataset for the German economy. Employing a recursive forecast experiment, we evaluate the forecast accuracy of the factor model with respect to German GDP. Furthermore, we investigate the role of revisions in forecast accuracy and assess the contribution of timely monthly observations to the forecast performance. Finally, we compare the performance of the mixed-frequency model with that of a factor model, based on time-aggregated quarterly data.     

Anchoring the yield curve using survey expectations

The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations can accurately predict yields, but they are typically not available for all maturities and/or forecast horizons. We show how survey expectations can be exploited to improve the accuracy of yield curve forecasts given by a base model. We do so by employing a flexible exponential tilting method that anchors the model forecasts to the survey expectations, and we develop a test to guide the choice of the anchoring points. The method implicitly incorporates into yield curve forecasts any information that survey participants have access to—such as information about the current state of the economy or forward‐looking information contained in monetary policy announcements—without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy relative to the class of models that are widely adopted by financial and policy institutions for forecasting the term structure of interest rates.     

A new approach to estimating earnings forecasting models: Robust regression MM-estimation

This study focuses on the impact of model estimation methods on earnings forecast accuracy. Compared with an ordinary least squares (OLS) regression combined with winsorization, robust regression MM-estimation improves the earnings forecast accuracy of all the models examined, especially for those with more variables. My findings indicate that the impact of outliers on the OLS regression increases with the number of variables in the models, alerting researchers who use OLS regressions for forecasting. My findings explain the puzzling negative relationship between earnings forecast accuracy and the number of model variables in prior research. Moreover, I demonstrate the valuation implications of earnings forecasted using robust regression MM-estimation. This study contributes to earnings forecasting, valuation, and influential observation treatment in forecasting.     

A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP

We derive forecast weights and uncertainty measures for assessing the roles of individual series in a dynamic factor model (DFM) for forecasting the euro area GDP from monthly indicators. The use of the Kalman smoother allows us to deal with publication lags when calculating the above measures. We find that surveys and financial data contain important information for the GDP forecasts beyond the monthly real activity measures. However, this is discovered only if their more timely publication is taken into account properly. Differences in publication lags play a very important role and should be considered in forecast evaluation.