Essays in real-time forecasting

This thesis contains three essays in the field of real-time econometrics, and more particularlyforecasting.The issue of using data as available in real-time to forecasters, policymakers or financialmarkets is an important one which has only recently been taken on board in the empiricalliterature. Data available and used in real-time are preliminary and differ from ex-postrevised data, and given that data revisions may be quite substantial, the use of latestavailable instead of real-time can substantially affect empirical findings (see, among others,Croushore’s (2011) survey). Furthermore, as variables are released on different datesand with varying degrees of publication lags, in order not to disregard timely information,datasets are characterized by the so-called “ragged-edge”structure problem. Hence, specialeconometric frameworks, such as developed by Giannone, Reichlin and Small (2008) mustbe used.The first Chapter, “The impact of macroeconomic news on bond yields: (in)stabilities overtime and relative importance”, studies the reaction of U.S. Treasury bond yields to real-timemarket-based news in the daily flow of macroeconomic releases which provide most of therelevant information on their fundamentals, i.e. the state of the economy and inflation. Wefind that yields react systematically to a set of news consisting of the soft data, which havevery short publication lags, and the most timely hard data, with the employment reportbeing the most important release. However, sub-samples evidence reveals that parameterinstability in terms of absolute and relative size of yields response to news, as well assignificance, is present. Especially, the often cited dominance to markets of the employmentreport has been evolving over time, as the size of the yields reaction to it was steadilyincreasing. Moreover, over the recent crisis period there has been an overall switch in therelative importance of soft and hard data compared to the pre-crisis period, with the latterbecoming more important even if less timely, and the scope of hard data to which marketsreact has increased and is more balanced as less concentrated on the employment report.Markets have become more reactive to news over the recent crisis period, particularly tohard data. This is a consequence of the fact that in periods of high uncertainty (bad state),markets starve for information and attach a higher value to the marginal information contentof these news releases.The second and third Chapters focus on the real-time ability of models to now-and-forecastin a data-rich environment. It uses an econometric framework, that can deal with largepanels that have a “ragged-edge”structure, and to evaluate the models in real-time, weconstructed a database of vintages for US variables reproducing the exact information thatwas available to a real-time forecaster.The second Chapter, “Real-time nowcasting of GDP: a factor model versus professionalforecasters”, performs a fully real-time nowcasting (forecasting) exercise of US real GDPgrowth using Giannone, Reichlin and Smalls (2008), henceforth (GRS), dynamic factormodel (DFM) framework which enables to handle large unbalanced datasets as availablein real-time. We track the daily evolution throughout the current and next quarter of themodel nowcasting performance. Similarly to GRS’s pseudo real-time results, we find thatthe precision of the nowcasts increases with information releases. Moreover, the Survey ofProfessional Forecasters does not carry additional information with respect to the model,suggesting that the often cited superiority of the former, attributable to judgment, is weakover our sample. As one moves forward along the real-time data flow, the continuousupdating of the model provides a more precise estimate of current quarter GDP growth andthe Survey of Professional Forecasters becomes stale. These results are robust to the recentrecession period.The last Chapter, “Real-time forecasting in a data-rich environment”, evaluates the abilityof different models, to forecast key real and nominal U.S. monthly macroeconomic variablesin a data-rich environment and from the perspective of a real-time forecaster. Amongthe approaches used to forecast in a data-rich environment, we use pooling of bi-variateforecasts which is an indirect way to exploit large cross-section and the directly pooling ofinformation using a high-dimensional model (DFM and Bayesian VAR). Furthermore forecastscombination schemes are used, to overcome the choice of model specification faced bythe practitioner (e.g. which criteria to use to select the parametrization of the model), aswe seek for evidence regarding the performance of a model that is robust across specifications/combination schemes. Our findings show that predictability of the real variables isconfined over the recent recession/crisis period. This in line with the findings of D’Agostinoand Giannone (2012) over an earlier period, that gains in relative performance of modelsusing large datasets over univariate models are driven by downturn periods which are characterizedby higher comovements. These results are robust to the combination schemesor models used. A point worth mentioning is that for nowcasting GDP exploiting crosssectionalinformation along the real-time data flow also helps over the end of the great moderation period. Since this is a quarterly aggregate proxying the state of the economy,monthly variables carry information content for GDP. But similarly to the findings for themonthly variables, predictability, as measured by the gains relative to the naive randomwalk model, is higher during crisis/recession period than during tranquil times. Regardinginflation, results are stable across time, but predictability is mainly found at nowcastingand forecasting one-month ahead, with the BVAR standing out at nowcasting. The resultsshow that the forecasting gains at these short horizons stem mainly from exploiting timelyinformation. The results also show that direct pooling of information using a high dimensionalmodel (DFM or BVAR) which takes into account the cross-correlation between thevariables and efficiently deals with the “ragged-edge”structure of the dataset, yields moreaccurate forecasts than the indirect pooling of bi-variate forecasts/models.Doctorat en Sciences économiques et de gestion