Essays in real-time forecasting

This thesis contains three essays in the field of real-time econometrics, and more particularly<p>forecasting.<p>The issue of using data as available in real-time to forecasters, policymakers or financial<p>markets is an important one which has only recently been taken on board in the empirical<p>literature. Data available and used in real-time are preliminary and differ from ex-post<p>revised data, and given that data revisions may be quite substantial, the use of latest<p>available instead of real-time can substantially affect empirical findings (see, among others,<p>Croushore’s (2011) survey). Furthermore, as variables are released on different dates<p>and with varying degrees of publication lags, in order not to disregard timely information,<p>datasets are characterized by the so-called “ragged-edge”structure problem. Hence, special<p>econometric frameworks, such as developed by Giannone, Reichlin and Small (2008) must<p>be used.<p>The first Chapter, “The impact of macroeconomic news on bond yields: (in)stabilities over<p>time and relative importance”, studies the reaction of U.S. Treasury bond yields to real-time<p>market-based news in the daily flow of macroeconomic releases which provide most of the<p>relevant information on their fundamentals, i.e. the state of the economy and inflation. We<p>find that yields react systematically to a set of news consisting of the soft data, which have<p>very short publication lags, and the most timely hard data, with the employment report<p>being the most important release. However, sub-samples evidence reveals that parameter<p>instability in terms of absolute and relative size of yields response to news, as well as<p>significance, is present. Especially, the often cited dominance to markets of the employment<p>report has been evolving over time, as the size of the yields reaction to it was steadily<p>increasing. Moreover, over the recent crisis period there has been an overall switch in the<p>relative importance of soft and hard data compared to the pre-crisis period, with the latter<p>becoming more important even if less timely, and the scope of hard data to which markets<p>react has increased and is more balanced as less concentrated on the employment report.<p>Markets have become more reactive to news over the recent crisis period, particularly to<p>hard data. This is a consequence of the fact that in periods of high uncertainty (bad state),<p>markets starve for information and attach a higher value to the marginal information content<p>of these news releases.<p>The second and third Chapters focus on the real-time ability of models to now-and-forecast<p>in a data-rich environment. It uses an econometric framework, that can deal with large<p>panels that have a “ragged-edge”structure, and to evaluate the models in real-time, we<p>constructed a database of vintages for US variables reproducing the exact information that<p>was available to a real-time forecaster.<p>The second Chapter, “Real-time nowcasting of GDP: a factor model versus professional<p>forecasters”, performs a fully real-time nowcasting (forecasting) exercise of US real GDP<p>growth using Giannone, Reichlin and Smalls (2008), henceforth (GRS), dynamic factor<p>model (DFM) framework which enables to handle large unbalanced datasets as available<p>in real-time. We track the daily evolution throughout the current and next quarter of the<p>model nowcasting performance. Similarly to GRS’s pseudo real-time results, we find that<p>the precision of the nowcasts increases with information releases. Moreover, the Survey of<p>Professional Forecasters does not carry additional information with respect to the model,<p>suggesting that the often cited superiority of the former, attributable to judgment, is weak<p>over our sample. As one moves forward along the real-time data flow, the continuous<p>updating of the model provides a more precise estimate of current quarter GDP growth and<p>the Survey of Professional Forecasters becomes stale. These results are robust to the recent<p>recession period.<p>The last Chapter, “Real-time forecasting in a data-rich environment”, evaluates the ability<p>of different models, to forecast key real and nominal U.S. monthly macroeconomic variables<p>in a data-rich environment and from the perspective of a real-time forecaster. Among<p>the approaches used to forecast in a data-rich environment, we use pooling of bi-variate<p>forecasts which is an indirect way to exploit large cross-section and the directly pooling of<p>information using a high-dimensional model (DFM and Bayesian VAR). Furthermore forecasts<p>combination schemes are used, to overcome the choice of model specification faced by<p>the practitioner (e.g. which criteria to use to select the parametrization of the model), as<p>we seek for evidence regarding the performance of a model that is robust across specifications/<p>combination schemes. Our findings show that predictability of the real variables is<p>confined over the recent recession/crisis period. This in line with the findings of D’Agostino<p>and Giannone (2012) over an earlier period, that gains in relative performance of models<p>using large datasets over univariate models are driven by downturn periods which are characterized<p>by higher comovements. These results are robust to the combination schemes<p>or models used. A point worth mentioning is that for nowcasting GDP exploiting crosssectional<p>information 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,<p>monthly variables carry information content for GDP. But similarly to the findings for the<p>monthly variables, predictability, as measured by the gains relative to the naive random<p>walk model, is higher during crisis/recession period than during tranquil times. Regarding<p>inflation, results are stable across time, but predictability is mainly found at nowcasting<p>and forecasting one-month ahead, with the BVAR standing out at nowcasting. The results<p>show that the forecasting gains at these short horizons stem mainly from exploiting timely<p>information. The results also show that direct pooling of information using a high dimensional<p>model (DFM or BVAR) which takes into account the cross-correlation between the<p>variables and efficiently deals with the “ragged-edge”structure of the dataset, yields more<p>accurate forecasts than the indirect pooling of bi-variate forecasts/models.Doctorat en Sciences économiques et de gestion