The Effect of Data Revisions on the Basic New Keynesian Model

This paper proposes an extended version of the basic New Keynesian monetary (NKM) model which contemplates revision processes of output and inflation data in order to assess the importance of data revisions on the estimated monetary policy rule parameters and the transmission of policy shocks. Our empirical evidence based on a structural econometric approach suggests that although the initial announcements of output and inflation are not rational forecasts of revised output and inflation data, ignoring the presence of non well-behaved revision processes may not be a serious drawback in the analysis of monetary policy in this framework. However, the transmission of inflation-push shocks is largely affected by considering data revisions. The latter being especially true when the nominal stickiness parameter is estimated taking into account data revision processes.

Data Revisions in the Estimation of DSGE Models

Revisions of US macroeconomic data are not white-noise. They are persistent, correlated with real-time data, and with high variability (around 80% of volatility observed in US real-time data). Their business cycle effects are examined in an estimated DSGE model extended with both real-time and final data. After implementing a Bayesian estimation approach, the role of both habit formation and price indexation fall significantly in the extended model. The results show how revision shocks of both output and inflation are expansionary because they occur when real-time published data are too low and the Fed reacts by cutting interest rates. Consumption revisions, by contrast, are countercyclical as consumption habits mirror the observed reduction in real-time consumption. In turn, revisions of the three variables explain 9.3% of changes of output in its long-run variance decomposition.

Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis

Global hydrological models (GHMs) model the land surface hydrologic dynamics of continental-scale river basins. Here we describe one such GHM, the Macro-scale - Probability-Distributed Moisture model.09 (Mac-PDM.09). The model has undergone a number of revisions since it was last applied in the hydrological literature. This paper serves to provide a detailed description of the latest version of the model. The main revisions include the following: (1) the ability for the model to be run for n repetitions, which provides more robust estimates of extreme hydrological behaviour, (2) the ability of the model to use a gridded field of coefficient of variation (CV) of daily rainfall for the stochastic disaggregation of monthly precipitation to daily precipitation, and (3) the model can now be forced with daily input climate data as well as monthly input climate data. We demonstrate the effects that each of these three revisions has on simulated runoff relative to before the revisions were applied. Importantly, we show that when Mac-PDM.09 is forced with monthly input data, it results in a negative runoff bias relative to when daily forcings are applied, for regions of the globe where the day-to-day variability in relative humidity is high. The runoff bias can be up to - 80% for a small selection of catchments but the absolute magnitude of the bias may be small. As such, we recommend future applications of Mac-PDM.09 that use monthly climate forcings acknowledge the bias as a limitation of the model. The performance of Mac-PDM.09 is evaluated by validating simulated runoff against observed runoff for 50 catchments. We also present a sensitivity analysis that demonstrates that simulated runoff is considerably more sensitive to method of PE calculation than to perturbations in soil moisture and field capacity parameters.

Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions

We examine how the accuracy of real-time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest-available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple-vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.

Predicting early data revisions to US GDP and the effects of releases on equity markets

The effects of data uncertainty on real-time decision-making can be reduced by predicting early revisions to US GDP growth. We show that survey forecasts efficiently anticipate the first-revised estimate of GDP, but that forecasting models incorporating monthly economic indicators and daily equity returns provide superior forecasts of the second-revised estimate. We consider the implications of these findings for analyses of the impact of surprises in GDP revision announcements on equity markets, and for analyses of the impact of anticipated future revisions on announcement-day returns.

Anticipatory effects in the FTSE 100 index revisions

This paper examines the price impact of trading due to expected changes in the FTSE 100 index composition. We focus on the latter index because it employs publicly-known objective criteria to determine membership and hence it provides a natural context to investigate anticipatory trading e ects. We propose a panel-regression event study that backs out these anticipatory e ects by looking at the price impact of the ex-ante proba-bility of changing index membership status. Our ndings reveal that anticipative trading explains about 40% and 23% of the cumulative abnormal returns of additions and deletions, respectively. We con rm these in-sample results out of sample by tracking the performance of a trading strategy that relies on the addition/deletion probability estimates. The perfor-mance is indeed very promising in that it entails an average daily excess return of 11 basis points over the FTSE 100 index.

Target difficulty, intra-year target revisions and firm performance: evidence from business units' targets

Target difficulty is often argued to increase performance. While this association is well established in experimental research, empirical evidence in field research is rather mixed. We attempt to explain this inconsistency by analyzing the importance of intra-year target revisions, which are especially prevalent in real-world field settings. Using survey and archival data from 97 firms, we find that firms with more challenging business unit targets revise targets more often, in line with asymmetric, downward target revisions. Results further show that the degree to which targets are revised during a period results in negative effects on firm performance, as the anticipation of revision negatively affects the business unit management’s performance incentives. Additionally, we find that using targets predominantly for either decision-making or control influences the overall performance effects of target revisions. Our findings may partially explain the mixed field study evidence regarding the effects of target difficulty.

What Does the Market Learn from Stock Offering Revisions?

We examine the disclosure of size revisions of seasoned stock offerings to see what information revisions impart to investros. Revisions could deliver firm-originated infoirmation, which discloses something managers know about the firm. Alternatively, they could disseminate market-originated information, which is information market participants have but which is not conveyed until trading takes place. Our results reject the notion that revisions reveal firm-originated news. Instead, the results are consistent with the market-originated news hypothesis and suggest a mechanism that investros and underwriters use to learn about the demand for an offering.