Now-casting Irish GDP

In this paper we present "now-casts" of Irish GDP using timely data from a panel data set of 41 different variables. The approach seeks to resolve two issues which commonly confront forecastors of GDP - how to parsimoniously avail of the many different series, which can potentially influence GDP and how to reconcile the within-quarterly release of many of these series with the quarterly estimates of GDP? The now-casts in this paper are generated by firstly, using dynamic factor analysis to extract a common factor from the panel data set and, secondly, through use of bridging equations to relate the monthly data to the quarterly GDP estimates. We conduct an out-of-sample forecasting simulation exercise, where the results of the now-casting exercise are compared with those of a standard benchmark model.

Now-casting and the real-time data flow

The term now-casting is a contraction for now and forecasting and has been used for a long-time in meteorology and recently also in economics. In this paper we survey recent developments in economic now-casting with special focus on those models that formalize key features of how market participants and policy makers read macroeconomic data releases in real time, which involves: monitoring many data, forming expectations about them and revising the assessment on the state of the economy whenever realizations diverge sizeably from those expectations. (Prepared for G. Elliott and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 2, Elsevier-North Holland)

High-dimensional Linear Regression for Dependent Data with Applications to Nowcasting

Recent research has focused on $\ell_1$ penalized least squares (Lasso) estimators for high-dimensional linear regressions in which the number of covariates $p$ is considerably larger than the sample size $n$. However, few studies have examined the properties of the estimators when the errors and/or the covariates are serially dependent. In this study, we investigate the theoretical properties of the Lasso estimator for a linear regression with a random design and weak sparsity under serially dependent and/or nonsubGaussian errors and covariates. In contrast to the traditional case, in which the errors are independent and identically distributed and have finite exponential moments, we show that $p$ can be at most a power of $n$ if the errors have only finite polynomial moments. In addition, the rate of convergence becomes slower owing to the serial dependence in the errors and the covariates. We also consider the sign consistency of the model selection using the Lasso estimator when there are serial correlations in the errors or the covariates, or both. Adopting the framework of a functional dependence measure, we describe how the rates of convergence and the selection consistency of the estimators depend on the dependence measures and moment conditions of the errors and the covariates. Simulation results show that a Lasso regression can be significantly more powerful than a mixed-frequency data sampling regression (MIDAS) and a Dantzig selector in the presence of irrelevant variables. We apply the results obtained for the Lasso method to nowcasting with mixed-frequency data, in which serially correlated errors and a large number of covariates are common. The empirical results show that the Lasso procedure outperforms the MIDAS regression and the autoregressive model with exogenous variables in terms of both forecasting and nowcasting

Multivariate Hierarchical Frameworks for Modelling Delayed Reporting in Count Data

In many fields and applications count data can be subject to delayed reporting. This is where the total count, such as the number of disease cases contracted in a given week, may not be immediately available, instead arriving in parts over time. For short term decision making, the statistical challenge lies in predicting the total count based on any observed partial counts, along with a robust quantification of uncertainty. In this article we discuss previous approaches to modelling delayed reporting and present a multivariate hierarchical framework where the count generating process and delay mechanism are modelled simultaneously. Unlike other approaches, the framework can also be easily adapted to allow for the presence of under-reporting in the final observed count. To compare our approach with existing frameworks, one of which we extend to potentially improve predictive performance, we present a case study of reported dengue fever cases in Rio de Janeiro. Based on both within-sample and out-of-sample posterior predictive model checking and arguments of interpretability, adaptability, and computational efficiency, we discuss the advantages and disadvantages of each modelling framework.Comment: Biometrics (2019

Data Revisions and the Output Gap

Preliminary and delayed Colombian GDP reports are replaced with optimal in-sample now-casts of "true" GDP figures derived from a model for data revisions. The new GDP time series is augmented with optimal out-of-sample forecasts and back-casts of the "true" GDP figures derived from the same model. The trend-cycle component of the augmented GDP series is filtered. The resulting gap is more resistant than the ordinary HP filter to the end of sample optimal filtering problem as well as to GDP revisions and delays. The short term noise of the final output gap estimate is also reduced. Adjusting for data revisions and delays reduce the uncertainty of estimated gaps. The extended and further extended HP estimates of the output gap show an impressive efficiency gain with respect to the ordinary HP gap, 43% and 47% respectively, on average. The new extension increases the efficiency in 7.4%, on average, with respect to extended HP estimates. These results constitute a benchmark to future work on real time estimation of the output gap under GDP revisions and delays in Colombia.Data Revisions, Now-casting, Real Time Economic Analysis. Classification JEL: C22, C53, C82.

Observation and analysis of Etesian wind storms in the Saroniko Gulf

The purpose of this work is to study the Etesian winds in the Saroniko Gulf to understand the complicate physical processes that generate the windstorms in the Saroniko Gulf. It is developed a study of the weather patterns characteristic of the Saroniko Gulf and a comparisons of the wind data measured on land by the station of Helliniko airport (years: 1990-2000) and at sea by a buoy (years: 2000- 2002) during the month of August. It is presented a statistical model that shows, as a probability of occurrence, the variations of the wind speed in function of the variations of the wind direction. Finally the statistical model is compared with the RAMS limited area model during a test case in August 2004; the now-casting results obtained using these two different methods are compared to data measured by station of the automatic weather network of the HNM

Multi-sensor analysis of extreme events in North-Eastern Italy

International audienceThe North-eastern part of Italy is known to be one of the most rainy regions in Europe. In this paper three extreme events are analysed, using a multi-sensor observing system including a weather radar and a dense telemetric network of surface stations, recording precipitation, wind, temperature and relative humidity. The cases examined comprise two long lasting rainfall events impacting two distinct areas, and a vigorous hail-producing thunderstorm event over the plains. In all cases, inter-comparison between remotely sensed and surface observations, including estimates and measures of precipitation and wind, helps to better understand the behaviour of the atmosphere, thus supporting operational fore- and now-casting. In the case of widespread precipitation, a relation is suggested between the wind speed and direction at medium/low levels with the location of the maximum precipitation relative to the mountains. This reflects the dynamical interaction between the mountain barrier and the atmospheric flux impinging upon it. This flux can be estimated by the automatic weather station of Mt. Cesen, a focal point for a now-casting of the rain in the Veneto Region. Analysis of strong thunderstorm activity makes extensive use of radar data. In the examined case the interaction of a sea breeze-like circulation with a mesoscale trough gave rise to a distinct convergence line that triggered a severe and long-lived hail-producing multi-cell thunderstorm. The hail was successfully detected by the radar's hail detection algorithm

Modeling Data Revisions

A dynamic linear model for data revisions and delays is proposed. This model extends Jacobs & Van Norden's [13] in two ways. First, the "true" data series is observable up to a fixed period of time M. And second, preliminary figures might be biased estimates of the true series. Otherwise, the model follows Jacobs & Van Norden's [13] so their gains are extended through the new assumptions. These assumptions represent the data release process more realistically under particular circumstances, and improve the overall identification of the model. An application to the year to year growth of the Colombian quarterly GDP reveals that preliminary growth reports under-estimate the true growth, and that measurement errors are predictable from the information available at the data release. The models implemented in this note help this purpose.Data Revisions, Now-casting, Real Time Economic Analysis. Classification JEL: C22, C53, C82.