Using big data in official statistics: Why? When? How? What for?

[EN] This paper analyses the potential usefulness of big data in official statistics starting from four key questions such as Why? When? How? and What for - should we use big data in official statistics? To derive some answers related to empirical cases. This paper presents a big data classification by types, which is then used to identify how big data can answer to specific information needs in key policy areas. Based on the findings of these investigations, some very provisional and subjective answers to the questions raised above are derived.Mazzi, GL. (2018). Using big data in official statistics: Why? When? How? What for?. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 237-245. https://doi.org/10.4995/CARMA2018.2018.8576OCS23724

Construction of coincident indicators for the euro area. 5th EUROSTAT Colloquium on Modern Tools For Business Cycle Analysis, Luxembourg, 29th September – 1st October 2008.

The availability of timely and reliable information on main macroeconomic variables is considered both by policy makers and analysts as crucial for an effective process of decision making. Unfortunately official statistics cannot always meet adequately user needs. This is the reason why, using econometric techniques analysts try to anticipate or estimate in real time main macroeconomic movements. In this paper we compare several econometric models for the estimation of the period on period growth rate for the euro area Gross Domestic Product (GDP) and Industrial Production Index (IPI). This comparison is made on the basis of real time results provided by these models over six years (2002-2007). Tests of absence of bias are performed and Diebold-Mariano tests help us to select among the models. The paper also presents a new indicator for euro area employment quarterly growth, which seems to perform rather well in the recent past, although this is still a preliminary assessment as we are only at an early stage of running the indicator.coincident indicators;GDP;industrial production;employment;euro area;

Construction of coincident indicators for euro area key macroeconomic variables. 28th International Symposium on Forecasting, Nice, June 23 2008.

The availability of timely and reliable information on main macroeconomic variables is considered both by policy makers and analysts crucial for an effective process of decision making. Unfortunately official statistics cannot always meet adequately users' needs, especially concerning their timely availability. This is the reason why, using econometric techniques, analysts try to anticipate or estimate in real time short-term movements of main macroeconomic variables. In this paper we propose a strategy simple and easily replicable in production processes for the estimation of the period on period growth rates of the euro area Industrial Production Index and Gross Domestic Product (GDP). Our strategy is based on the classical multivariate regression model on growth rates with autoregressive error term which is widely used in anticipating economic movements. Concerning GDP three different equations were identified, while for Industrial Production Index we have identified only two suitable representations. Furthermore for both variables we also use a purely autoregressive representation as a benchmark.

X11-like Seasonal Adjustment of Daily Data

Resumen de la ponencia[EN] High frequency data, i.e. data observed at infra-monthly intervals, have been used for decades by statisticians and econometricians in the financial and industrial worlds. Weekly data were already used in the 20’s by official statisticians to assess the short-term evolution of the Economy. For example, Crum (1927) studied the series of weekly bank debits outside New York city from 1919 to 1026 and proposed a method to seasonally adjust these data based on the median-link-relative method developed by Persons (1919). Nowadays, these data are ubiquitous and concern almost all sectors of the Economy. Numerous variables are collected weekly, daily or even hourly, that could bring valuable information to official statisticians in their evaluation of the state and short-term evolution of the Economy. But these data also bring challenges with them: they are very volatiles and show more outliers and breaks; they present multiple and non integer periodicities and their correct modeling implies numerous regressors: calendar effects, outliers, harmonics. The current statistician’s traditional toolbox, methods and algorithms, has been developed mainly for monthly and quarterly series; how should these tools be adapted to handle time series of thousands observations with specific characteristics and dynamics efficiently? We present some ideas to adapt the main seasonal adjustment methods, and especially “the X11 family” i.e. methods based on moving averages like X11, X11-ARIMA, X12-ARIMA and X-13ARIMA-SEATS. We also make some recommendations about the most appropriate methods for pretreatment and filtering of daily and weekly data.Ladiray, D.; Mazzi, GL. (2018). X11-like Seasonal Adjustment of Daily Data. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 266-266. https://doi.org/10.4995/CARMA2018.2018.8574OCS26626

Some guidance for the use of Big Data in macroeconomic nowcasting

[EN] This paper develops an operational step by step approach aiming to facilitate the use of Big Data in nowcasting exercises. Each step includes a description of the problem and a set of recommendations addressing the most relevant available solution. The approach includes nine steps starting from the theoretical availability of Big Data until the publication of new nowcasting including also Big Data. In designing this operational step by step approach, the preliminary results of an ongoing Eurostat project on Big Data and macroeconomic nowcasting have been used as a starting point. Further elaboration has been carried out in order to make the operational step by step approach more concrete and prescriptive. Its aim is to provide a concrete help for experts involved in the construction of nowcasting especially in the judgment about the usefulness of the presence of Big Data in their models. It also provides guidance related to the dissemination of new nowcasting based also on Big Data.Mazzi, GL. (2016). Some guidance for the use of Big Data in macroeconomic nowcasting. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 7-14. https://doi.org/10.4995/CARMA2016.2015.4243OCS71

Evaluation of Nonlinear time-series models for real-time business cycle analysis of the Euro area

In this paper, we aim at assessing Markov-switching and threshold models in their ability to identify turning points of economic cycles. By using vintage data that are updated on a monthly basis, we compare their ability to detect ex-post the occurrence of turning points of the classical business cycle, we evaluate the stability over time of the signal emitted by the models and assess their ability to detect in real-time recession signals. In this respect, we have built an historical vintage database for the Euro area going back to 1970 for two monthly macroeconomic variables of major importance for short-term economic outlook, namely the Industrial Production Index and the Unemployment Rate.Business cycle, Euro zone, Markov switching model, SETAR model, unemployment, industrial production.

Survey Data as Coicident or Leading Indicators

In this paper we propose a monthly measure for the euro area Gross Domestic Product (GDP) based on a small scale factor model for mixed frequency data, featuring two factors: the first is driven by hard data, whereas the second captures the contribution of survey variables as coincident indicators. Within this framework we evaluate both the in-sample contribution of the second survey-based factor, and the short term forecasting performance of the model in a pseudo-real time experiment. We find that the survey-based factor plays a significant role for two components of GDP: Industrial Value Added and Exports. Moreover, the two factor model outperforms in terms of out of sample forecasting accuracy the traditional autoregressive distributed lags (ADL) specifications and the single factor model, with few exceptions for Exports and in growth rates.Survey data, Temporal Disaggregation. Multivariate State Space Models. Dynamic factor Models. Kalman filter and smoother. Chain-linking

A Monthly Indicator of the Euro Area GDP

A continuous monitoring of the evolution of the economy is fundamental for the decisions of public and private decision makers. This paper proposes a new monthly indicator of the euro area real Gross Domestic Product (GDP), with several original features. First, it considers both the output side (six branches of the NACE classification) and the expenditure side (the main GDP components) and combines the two estimates with optimal weights reflecting their relative precision. Second, the indicator is based on information at both the monthly and quarterly level, modelled with a dynamic factor specification cast in state-space form. Third, since estimation of the multivariate dynamic factor model can be numerically complex, computational efficiency is achieved by implementing univariate filtering and smoothing procedures. Finally, special attention is paid to chain-linking and its implications, via a multistep procedure that exploits the additivity of the volume measures expressed at the prices of the previous year.Temporal Disaggregation, Multivariate State Space Models, Dynamic factor Models, Kalman filter and smoother, Chain-linking

Real time estimation of potential output and output gap for theeuro-area: comparing production function with unobserved componentsand SVAR approaches

We develop a new version of the production function (PF) approach usually used for estimating the output gap of the euro area. Our version does not call for any (often imprecise) measure of the capital stock and improves the estimation of the trend total factor productivity. We asses this approach by comparing it with two other multivariate methods mostly used for output gap estimates, a multivariate unobserved components (MUC) model and a Structural Vector Auto-Regressive (SVAR) model. The comparison is conducted by relying on assessment criteria such as the concordance of the turning points chronology with a reference one, the inflation forecasting power and the real-time consistency of the estimates. Two contributions are achieved. Firstly, we take into account data revisions and their impact on the output gap estimates by using vintage datasets coming from the Euro Area Business Cycle (EABCN) Real-Time Data-Base (RTDB). Secondly, the PF approach, generally employed by policy-makers despite of its difficult implementation, is assessed. We thus improve on previous papers which limited their assessment on other multivariate methods, e.g. MUC or SVAR models. The different methods show different ranks in relation to the three criteria. This new PF estimate appears highly concordant with the reference chronology. Its forecasting power appears favourable only for the shortest horizon (1 month). Finally, the SVAR model appears more consistent in real-time.potential output, production function, state-space models, structural VARs