WP6 Early estimates of economic indicators. WP6 coordinator: Tomaž Špeh, SURS ESSNet Big data: BDES 2018 Sofia 14.,

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1 WP6 Early estimates of economic indicators WP6 coordinator: Tomaž Špeh, SURS ESSNet Big data: BDES 2018 Sofia 14.,

2 WP6 objectives Outline Summary of activities carried out and results achieved WP6 pilots: Early estimates of economic indicators Impact of big data sources on economic indicators (Correlation, Time lag, Selectivity, etc.) Quality assessment of the input, throughput and output phase of the process Lessons learned Data sources used and their specifics (Appropriateness, Accessibility, Availability in time) Data cleaning strategies Publication lags & realistic vintages of data, Data pretreatment Recommendations and future perspectives 2

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4 WP6 objectives The main goal of the WP6 was to explore how a combination of (early available) big data sources, administrative and existing official statistical data could be used in creating an existing or new early estimates for official statistics: Exploration of big data sources and statistical areas where those sources could be used Other administrative and statistical sources which could be combined with investigated big data sources Implementing business cases identified in SGA-1 period Data collection, data linking, data processing, methodological and IT issues Examples of calculated concrete estimates for economic indicators with quality assessment of results. 4

5 Work done Investigate multiple big data, administrative and other existing sources Collaboration with WP7 team -Joint meeting in Warsaw Nowcasting the turnover indicators (April 2016) One of the pilots that was started in WP6 SGA-1, Statistics Finland (basic proposal), Statistical Office Slovenia Finalizing the proposal for SGA2 (November 2016) Successes to have access to big data sources First estimates of early economic indicators using also big data source were produced WP6 coordination meeting, Ljubljana, October 2017, WP6&WP7 face to face meeting, Lisbon, April Draft deliverables prepared 5

6 Proposed pilot for WP6 SGA-2 Title of the pilot: Early estimates of economic indicators Potential economic indicators: Gross domestic product (GDP), Consumer price index (CPI), Industry production index (IPI) Retail sale, Balance of payments, Economic sentiment indictors, New leading economic indicators Create and test the methodology of creating early estimates for at least one of the main economic indicators. Test the quality measures which assess quality of the sources, statistical production and statistical results Investigate multiple Big data and other existing sources for purposes of early estimates of at least one of the main economic indicators Many of the sources are available in most of the countries so it is possible to test them and create the results for more than one country. 6

7 WP6 SGA-2 Pilots data and Use of electronic transactions of System of payments and of the Anti-Money Laundering Reports data on estimating private household consumption Machine learning approaches for nowcasting GDP and TIO using firm-level traffic loops data Estimating early GDP and IPI using traffic loops data Predicting exports, based on Nights spent in tourism establishments Using internet data sources about the property market and job offers to forecast coincident and leading indicators Using Monte Carlo Markov Chain (MCMC) to clean the data, remove noise and solve the problem of missing data

8 Nowcasting Slovenian GDP using traffic loops data Nowcasting the quarterly GDP (t+ 45) The nowcasting model consists of 2 stages Principal Component Analysis (PCA) Linear regression is used: GDP is the dependent variable and the chosen PC are the predictors (X1,, Xn). Constant prices of GDP, quarterly, regressand Industry Enterprises' productions as primary Different types of traffic data as secondary All count spots on all roads All count spots on regional roads Goods motor vehicles on all roads Goods motor vehicles on regional roads Different PCA criterion: Percent of explained variability Number of cases/observations per variable Scale of eigenvalues 8

9 Imputation of traffic loops data Decision to not use all of the data: too much missing > untrustworthy imputations! methodological reasons (e.g. double counting) Criteria for use of count spots: At least 85% of data present over all years, No missing years present, Not a speedway junction sensor. The remaining count spots have: 2.4% missing observations in all six years (2.3% reg., 3.1% spw.) Imputing was done separately for every vehicle category in given month. Tested three methods of imputing on all count spots: Spot's donor was the closest suitable neighbour of the given spot. Spot's donors were all suitable neighbours from a set of four closest neighbours to the given spot. Spot's donors were all four of the closest neighbours to the given spot. Also tested an additional method for imputations on speedway spots. Neighbouring densities in the same month with regard to potential missing directions through some neighbours. Due to consistently small errors and robustness in small values the third method was chosen 9

10 Nowcasting GDP and IPI results Traffic data improves the quality of estimates in comparison of solely use of industry data PCA criterion 80 is optimal for our time series The best traffic data is represented by goods vehicles on regional roads data PCA methods together with traffic loops data can produce accurate estimations Open issue: Small number of observations Industry production data represent a big part in the evaluation of GDP, using the traffic data as regressors for Industry Production Index nowcasts. Longer time series: 48 learning points, 24 estimating points (2015, 2016) Optimal PCA criterion 80% 10

11 Nowcasting Finish GDP using traffic loops data Large revisions of short-term indicators - estimate the component which is missing in the data The main predictors in nowcasting application are firm-level sales extracted from the sales inquiry (available t +15) Nowcasting the Trend Indicator of Output (TIO) and quarterly GDP (t+ 16) Importance of using the realistic vintages of data, as the data are constantly "improved" by many internal processes, and by the accumulation of new data. The usage of revised data can arguably lead to too optimistic views on the nowcasting performance of our models and consequently invalidate the results Traffic loop data provide a competitive alternative to firm-level turnovers available at hourly frequency, contains numerous missing values Imputation of missing observations, rely on the regularized principal component technique (R-package missmda ). Use several models to deal with high dimensional econometric problems (Factor models, Linear regularized regressions, Boosting, Tree-based methods, Neural networks, R-package Caret ) Final nowcasts are based on forecast combination (Bayesian averaging of the individual predictions) simple unweighted average of trimmed models work best. 11

12 Nowcasting GDP results The quarterly results confirm the promising performance of traffic data for the production of early estimates of GDP. The t+16 nowcasts track well GDP growth, or at least do not show a substantially different performance compared to the firm-level sales. Fairly precise estimates of GDP growth well before the official publication by Statistics Finland (45 days reduction in the publication lag) Given the potentially real-time availability of traffic volumes measurements, these results indicate the need to further explore the nowcasting ability of models based on these data. 12

13 Traffic data data cleaning strategy at CBS Due to the large number of traffic loops (20,000 on the Dutch highways), another data cleaning strategy had to be developed. Principal component analysis cannot be used due to several reasons. The reason is that the dataset has more columns than rows when looking at daily data. To enhance the speed of the data cleaning process, a Monte Carlo Markov Chain (MCMC) algorithm was used to clean the data, remove noise and solve the problem of missing data An autoencoder is used for the dimension reduction neural network that learns to reconstruct a certain input on the output. 13

14 Estimating/forecasting early economic indicators at ISTAT Access to new data sources: Electronic transactions of the exchange circuits and interbank settlement of the System of Payments Anti-Money Laundering Aggregate Reports (SARA) Two case studies: the nowcasting of the value added of Services and the forecasting of Private household consumption for all goods In-depth analysis and accurate pre-treatment of the outliers due to the changes in the Regulations and laws ruling the sector of electronic transactions. For the evaluation of the new series of payments, reference was made to autoregressive distributed lag models (ADL). Value added in services: new series reduce the mean error statistics of the AR(1) the reduction ranges around 12% relatively to both quarterly and annual growth rates The forecasting model for private consumptions some of the considered series leads to an improvement in terms of MAE both for the forecast at one step and two steps respect to the baseline model 14

15 Nowcasting the ILO unemployment rate at Statistics Poland Nowcast the quarterly ILO unemployment rate, released with a delay of four months: (1) registered unemployment rate based on data from District Labour Offices and (2) job vacancy barometer based on online job offers. Data sources used: Labour Force Survey, Labour Force Survey, Online job offers (scraped) Use a structural time series (STS) model: model 0: without regression component, model 1: job vacancy barometer used as auxiliary variable, model 2: registered unemployment rate used as auxiliary variable. Summary of results The job vacancy index actually worsens the ILO unemployment rate. The register unemployment rate slightly improves the prediction of the ILO unemployment rate. The model without any variables seems to be sufficient but this is mainly because of a clear falling trend in the unemployment rate. 15

16 Predicting macroeconomic variables at Statistics Portugal Assessing early of regional GDP, economic climate, consumer price index, retail sales, etc., using available Big Data sources. Regression and Spatial Panel Data Models are used as the methodological approaches The regional GDP at NUTS 3 as the predicted variable. Volume of exports (INE, 2018), and overnight stays, both collected at NUTS III level as predictors. Use time series with quarterly data from 2007 to 2017, for 23 different regions, where with each time series having 40 time periods SAR (Spatial Autoregressive) is used to predict Quarterly GDP at regional level. Although the correlation between the variables is fairly good in some regions, this is not always the case in every Portuguese regions NUTS. 16

17 Recommendations about the methodology and process of calculating estimates Guidelines for other NSIs that they should consider before they try to experiment with estimations of economic indicators: Skills needed during the process Properties of the data source The pre-treatment process A selection of the regression models Quality assessment of data, models, target variables, benchmarks, etc. Deployment of a testing ground with realistic testing procedures 17

18 Future perspectives NSIs can address a major quality issue, namely the timeliness, to form an initial quick estimate of the target indicator by using a range of micro level data sources accumulated well before the official release is made, by employing large dimensional econometric models, This does not necessarily lead to too large revisions, but adds significantly to the quality of official statistics through timeliness dimension. This work can proceed in multiple directions Other data sources can be explored with the methodologies we have presented and possibly in relation to other indicators Other modeling frameworks are possible - quality measures with focus on precision criteria A real-time application can be programmed, especially relying on the traffic loops data (daily estimates) These methodologies and their added value is not limited to nowcasting the GDP or some aggregate final indicators, but could be explored in order to impute some missing components of the aggregated figures In case of dealing with a large data (traffic loops), a more novel approach for data cleaning and dimension reduction should be considered. By training subsets of data in different neural networks, the dimensionality problem is solved. Traffic loops inspected further by exploring clusters of the measurement points around designated areas (borders, manufacturing clusters, mining fields, etc.) 18