1. Big Data Econometrics: Nowcasting and Early Estimates
ESTAT Project (01/2017 – 01/2018)
Implemented by GOPA Consultants;
Technical Expertise Provided by Massimiliano Marcellino (Bocconi University), George Kapetanios (King’s College), Fabio Bacchini (ISTAT), Fotis Papailias (King’s College), Katerina Petrova (University of St. Andrews )

2. Why interested in Big Data for nowcasting?
Big Data can provide relevant complementary information to standard data, being based on different information sets
More granular perspective on the indicator of interest, both in the temporal and cross-sectional dimensions
It is timely available, generally not subject to revisions

3. Presentation Outline Big Data Types with Macro Nowcasting Potential
Feature Extraction: from Big Data to Time Series
Filtering techniques
Examples by Type
Reuters-based (Big Data) Uncertainty Indexes
Google-Based Uncertainty Indexes
Econometric Methodologies for Big Data
Next Steps

4. Big Data Types with Macro Nowcasting Potential Overview
Financial markets data
Scanner prices data
Electronic payments data
Online prices data
Mobile phones data
Online search data
Sensor data
Textual data
Satellite images data
Social media data

5. Nowcasting Specific Macroeconomic Variables Using Big Data
Unemployment
GDP and components
Inflation
Surveys
Financial variables

6. Types of Big Data by Dominant Dimension
Fat
Tall
Huge
Financial Markets X
Electronic Payments
Mobile Phones
Sensor Data / IoT
Satellite Images
Scanner Prices
Online Prices
Online Search
Fat (big cross-sectional dimension, N, small temporal dimension, T)
Tall (small N, big T)
Huge (big N, big T)

7. Uncertainty Indexes based on Big Data
Feature Extraction: from Big Data to Time Series for Econometric Modelling
General Data Conversion Framework for Un- structured Numerical Big Data
Empirical Examples
Uncertainty Indexes based on Big Data
In general, the majority of the examples indicate that linear aggregation seems a suitable choice in numerical exercises, although this largely depends on the conceptual setting of the nowcasting exercise too.

8. Feature Extraction of Big Data to Time Series for Econometric Modelling
Outliers?

9. Filtering techniques for high-frequency data
Outliers
Seasonal Patterns and Signal Extraction
From Signal Extraction to Uncertainty Indexes
STL filtering approach based on local linear regression, loess method (“Seasonal and Trend decomposition using Loess“) seems particularly suited – computationally very fast and can also handle high frequencies.

11. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Huge dimension? Subsampling in order to reduce the dimensionality problem

12. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Financial Markets Data

13. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Electronic Payments Data

14. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Electronic Payments Data

15. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Mobile Phones Data

16. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Sensor Data and IoT

19. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Satellite Images Data

20. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Online Prices Data

21. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Online Data

22. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Textual: Reuters-based Uncertainty Indexes
Use Reuters online database (big data) to build uncertainty indexes based on keywords

23. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Textual: Reuters-based Uncertainty Indexes

24. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Textual: Reuters-based Uncertainty Indexes

25. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Textual: Reuters-based Uncertainty Indexes

26. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Textual: Reuters-based Uncertainty Indexes

29. Feature Extraction: from Big Data to Time Series for Econometric Modelling
Online Search: Google-Based Uncertainty Indexes

32. Feature Extraction: from Big Data to Time Series for Econometric Modelling
In general, the majority of the examples indicate that linear aggregation seems a suitable choice in numerical exercises, although this largely depends on the conceptual setting of the nowcasting exercise too.

33. Econometric Methodologies for Big Data with Potential for Nowcasting
Penalised Regressions
Ridge
Lasso
Elastic Net
Spike and Slab Regressions
Compressed Regressions

34. Econometric Methodologies for Big Data with Potential for Nowcasting
Bayesian VARs
Reduced Rank VARs
Bayesian Reduced Rank VAR
Time Varying Parameter VAR
Stochastic Volatility VAR
Nonparametric time varying
Quantile and Expectile Regressions

35. Econometric Methodologies for Big Data with Potential for Nowcasting
Regression Trees
Boosting
Cluster Analysis
Random Forests
Neural Networks / Deep Learning

36. Econometric Methodologies for Big Data with Potential for Nowcasting
Factors
PCA
Sparse PCA
PLS
Forecast Combination
Bayesian Model Averaging
Frequentist Model Averaging
Mixed-Frequency Models

37. So Far…
Encouraging evidence for the use of Big Data for nowcasting and flash estimation
Official Statistical Agencies should consider Big Data
Improved flash estimates and possibly refinements of other statistics

38. Next Steps… Empirical test on possible timeliness gains
New metrics for official statistics using econometric techniques
ESTAT Big data handling tool