IP Library › Granted Patent US 12,299,745
Granted Patent B1
US 12,299,745 · App. 18/113,289 · Granted May 13, 2025

Methods and systems to quantify and index correlation risk in financial markets and risk management contracts thereon

Inventors: Giselle Claudette Guzman (New York, NY); Lawrence Klein (Gladwyne, PA)
Assignee: ECONOMIC ALCHEMY INC.
G06Q40/06G06Q10/04G06Q30/02G06Q40/08
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Quick Facts
Patent No.
US 12,299,745
App. No.
18/113,289
Granted
May 13, 2025
Kind
B1
Abstract

Systems and methods for creating indicators to quantify and index correlation risk that is market-wide among a broad set of asset classes or portfolio specific relative to an investor's portfolio holdings. The present disclosure relates to risk management in financial markets, and in particular to systems and methods for quantifying and indexing correlation risk such that these indices can serve as underlying assets for futures and options or other financial instruments that investors would use to hedge against the risk.

Claims (43)

1. A computer-implemented method for determining a measure of correlation risk of respective liquidity profiles of respective assets from a plurality of assets, the method comprising:

accessing data comprising transaction data corresponding to respective assets from the plurality of assets for a time period;

determining respective liquidity profiles of the respective assets in the plurality of assets based on determining at least one of (i) at least one of a respective liquidity indicator, a respective liquidity measure, and a respective liquidity metric for the respective assets from the plurality of assets, or (ii) at least one of a respective liquidity risk measure and a respective liquidity risk score for the respective assets from the plurality of assets;

performing an analysis of the respective liquidity profile of the respective assets from the plurality of assets to generate the measure of correlation risk between the respective liquidity profiles of at least two of the respective assets over a defined time window in the time period; and

displaying, via a graphical user interface, the measure of correlation risk between the respective liquidity profiles of the at least two of the respective assets over the time window.

2. The method of claim 1 , further comprising:

creating a correlation risk index of the respective liquidity profiles of the at least two of the respective assets from the plurality of assets over a rolling time window by rolling the defined time window by a set number of time periods to identify a rolled time window;

updating the analysis, on an on-going basis, for the rolled time window to find an updated measure of correlation risk between the respective liquidity profiles of the at least two of the respective assets from the plurality of assets over the rolled time window in the time period; and

displaying, via a graphical user interface, the updated measure of correlation risk between the respective liquidity profiles of the at least two of the respective assets from the plurality of assets over the rolling time window.

3. The method of claim 1 , further comprising:

creating a correlation risk index of the respective liquidity profiles of the at least two of the respective assets from the plurality of assets over an expanding time window by expanding the defined time window by a set number of time periods to identify an expanded time window;

updating the analysis, on an on-going basis, for the expanded time window to find an updated measure of correlation risk between the respective liquidity profiles of the at least two of the respective assets from the plurality of assets over the expanded time window in the time period; and

displaying, via a graphical user interface, the updated measure of correlation risk between the respective liquidity profiles of the at least two of the respective assets from the plurality of assets over the expanded time window.

4. The method of claim 1 , wherein the measure of correlation risk is a measure of a co-movement between the liquidity profiles of the at least two of the respective assets, the measure of a co-movement comprising at least one of: a correlation metric, a cointegration metric, a covariance metric, a causality metric, and a dependency metric.

5. The method of claim 4 , wherein when the measure of a co-movement is a correlation metric, the correlation metric comprises at least one of: a simple correlation, a multivariate correlation, a Spearman correlation, a Pearson correlation, a Kendall correlation, a time-varying correlation, a dynamic correlation, an implied correlation, a pair-wise correlation, an historical correlation, a forecasted correlation, and a stochastic correlation.

6. The method of claim 1 , wherein the analysis comprises:

compiling a plurality of respective time series of the respective liquidity profiles of the at least two of the respective assets from the plurality of assets over the defined time window in the time period;

generating a correlation matrix of the respective time series of the respective liquidity profiles of the at least two of the respective assets from the plurality of assets; and

determining, based on the correlation matrix, the measure of correlation risk between respective liquidity profiles of respective pairs of the respective assets from the plurality of assets.

7. The method of claim 6 , wherein the defined time window comprises one of a time interval, a data frequency, and a user-defined time period.

8. The method of claim 6 , wherein the correlation matrix comprises at least one of a standard correlation matrix and a matrix of pairwise correlations.

9. The method of claim 6 , wherein the respective assets comprise one or more of: any asset, an asset grouping, an equity asset, a fixed income asset, a commodity asset, a currency asset, a crypto currency asset, a non-fungible token asset, a cryptographic asset, an Exchange-Traded Fund asset, a derivative asset, a swaps instrument, an option contract, an equity option contract, a futures contract, a forward contract, an exchange traded product, an industry grouping of assets, a sector grouping of assets, a portfolio of assets, a bond, an interest-rate product, a stock, and an asset with a name.

10. The method of claim 1 , wherein the analysis comprises:

compiling respective time series of the respective liquidity profiles of the at least two of the respective assets from the plurality of assets over the defined time window in the time period;

generating a correlation matrix of the respective time series of the respective liquidity profiles for the at least two of the respective assets from the plurality of assets;

determining, based on the correlation matrix, the measure of correlation risk between respective liquidity profiles of respective pairs of the respective assets from the plurality of assets, wherein the determining further comprises determining a set of weights wherein each respective weight corresponds to a respective measure of correlation risk between respective liquidity profiles of respective pairs of respective assets;

applying a respective weight from the set of weights to a corresponding respective measure of correlation risk between respective liquidity profiles of respective pairs of respective assets;

aggregating weighted measures of correlation risk between respective liquidity profiles of respective pairs of respective assets; and

generating a composite measure of correlation risk of liquidity profiles of the respective assets.

11. The method of claim 10 , wherein the set of weights is determined based on at least one of: an arbitrary method; a signal extraction method, and an eigenvalue decomposition of the correlation matrix.

12. The method of claim 1 , further comprising generating a trade recommendation for buying or selling assets based on at least one of: (i) the measure of correlation risk between the respective liquidity profiles of the at least two of the respective assets, and (ii) a forecasted change in the measure of correlation risk between the respective liquidity profiles of the at least two of the respective assets.

13. The method of claim 1 , further comprising generating a ranking based on the measures of correlation risk of the respective liquidity profiles of the respective assets from the plurality of assets.

14. The method of claim 1 , wherein performing an analysis further comprises at least one of determining how the measure of correlation risk of respective liquidity profiles of respective assets from the plurality of assets: (i) trends over time; (ii) varies by groups of assets, (iii) concurrently trends over time and varies by groups of assets; and:

displaying, via a graphical user interface, a result of performing the analysis.

15. A computer-implemented method for determining a measure of correlation between a plurality of first assets and a plurality of second assets, the method comprising:

identifying one or more of the plurality of first assets in the plurality of second assets;

quantifying the measure of correlation between the identified one or more of the plurality of first assets and the plurality of second assets; and

displaying, via a graphical user interface, the measure of correlation between the identified one or more of the plurality of first assets and the plurality of second assets.

16. The method of claim 15 , wherein the plurality of first assets comprises an optimized basket and the plurality of second assets comprises an exchange-traded fund composition.

17. The method of claim 15 , further comprising conducting a plurality of hypothetical trades of the identified one or more of the plurality of first assets based on the measure of correlation with one or more of the plurality of second assets.

18. The method of claim 15 , wherein the measure of correlation is a measure of dependency between the plurality of first assets and the plurality of second assets, the measure of dependency comprising at least one of: a simple correlation, a multivariate correlation, a Spearman correlation, a Pearson correlation, a Kendall correlation, a time-varying correlation, a dynamic correlation, an implied correlation, a pair-wise correlation, an historical correlation, a forecasted correlation, a stochastic correlation, a correlation metric, a cointegration metric, a covariance metric, a co-movement metric, a causality metric, a relatedness metric, a clustering metric, a symmetry metric, a diversification metric, a relationship metric, a similarity metric, and a dependency metric.

19. The method of claim 15 , further comprising providing at least one of: (i) an identification module to identify securities, (ii) benchmarking capabilities, (iii) risk factor analytics, (iv) portfolio performance simulation using a scenario analysis module based on forecasts of at least one of interest rates, credit spreads, economic conditions, quantities, or states, financial conditions, quantities, or states, and asset prices, and (v) a screening and selection module for identifying candidate securities to buy or sell on the basis of the measure of correlation.

20. The method of claim 15 , wherein the plurality of first assets and the plurality of second assets respectively comprise at least one of a currency, a swap, a commodity, a bond, an equity, a futures contract, a forward contract, an option contract, an exchange traded fund, an exchange traded note, a spread, an index, and an asset having a name.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 1, 2025
From: ECONOMIC ALCHEMY INC.
To: GUZMAN, GISELLE C.
Reel/Frame 070694/0937 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 27, 2023
From: GUZMAN, GISELLE C.; KLEIN, LAWRENCE R.
To: ECONOMIC ALCHEMY, LLC.
Reel/Frame 063106/0700 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 27, 2023
From: ECONOMIC ALCHEMY, LLC.
To: ECONOMIC ALCHEMY INC.
Reel/Frame 063106/0756 →
Continuity (4)
Continuation 17355724 · Jun 23, 2021
Continuation 16905542 · Jun 18, 2020
Continuation 13677278 · Nov 14, 2012
Provisional Application 61629227 · Nov 14, 2011
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Mouchart, Michel, et al.; “Clustered Panel Data Models: An Efficient Approach for Nowcasting from Poor Data”; Dec. 4, 2003; 30 pages. [cited by applicant]
Cors, Andreas et al.; “An Approach for Timely Estimations of the German GDP (Abstract)”; AStA Advances in Statistical Analysis, vol. 87, No. 2, 2003; 2 pages. [cited by applicant]
Baffigi, Alberto et al.; “Bridge Models to Forecast the Euro Area GDP (Abstract)”; International Journal of Foecasting, vol. 20, Issue 3, Jul.-Sep. 2004; 3 pages. [cited by applicant]
Mouchart, Michel, et al.; “Clustered Panel Data Models: An Efficient Approach for Nowcasting from Poor Data”; International Journal of Forecasting 21 (2005) 577-594; 2005; 18 pages. [cited by applicant]
Nunes, Luis C.; “Nowcasting Quarterly GDP Growth in a Monthly Coincident Indicator Model (Abstract)”; Journal of Forecasting, vol. 24, Issue 8; Dec. 20, 2005; 4 pages. [cited by applicant]
Altissimo, Filippo et al.; “New Eurocoin: Tracking Economic Growth in Real Time”; Centre for Economic Policy Reseach Discussion Paper Series, Discussion Paper No. 5633, Apr. 2006; 37 pages. [cited by applicant]
Banbura, Marta et al.; “A Look Into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP”; Nov. 2006; 26 pages. [cited by applicant]
Marcellino, Massimiliano et al.; “Factor-MIDAS for Now- and Forcasting with Ragged-Edge Data: A Model Comparision for German GDP”; Bank of England CCBS Research Forum, 2007; 41 pages. [cited by applicant]
Monti, Francesca; “Forecast with Judgement and Models”; National Bank of Belgium, Working Paper Research, No. 153, Dec. 2008; 44 pages. [cited by applicant]
Matheson, Troy et al.; “Nowcasting and Predicting Data Revisions in Real Time Using Qualitative Panel Survey Data”; Reserve Bank of New Zealand, DP2007/02, Jan. 2007; 25 pages. [cited by applicant]
Gelper, Sarah et al.; “The Predictive Power of the European Economic Sentiment Indicator”; Katholieke Universiteit Leuven, Department of Decision Sciences and Information Management, Jan. 22, 2007; 15 pages. [cited by applicant]
Galbraith, John W. et al.; “Electronic Transactions as High-Frequency Indicators of Economic Activity”; Bank of Canada, Working Paper 2007-58, Dec. 2007; 29 pages. [cited by applicant]
Clements, Michael P. et al.; “Macroeconomic Forecasting with Mixed-Frequency Data: Forecasting Output Growth In the United States”; Journal of Nusiness and Economic Statistics, Oct. 2008, 10 pages. [cited by applicant]
Darne, Olivier; “Using Business Survey in Industrial and Services Sector to Nowcast GDP Growth: The French Case”; Economic Bulletin, Jul. 2008; 10 pages. [cited by applicant]
Kholodilin, Konstantin A., et al.; “A New Business Barometer for Germany: Construction and Evaluation of the Nowcast Accuracy”; Nov. 18, 2008; 11 pages. [cited by applicant]
D'Agostino, Antonello, et al.; “Now-Casting Irish GDP”; Central Bank & Financial Servies Authroity of Ireland, Research Technical Paper, 9/RT/08, Nov. 2008; 25 pages. [cited by applicant]
Proietti, Tommaso; “Estimation fo Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components (Abstract)”; MPRA Paper, University Library of Munich, Germany,… [cited by applicant]
Aastveit, Knut Are, et al.; “Estimating the Output Gap in Real-Time: A Factor Model Approach”; Dec. 9, 2008; 10 pages. [cited by applicant]
Lee, Kevin, et al.; “Nowcasting, Business Cycle Dating and the Interpreation of the New Information when Real Time Data are Available”; The University of Melbourne, Department of Economics, Research Paper No. 1040, May … [cited by applicant]
Kuzin, Vladimir, et al.; “Pooling Versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP”; Deutsche Bundesbank Eurosystem, Discussion Paper, Series 1: Economic Studies, No. Mar. 2009, 2… [cited by applicant]
Castle, Jennifer, et al.; “Nowcasting is Not Just Contemporaneous Forecasting”; National Institute Economic Review, 2009; 26 pages. [cited by applicant]
Schorfheide, Frank et al.; “Evaluating DSGE Model Forecasts of Comovements”; University of Pennsylvania, Oct. 17, 2010; 57 pages. [cited by applicant]
Aruoba, A. Boragan, et al.; “Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions”; National Bureau of Economic Research, Working Paper 15657, Jan. 2010; 17 pages. [cited by applicant]
Kholodilin, Konstantin A., et al.; “Do Google Searches Help in Nowcasting Private Consumption? A Real-Time Evidence for the US”; ETH Zurich, Research Collection, Working Paper, Apr. 2010; 29 pages. [cited by applicant]
Gilbert, Thomas, et al.; “Why Do Certain Macroeconomic News Announcements Have a Big Impact on Asset Prices?”; Apr. 6, 2010; 38 pages. [cited by applicant]
Rossiter, James; “Nowcasting the Global Economy”; Bank of Canada Discussion Paper 2010-12, Sep. 2010; 26 pages. [cited by applicant]
Norin, Anna; “Nowcasting of the Gross Regional Product”; 50th Congress of the European Regional Science Association: Sustainable Regional Growth and Development in the Creative Knowledge Economy, Aug. 19-23, 2010; 11 pa… [cited by applicant]
Liebermann, Joelle; “Real-Time Nowcasting of GDP: Factor Model Versus Professional Forecasters”; Munich Personal RePEc Archive, Dec. 2010; 36 pages. [cited by applicant]
Faust, Jon et al.; “Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach”; National Bureau of Economic Research, Working Paper 16725, Jan. 2011; 41 pages. [cited by applicant]
Askitas, Nikolaos et al.; “Nowcasting Business Cycles Using Toll Data”; IZA Discussion Paper No. 5522, Feb. 2011; 19 pages. [cited by applicant]
Lahiri, Kajal et al.; “Nowcasting US GDP: The Role of ISM Business Surveys”; SUNY Department of Economics, Mar. 2011; 30 pages. [cited by applicant]
Sorensen, Jonas; “Indicator Models for Private Consumption”; Monetary Review, 1st Quarter 2011, Part 1; 13 pages. [cited by applicant]
Garratt, Anthony et al.; “Measuring Output Gap Nowcast Uncertainty”; The Australian National University, Centre for Applied Macroeconomic Analysis (CAMA), CAMA Working Paper 16/2011, Jun. 2011; 24 pages. [cited by applicant]
Banbura, Marta et al., “Nowcasting,” Working Papers ECARES 2010-021, Oxford Handbook on Economic Forecasting (2010), 36 pages. [cited by applicant]
Branch, William A.; “Nowcasting and the Taylor Rule”; University of California, Irvine, Jul. 11, 2011; 32 pages. [cited by applicant]
Carnot, Vincent et al.; “Economic Forecasting and Policy”; Second Edition, Chapter 2, Jul. 26, 2011; 8 pages. [cited by applicant]
Guzman, Giselle C.; “Using Sentiment to Predict GDP Growth and Stock Returns”; Preliminary Draft, Munich Personal RePEc Archive, Jun. 29, 2008, 41 pages. [cited by applicant]
Guzman, Giselle C.; “An Inflation Expectations Horserace”; Preliminary Draft, Munich Personal RePEc Archive, Jan. 25, 2010; 44 pages. [cited by applicant]
Guzman, Giselle C.; “The Case for Higher Frequency Inflation Expectations”; Preliminary Draft, Munich Personal RePEc Archive, Jun. 29, 2011; 44 pages. [cited by applicant]
Guzman, Giselle C.; “Internet Search Behavior as an Economic Forecasting Tool: The Case of Inflation Expectations”; Preliminary Draft, Munich Personal RePEc Archive, Nov. 29, 2011; 38 pages. [cited by applicant]
Vosen, Simeon et al.; “A Monthly Consumption Indicator for Germany Based on Internet Search Query Data”; Applied Economic Letters, vol. 19, Iss. 7, 2012; 27 pages. [cited by applicant]
Wieland, Volker et al.; “Macroeconomic Model Comparisons and Forecast Competitions”; Voxeu.org, Feb. 13, 2012; 4 pages. [cited by applicant]
Matteson, David S.; “Time-Frequency Functional Models: An Approach for Identifying and Predicting Economic Recessions in Real-Time”; Cornell University, Department of Statistical Science, May 17, 2014; 42 pages. [cited by applicant]
Molodtsova, Tanya et al.; “Taylor Rule Exchange Rate Forecasting During the Financial Crisis”; National Bureau of Economic Research, Working Paper 18330, Aug. 2012; 41 pages. [cited by applicant]
Scotti, Chiara et al.; “Real-Time Aggregation of Macroeconomic Surprises: A Real Activity Surprise Index”; Federal Reserve Board, Apr. 26, 2012; 24 pages. [cited by applicant]
Campbell, Jeffrey R. et al.; “Macroeconomic Effects of Federal Reserve Forward Guidance”; Working Paper Mar. 2012, Federal Reserve Bank of Chicago, 2012; 61 pages. [cited by applicant]
D'Agostino, Antonello et al.; “Survey-Based Nowcasting of US Growth: A Real-Time Forecast Comparison Over More Than 40 Years”; European Central Bank, Working Paper No. 1455, 2012; 23 pages. [cited by applicant]
Kuzin, Vladimir, et al.; “Pooling Versus Model Selection for Nowcasting GDP with Many Predictors: Empirical Evidence for Six Industrialized Countries”; Deutsche Bundesbank, 2013; 65 pages. [cited by applicant]
Hendry, David et al.; “Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview”; University of Oxford, Department of Economics, Discussion Paper No. 674, Sep. 2013; 74 pages. [cited by applicant]
Koop, Gary et al.; “Macroeconomic Nowcasting Using Google Probabilities”; Aug. 2013; 31 pages. [cited by applicant]
Antenucci, Dolan et al.; “Ringtail: Feature Selection for Easier Nowcasting”; 16th International Workshop on the Web and Databases, Jun. 23, 2013, New York, NY; 6 pages. [cited by applicant]
Giusto, Andrea et al.; “Nowcasting U.S. Business Cycle Turning Points with Vector Quantization”; Dalhousie University, Department of Economics, Sep. 2013; 35 pages. [cited by applicant]
Herrmannova, Lenka; “Forecasting and Nowcasting Power of Confidence Indicators: Evidence for Central Europe”; Charles University in Prague, Instutute of Economic Studies, Rigorous Thesis, Sep. 9, 2013; 140 pages. [cited by applicant]
Picerno, James; “Nowcasting the Business Cycle: A Practical Guide for Spotting Business Cycle Peaks Ahead of the Crowd”; Beta Publishing, 2014; 6 pages. [cited by applicant]
O'Donoghue, Cathal et al.; “Nowcasting in Microsimulation Models: A Methodological Survey”; Journal of Artificial Societies and Social Simulation 17 (4) 12, Oct. 31, 2014, 11 pages. [cited by applicant]
Brave, Scott A. et al.; “Nowcasting Using the Chicago Fed National Activity Index”; Federal Reserve Bank of Chicago, 2014; 107 pages. [cited by applicant]
Higgins, Patrick; “GDP Now: A Model for GDP ‘Nowcasting’”; Working Paper No. 2014-7, Federal Reserve Bank of Atlanta, 2014; 87 pages. [cited by applicant]
Duffy, David et al.; “Quarterly Economic Commentary”; The Economic and Social Research Institute, Oct. 8, 2014; 100 pages. [cited by applicant]
Kourentzes, Nikolaos et al.; “Increasing Knowledge Base for Nowcasting GDP by Quantifying the Sentiment About the State of Economy”; Workshop on Using Big Data for Forecasting and Statistics, Feb. 15, 2014; 16 pages. [cited by applicant]
Kunovac, Davor et al.; “Nowcasting GDP Using Available Monthly Indicators”; Croatian National Bank, Working Papers W-39, Oct. 2014; 28 pages. [cited by applicant]
Massachusetts Institute of Technology; “The Emerging Pitfalls of Nowcasting with Big Data”; Aug. 18, 2014; 6 pages. [cited by applicant]
United Nations; “Handbook on Economic Tendency Surveys”; Statistical Papers, Series M, No. 96; 2015; 253 pages. [cited by applicant]
Caruso, Alberto; “Nowcasting Mexican GDP”; Ecares Working Paper 2015-40; Oct. 2015; 30 pages. [cited by applicant]
Henzel, Steffen et al.; “Nowcasting Regional GDP: The Case of the Free State of Saxony”; CESifo Working Paper, No. 5336; Apr. 2015; 29 pages. [cited by applicant]
Galbraith, John W. et al.; “Nowcasting GDP with Electronic Payments Data”; European Central Bank (ECB); ECB Statistics Paper No. 10; Aug. 2015; 21 pages. [cited by applicant]
Kovacs, Kevin et al.; “Nowcasting German Turning Points Using CUSUM Analysis”; The George Washington University Center of Economic Research, Research Program on Forecasting (RPF); RPF Working Paper No. 2016-014; Dec. 20… [cited by applicant]
Modugno, Michele et al.; “Nowcasting Turkish GDP and News Decomposition”; Finance and Economics Discussion Series 2016-044; May 2016; 40 pages. [cited by applicant]
Abdalla, Ahmed; “The Power of Aggregate Book-to-Market Innovations: Forecasting, Nowcasting, and Dating the Real Economy”; London School of Economics; Jul. 2016; 52 pages. [cited by applicant]
Kim, Hyan Hak et al.; “Methods for Pastcasting, Nowcasting and Forecasting Using Factor-MIDAS”; Aug. 2016; 50 pages. [cited by applicant]
Diebold, Francis X.; “Forecasting in Economics, Business, Finance and Beyond”; University of Pennsylvania, Edition 2017, Aug. 1, 2017; 619 pages. [cited by applicant]
Chernis, Tony et al.; “A Dynamic Factor Model for Nowcasting Canadian GDP Growth”; Bank of Canada Working Paper No. 2017-2, Feb. 2017; 30 pages. [cited by applicant]
Marsilli, Clement; “Nowcasting US Inflation Using a Midas Augmented Phillips Curve”; Int. J. Computational Economics and Econometrics, vol. 7, Nos. 1/2, 2017; 14 pages. [cited by applicant]
Dahlhaus, Tatjana et al.; “Nowcasting BRIC+M in Real Time”; Bank of Canada Working Paper No. 2015-38, Oct. 2015; 45 pages. [cited by applicant]
Antolin-Diaz, Juan et al.; “Advances in Nowcasting Economic Activity”; XIII Annual Conference on Real-Time Data Analysis, Bank of Spain, Oct. 19, 2017; 50 pages. [cited by applicant]
Glaeser, Edward L. et al.; “Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity”; Harvard Business School, Working Paper 18-022, Oct. 2017; 56 pages. [cited by applicant]
Dartmouth College, Tuck School of Business, Elective Courses, 2020; 54 pages. [cited by applicant]
“King's College London PhD Studentships in Economic Measurement: Economic Statistics, Centre of Excellence 2017, UK”; 2017; 6 pages. [cited by applicant]
University of Kansas Economics; Student Conference Presentations 2007-2015; 7 pages. [cited by applicant]
Federal Reserve Bank of New York, Research and Statistic Group, Research Analyst Program; 2018; 12 pages. [cited by applicant]
“Advanced Workshop for Central Bankers”; Centre for International Macroeconomics, Northwestern University, Sep. 6-13, 2016; 6 pages. [cited by applicant]
MIT Center for Digital Business, 2012 Calendar of Events; 2 pages. [cited by applicant]
Columbia University, Quantitative Methods in the Social Sciences (QMSS) Courses, 2012; 7 pages. [cited by applicant]
Midwest Economics Group (MEG) Program, 26th Annual Meeting of the Midwest Economics Group, Department of Economics, University of Illinois at Urbana-Champaign, Oct. 21-22, 2016; 14 pages. [cited by applicant]
European Commission, Collaboration in Research and Methodology for Official Statistics, “Workshop on Using Big Data for Forecasting and Statistics”; Apr. 7-8, 2014; 4 pages. [cited by applicant]
Norges Bank, Central Bank of Norway, “Recent Developments in the Econometrics of Macroeconomics and Finance”; Jun. 2-4, 2010; 2 pages. [cited by applicant]