Big Data: New Tricks for Econometrics

By Hal R. Varian. 2014

1,494 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Statistics and Probability, Economics & Operations Research

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Citations in the corpus

Ensemble Methods for Causal Effects in Panel Data Settings (2019, background): “further research in the causal panel data setting. In many prediction problems researchers have found that combinations of prediction methods (“ensembles”) perform better than individual methods (Hal Varian, 2014; James Surowiecki, 2005; Zhi-Hua Zhou, 1983). A simple example is random forests, which”
Generalized random forests (2016, methodology): “One challenge with the empirical moment estimator based on the above is that, if B is small, the variance estimates ĤBLB n (x) may be negative. In our software, we avoid this problem by using a Bayesian analysis of variance following, e.g., Gelman et al. (2014), with an improper uniform prior for ĤH”
Will digital technologies transform agriculture in developing countries (2016, neutral): “More recent papers look at specific aspects of the internet, such as the functioning of internet markets (Ellison and Ellison 2005, Levin 2011) or the internet as an increasingly important source for large data sets that are useful in economic inquiry (Varian 2014).”
Big Data in Agriculture: A Challenge for the Future (2018, methodology): “Parametric approaches include autoregressive or moving average models; non-parametric approaches include covariance or spectrum and usually focus on a smooth spectral density. The Bayesian Structural Time Series (BSTS) model works well for handling the variable selection problem in the case of time ”
Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods (2019, methodology): “We consider several ML models in a data-rich environment with hundreds of variables from the FRED-MD, a monthly database put together by 1See Varian (2014) and Mullainathan & Spiess (2017) for discussions of ML methods and big data in economics.”
Big Data sources and methods for social and economic analyses (2017, methodology): “This new data paradigm is called to transform the landscape for socio-economic policy and research (Einav and Levin, 2014; Varian, 2014) as well as for business management and decision-making.”
Forecasting: theory and practice (2020, background): “Are advances in computing and data availability going to pave the way for a revolution in forecasting? Is big data going to significantly increase the forecast accuracy of macroeconomic forecasts? The last two decades have seen a proliferation of literature on forecasting using big data (Varian, 201”
Why Are There Still So Many Jobs? The History and Future of Workplace (2015, background): “My general observation is that 10 Varian (2014) provides an introduction to machine learning techniques for economists. the tools are inconsistent: uncannily accurate at times; typically only so-so; and occasionally unfathomable.”
Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being (2017, background): “However, simultaneously the Lasso “shrinks” the magnitude of coefficients towards zero, even for those that remain non-zero (Varian, 2014).”
Born to be green: new insights into the economics and management of green entrepreneurship (2017, background): “Notably, the growing use of machine learning methods are likely to complement these efforts and open up new avenues of research (Varian 2014; Székely and Vom Brocke 2017),”
The Impact of Machine Learning on Economics (2018, methodology): “See Varian (2014) for an overview of some of the most popular methods, and Mullainathan and Spiess (2017) for more details.”