In this paper we show how to use search engine data to forecast near-term values of economic indicators. Examples include automobile sales, unemployment claims, travel destination planning and consumer confidence.

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Predicting the Present with Google Trends

Can Google queries help predict economic activity?The answer depends on what you mean by "predict." Google Trends and Google Insights for Search provide a real time report on query volume, while economic data is typically released several days after the close of the month. Given this time lag, it is not implausible that Google queries in a category like "Automotive/Vehicle Shopping" during the first few weeks of March may help predict what actual March automotive sales will be like when the official data is released halfway through April.That famous economist Yogi Berra once said "It's tough to make predictions, especially about the future." This inspired our approach: let us lower the bar and just try to predict the present. Our work to date is summarized in a paper called Predicting the Present with Google Trends. We find that Google Trends data can help improve forecasts of the current level of activity for a number of different economic time series, including automobile sales, home sales, retail sales, and travel behavior. Even predicting the present is useful, since it may help identify "turning points" in economic time series. If people start doing significantly more searches for "Real Estate Agents" in a certain location, it is tempting to think that house sales might increase in that area in the near future.Our paper outlines one approach to short-term economic prediction, but we expect that there are several other interesting ideas out there. So we suggest that forecasting wannabes download some Google Trends data and try to relate it to other economic time series. If you find an interesting pattern, post your findings on a website and send a link to econ-forecast@google.com. We'll report on the most interesting results in a later blog post.It has been said that if you put a million monkeys in front of a million computers, you would eventually produce an accurate economic forecast. Let's see how well that theory works.

This paper combines income tax returns with Flow of Funds data to estimate the distribution of household wealth in the United States since 1913. We estimate wealth by capitalizing the incomes reported by individual taxpayers, accounting for assets that do not generate taxable income. We successfully test our capitalization method in three micro datasets where we can observe both income and wealth: the Survey of Consumer Finance, linked estate and income tax returns, and foundations’ tax records. Wealth concentration has followed a U-shaped evolution over the last 100 years: It was high in the beginning of the twentieth century, fell from 1929 to 1978, and has continuously increased since then. The rise of wealth inequality is almost entirely due to the rise of the top 0.1% wealth share, from 7% in 1979 to 22% in 2012|a level almost as high as in 1929. The bottom 90% wealth share rst increased up to the mid-1980s and then steadily declined. The increase in wealth concentration is due to the surge of top incomes combined with an increase in saving rate inequality. Top wealth-holders are younger today than in the 1960s and earn a higher fraction of total labor income in the economy. We explain how our ndings can be reconciled with Survey of Consumer Finances and estate tax data.

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Wealth Inequality in the United States since 1913: Evidence from Capitalized Income Tax Data

Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by econom...

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Big Data: New Tricks for Econometrics

Although the Paris Agreement's goals (1) are aligned with science (2) and can, in principle, be technically and economically achieved (3), alarming inconsistencies remain between science-based targets and national commitments. Despite progress during the 2016 Marrakech climate negotiations, long-term goals can be trumped by political short-termism. Following the Agreement, which became international law earlier than expected, several countries published mid-century decarbonization strategies, with more due soon. Model-based decarbonization assessments (4) and scenarios often struggle to capture transformative change and the dynamics associated with it: disruption, innovation, and nonlinear change in human behavior. For example, in just 2 years, China's coal use swung from 3.7% growth in 2013 to a decline of 3.7% in 2015 (5). To harness these dynamics and to calibrate for short-term realpolitik, we propose framing the decarbonization challenge in terms of a global decadal roadmap based on a simple heuristic—a “carbon law”—of halving gross anthropogenic carbon-dioxide (CO2) emissions every decade. Complemented by immediately instigated, scalable carbon removal and efforts to ramp down land-use CO2 emissions, this can lead to net-zero emissions around mid-century, a path necessary to limit warming to well below 2°C.

/pdf/a-roadmap-for-rapid-decarbonization-t1fz721n8v.pdf

A roadmap for rapid decarbonization

On 19 December 2014, the Centre for Economic Policy Research and the Bank of England hosted a discussion forum based around Thomas Piketty’s book, Capital in the twenty-first century, with a number of economists from academia, public sector bodies and private sector institutions. Four speakers presented research on various issues relating to inequality, including: access to education; wealth and taxation policy; and the role of governance and institutions. This article presents each speaker’s key arguments, and includes a summary of the open-floor debate that followed.

Capital in the 21st Century

Data on the volume of online searches can be used as indicators of economic activity. This article examines the use of these data for labour and housing markets in the United Kingdom. These data provide some additional information relative to existing surveys. And with further development, internet search data could become an important tool for economic analysis.

https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=1865276

Rachana Shanbhogue

Papers

Capital in the 21st Century

Using Internet Search Data as Economic Indicators

Using internet search data as economic indicators