Using Internet Search Data

Martin Ryan posted recently (here and here) on the potential predictive power of Twitter and the Internet. Liberty Street Economics, a blog on the Federal Reserve Bank of New York's website has a new post looking at the predictive power of Internet search data.

They look at two potential uses of the data: "now-casting", which aims to provide information on current conditions which bypasses the normal lag periods of official economic and financial data, and more traditional forecasting.

For now-casting, they use Google search data to anticipate a weekly index of mortgage refinancing. They find that "the search index increases the R² by about 10 percentage points and is highly statistically significant, which suggests that the search data have information not captured by the model’s other variables". Real-time information on a weekly index may not be particularly useful, however the appendix cites what may be more interesting research:

"Askitas and Zimmermann (2009) show strong correlations between search data and German unemployment. D’Amuri (2009) of the Bank of Italy finds that an Internet-search-based measure is superior to other leading indicators in predicting Italian unemployment. D’Amuri and Marcucci (2009) find that augmenting models of the U.S. unemployment rate with an Internet job-search indicator outperforms traditional forecasting methods and the Survey of Professional Forecasters. Suhoy (2009) of the Bank of Israel finds search data to be a good predictor of labor market conditions in that country."

They find search data less useful when attempting to forecast: "using Internet search data to predict financial market movements is a more fraught exercise. We could not forecast gold prices, European sovereign spreads, interbank rates, and equity market implied volatility with models using search data". However they have better results in markets that may contain less open information, such as renminbi (the Chinese currency) forecasting. This seems an interesting area, however countries which have restricted economic and/or financial information are also more likely to place restrictions on Internet access and usage.

The post and the appendix contain many more references.

On a side note, the post also helps with language skills: 人民, or rénmín, is people's and 币, or bì, is currency. 人民币 = people's currency, or renminbi. Pronunciation is left to the reader.

Unemployment Data and Google: From Forecasts to History Class

A belated thanks to Michael Breen for pointing out a recent article on VoxEU.org about predicting unemployment using Google Trends. The article is by D'Amuri and Marcucci: "The predictive power of Google data: New evidence on US unemployment". I have followed the literature on Google-search data and unemployment ( previously, here), but wasn't aware of the work by D'Amuri and Marcucci until now.

The research mentioned on the blog before (by Varian and Choi) was conducted to predict U.S. unemployment insurance claims using Google Trend data based around keywords such as "unemployment" and "social insurance". D'Amuri and Marcucci differ in their approach by using the "Google Index" – the incidence of Google job-search related queries over total queries – proved to have predictive power in forecasting unemployment in Germany and Israel (see Askitas and Zimmermann 2009 and Suhoy 2009).

Both approaches improve the predictive power of unemployment forecasting, but each has some limitation. D'Amuri and Marcucci mention that the Google Index could be partly driven by on-the-job search, rather than unemployed job search activities. It can be argued that this is only a problem to the extent that on-the-job search happens during a recession. D'Amuri and Marcucci also consider that not everyone has access to the internet, and therefore that people using the internet for job search are not randomly selected among job-seekers. It follows that people using the internet for unemployment benefit information are not randomly selected among the newly unemployed. These are illustrations of the sample selection problem in econometric analysis (as distinct from self-selection; Heckman provides an overview here).

So while Google Trends is a useful tool in unemployment (and other) predictions, there are reasons to be cautious; in particular, in relation to sample selection. Another limitation that has been remarked upon is that Google Trends only provides data from 2004 onwards. This is why I was intrigued when I typed "unemployment" into Google earlier today, investiagted the "options" at the top of the page, and then clicked "timeline" instead of "standard view". What I got was a picture along the lines of the one below, except that the chart was for unemployment from 1900-2010, instead of "Book of Revelation". I was unable to take a screen-grab of the unemployment chart; but you can follow a link to it here.


It's possible to click on any decade in the chart, any year and any month. Associated news stories appear in each of these categories. This is a powerful tool for finding out what was beeing reported in the media at the time any major news story was being covered. All of this is powered by Google News Timeline: a web application that organises information chronologically. "It allows users to view news and other data sources on a zoomable, graphical timeline. You can navigate through time by dragging the timeline, setting the "granularity" to weeks, months, years, or decades, or just including a time period in your query." However, using the News Timeline application directly is somewhat different to using the "timeline view" in web search. The latter provides charts such as the one shown above.

The unemployment chart shows two spikes: one at the start of the 1930's, and one in 2009. But what does this mean? According to the Google Blog, "the graph across the top of the page summarizes how dates in your results are spread through time, with higher bars representing a larger number of unique dates." Where does this historical data come from? From Google's "News Archive Search" service. News Archive Search produces the same results as the "timeline view" in web search. Search results include content from a number of sources, including both partner content digitized by Google through their News Archive Partner Program and online archival materials. More information about News Archive Search is available here.

There are some parallels to be drawn between News Archive Search (particularly the associated graphical illustrations) and the "news reference volume" feature in Google Trends. However, it is important to note that Google's "timeline" news graphs are based on monthly data-points; not daily data-points such as those used in (Trends) news reference volume. According to Google, Archive Search works as follows: "Articles related to a single story within a given time period are grouped together to allow users to see a broad perspective on the topics they are searching."

The Employment Outlook in Ireland

In today's Business World, it is reported that "Irish employers remain gloomy about the prospects for hiring staff in early 2010 with 17pc expecting to cut payroll numbers in the first three months of the New Year... Employers also report their sixth consecutive quarter of negative hiring activity." This is according to a survey from Manpower.

The Manpower Employment Outlook Survey examines anticipated hiring activities across 35 countries and territories. The current survey in this country measures the intentions of 620 Irish employers to increase or decrease their workforce over the next three months. The report is available here.

The joy of macroeconomics

Apropos of nothing here is a great quote from Willem Buiter (& not just for the allusion to Winnie the Pooh):
"Never mind that anyone providing point forecasts of anything without also offering at least some information about of the rest of the probability distribution of future outcomes (variance, skewness, kurtosis, single-peakedness etc) is either a fool, a knave (or both) or caters to an audience consisting of bears of very little brain."
I wonder is there any chance that some of our local macroeconomic experts ( & the hacks who lap up these forecasts) like to take this on board?

http://blogs.ft.com/maverecon/2009/10/another-quarter-of-negative-gdp-growth-in-the-uk-situation-hopeless-but-not-serious/