Note from dshort: This commentary has been revised to include March Real Retail Sales (adjusted with yesterday's CPI report) and today's release of March Industrial Production. Official recession calls are the responsibility of the NBER Business Cycle Dating Committee, which is understandably vague about the specific indicators on which they base their decisions. This committee statement is about as close as they get to identifying their method. There is, however, a general belief that there are four big indicators that the committee weighs heavily in their cycle identification process. They are: With yesterday's release of CPI data for March, we can now calculate Real Retail Sales. As the adjacent chart shows, this indicator has recovered from its December-January slump and has now hit a record high. The real series was up 0.94% in March and 2.21% year-over-year. As we can see in the YoY chart in the appendix below, the downward trend since early 2011 hit a trough (so far) in January, but the increases in February and March are perhaps signaling a trend reversal. Time will tell. The latest Industrial Production data includes the Fed's extensive annual revisions published at the end of last month. The March month-over-month increase of 0.7% beat the Investing.com forecast of 0.5%, and the February MoM was revised upward from 0.6% to a whopping 1.2%. The Fed report explains: "The rise in February was higher than previously reported primarily because of stronger gains for durable goods manufacturing and for mining. For the first quarter as a whole, industrial production moved up at an annual rate of 4.4 percent, just slightly slower than in the fourth quarter of 2013." The chart and table below illustrate the performance of the Big Four with an overlay of a simple average of the four since the end of the Great Recession. The data points show the cumulative percent change from a zero starting point for June 2009. We now have three of the four indicator updates for the 57th month following the recession. With one data point left for March, the Big Four Average (gray line below) is showing the two strongest advances of the past twelve months. The overall picture of the US economy had been one of a ploddingly slow recovery from the Great Recession, and the data for December and January months documented a sharp contraction contraction. The recovery over the past two months appears to support the general view that severe winter weather was responsible for the contraction, and that we shouldn't read the slippage as the beginnings of a business cycle decline. At this point it looks like the Winter slump has been reversed. The next update of the Big Four will be the March Personal Income less Transfer Payments, which will be available on May 1st. The charts above don't show us the individual behavior of the Big Four leading up to the 2007 recession. To achieve that goal, I've plotted the same data using a "percent off high" technique. In other words, I show successive new highs as zero and the cumulative percent declines of months that aren't new highs. The advantage of this approach is that it helps us visualize declines more clearly and to compare the depth of declines for each indicator and across time (e.g., the short 2001 recession versus the Great Recession). Here is my own four-pack showing the indicators with this technique. Now let's examine the behavior of these indicators across time. The first chart below graphs the period from 2000 to the present, thereby showing us the behavior of the four indicators before and after the two most recent recessions. Rather than having four separate charts, I've created an overlay to help us evaluate the relative behavior of the indicators at the cycle peaks and troughs. (See my note below on recession boundaries). The chart above is an excellent starting point for evaluating the relevance of the four indicators in the context of two very different recessions. In both cases, the bounce in Industrial Production matches the NBER trough while Employment and Personal Incomes lagged in their respective reversals. As for the start of these two 21st century recessions, the indicator declines are less uniform in their behavior. We can see, however, that Employment and Personal Income were laggards in the declines. Now let's look at the 1972-1985 period, which included three recessions -- the savage 16-month Oil Embargo recession of 1973-1975 and the double dip of 1980 and 1981-1982 (6-months and 16-months, respectively). And finally, for sharp-eyed readers who can don't mind squinting at a lot of data, here's a cluttered chart from 1959 to the present. That is the earliest date for which all four indicators are available. The main lesson of this chart is the diverse patterns and volatility across time for these indicators. For example, retail sales and industrial production are far more volatile than employment and income. History tells us the brief periods of contraction are not uncommon, as we can see in this big picture since 1959, the same chart as the one above, but showing the average of the four rather than the individual indicators. The chart clearly illustrates the savagery of the last recession. It was much deeper than the closest contender in this timeframe, the 1973-1975 Oil Embargo recession. While we've yet to set new highs, the trend has collectively been upward, although we have that strange anomaly caused by the late 2012 tax-planning strategy that impacted the Personal Income. Here is a close-up of the average since 2000. Each of the four major indicators discussed in this article are illustrated below in three different data manipulations: The US Industrial Production Index (INDPRO) is the oldest of the four indicators, stretching back to 1919. The log scale of the first chart is particularly useful in showing the correlation between this indicator and early 20th century recessions. This data series is computed as by taking Personal Income (PI) less Personal Current Transfer Receipts (PCTR) and deflated using the Personal Consumption Expenditure Price Index (PCEPI). I've chained the data to the latest price index value. The "Tax Planning Strategies" annotation refers to shifting income into the current year to avoid a real or expected tax increase. For a visual sense of the relative size of Personal Income and Transfer Receipts (Social Security, etc.), here is stacked area chart of nominal values. Many people assume that Transfer Receipts (Payments) are a larger source of income than they really are. There are many ways to plot employment. The one referenced by the Federal Reserve researchers as one of the NBER indicators is Total Nonfarm Employees (PAYEMS). This indicator is a splicing of the discontinued retail sales series (RETAIL, discontinued in April 2001) with the Retail and Food Services Sales (RSAFS) and deflated by the seasonally adjusted Consumer Price Index (CPIAUCSL). I used a splice point of January 1995 because that date was mentioned in the FRED notes. My experiments with other splice techniques (e.g., 1992, 2001 or using an average of the overlapping years) didn't make a meaningful difference in the behavior of the indicator in proximity to recessions. I've chained the data to the latest CPI value. Note: I represent recessions as the peak month through the month preceding the trough to highlight the recessions in the charts above. For example, the NBER dates the last cycle peak as December 2007, the trough as June 2009 and the duration as 18 months. The "Peak through the Period preceding the Trough" series is the one FRED uses in its monthly charts, as explained in the FRED FAQs illustrated in this Industrial Production chart. FREE AppDownload
SAUSALITO, Calif. These days, Twitter cofounder Biz Stone is focused on his new startup, Jelly Industries. Jelly describes itself on its website as a loose network of
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NER:构建命名实体识别器,以从Business Insider的数据中提取CEO的姓名,公司的名称和百分比
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内尔 该项目采用了2013-2014年的Business Insider文章,并从数据中提取了所有CEO,公司的名称和百分比。 对于这三个目标中的每一个,都建立了基于逻辑回归的命名实体识别器(NER)。 由此产生的结果可用于帮助构建问题解答(QA)系统。
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NER:构建命名实体识别器,以从Business Insider的数据中提取CEO的姓名,公司的名称和百分比 (744个子文件)
ceo_names 19KB
company_names 17KB
companies.csv 69KB
percentage.csv 40KB
ceo.csv 35KB
Named_Entity_Recognition_Business_Insider.docx 25KB
Executive Summary.docx 12KB
.gitignore 30B
sample-output.html 356KB
Business-Insider-Text-Analytics.ipynb 29KB
README.md 324B
Named_Entity_Recognition_Business_Insider.pdf 127KB
Executive Summary.pdf 56KB
percentages 214KB
2014-04-16.txt 335KB
2014-08-20.txt 322KB
2014-10-15.txt 304KB
2014-09-03.txt 290KB
2014-07-16.txt 286KB
2013-01-16.txt 285KB
2014-03-05.txt 267KB
2014-12-03.txt 253KB
2013-07-17.txt 241KB
2014-03-28.txt 235KB
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2014-02-19.txt 233KB
2013-03-06.txt 228KB
2013-12-04.txt 228KB
2014-09-22.txt 226KB
2014-10-09.txt 225KB
2014-09-26.txt 225KB
2014-09-17.txt 225KB
2014-10-13.txt 224KB
2014-10-02.txt 223KB
2014-09-30.txt 223KB
2013-06-05.txt 221KB
2013-02-20.txt 221KB
2013-11-19.txt 219KB
2014-01-15.txt 218KB
2014-07-30.txt 218KB
2013-04-17.txt 216KB
2013-02-07.txt 215KB
2014-09-29.txt 214KB
2014-01-08.txt 213KB
2013-01-23.txt 212KB
2014-08-13.txt 212KB
2013-04-16.txt 212KB
2014-02-05.txt 211KB
2013-10-16.txt 209KB
2013-11-06.txt 206KB
2014-09-10.txt 205KB
2014-12-02.txt 204KB
2014-11-04.txt 204KB
2014-12-16.txt 203KB
2014-08-07.txt 201KB
2014-01-17.txt 199KB
2014-11-03.txt 196KB
2013-02-12.txt 196KB
2013-01-17.txt 195KB
2014-03-31.txt 193KB
2014-10-08.txt 192KB
2014-07-28.txt 192KB
2014-10-22.txt 191KB
2014-09-16.txt 190KB
2013-01-22.txt 189KB
2014-11-20.txt 187KB
2013-10-09.txt 187KB
2014-07-14.txt 187KB
2014-11-05.txt 187KB
2014-03-19.txt 187KB
2014-01-06.txt 186KB
2014-01-24.txt 186KB
2014-02-25.txt 185KB
2013-12-02.txt 184KB
2014-08-26.txt 184KB
2014-10-07.txt 183KB
2014-12-17.txt 183KB
2014-02-04.txt 183KB
2014-12-01.txt 183KB
2013-01-02.txt 182KB
2013-11-11.txt 182KB
2014-12-18.txt 180KB
2014-01-13.txt 180KB
2014-07-29.txt 180KB
2014-01-22.txt 180KB
2013-01-25.txt 180KB
2014-10-05.txt 179KB
2014-02-24.txt 179KB
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2014-10-27.txt 178KB
2014-11-17.txt 177KB
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2014-07-15.txt 177KB
2014-10-24.txt 176KB
2014-01-23.txt 176KB
2014-08-22.txt 176KB
2014-10-29.txt 175KB
2014-08-08.txt 175KB
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