Civil Rights
Movements, leaders, victories and the continuing fight for equality.
Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
In August 1908, a white mob attacked Springfield, Illinois’s Black community, destroying homes and businesses and lynching two Black men. National outrage over the violence helped spur the movement that created the NAACP the following year.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
Sir Clive Granger | |
|---|---|
Granger in 2008 | |
| Born | 4 September 1934 Swansea, Wales, U.K. |
| Died | 27 May 2009 (aged 74) San Diego, California, U.S. |
| Academic background | |
| Education | University of Nottingham |
| Harry Pitt | |
| Influences | David Hendry Norbert Wiener John Denis Sargan Alok Bhargava |
| Academic work | |
| Discipline | Financial economics Econometrics |
| Institutions | Erasmus University Rotterdam University of California, San Diego University of Nottingham |
Doctoral students | |
Notable ideas | Cointegration Granger causality Autoregressive fractionally integrated moving average |
| Awards | Nobel Memorial Prize in Economic Sciences (2003) |
| Website | |
Sir Clive William John Granger (/ˈɡreɪndʒər/; 4 September 1934 – 27 May 2009) was a British econometrician known for his contributions to nonlinear time series analysis.[1] He taught in Britain, at the University of Nottingham and in the United States, at the University of California, San Diego. Granger was awarded the Nobel Memorial Prize in Economic Sciences in 2003 in recognition of the contributions that he and his co-winner, Robert F. Engle, had made to the analysis of time series data. This work fundamentally changed the way in which economists analyse financial and macroeconomic data.[2]
Clive Granger was born in 1934 in Swansea, south Wales, United Kingdom, to Edward John Granger and Evelyn Granger.[3] The next year his parents moved to Lincoln.
During World War II Granger and his mother moved to Cambridge because Edward joined the Royal Air Force and deployed to North Africa. Here they stayed first with Evelyn's mother, then later Edward's parents, while Clive began school. Clive would later recall a primary school teacher telling his mother that "[Clive] would never be successful".[4]
Clive started secondary school in Cambridge, but continued in Nottingham, where his family moved after the war. Here two teachers encouraged Granger's interest in physics and applied mathematics.[5] He had anticipated following the convention of completing schooling at age 16 to enter the workforce and saw himself working in a bank or insurance company. However, positive social influence from his peers and support from his father led him to enroll in sixth-form for two years as preparation for a university degree.[4]
Granger enrolled in a joint degree in economics and mathematics at the University of Nottingham but switched to full mathematics in his second year. After receiving his BA in 1955, he remained at the University of Nottingham for a PhD in statistics under the supervision of Harry Pitt.[4]
In 1956, aged 21, Granger was appointed a junior lecturer in statistics at the university. His interest in applied statistics and economics led him to choose as the topic of his doctoral thesis time series analysis, a field in which he felt that relatively little work had been done at the time.[3] In 1959 Granger completed his PhD degree with a thesis titled "Testing for Non-stationarity".
Granger spent the next academic year, 1959–60, at Princeton University under a Harkness Fellowship of the Commonwealth Fund. He had been invited to Princeton by Oskar Morgenstern to participate in his Econometrics Research Project. Here, Granger and Michio Hatanaka as assistants to John Tukey on a project using Fourier analysis on economic data.
In 1964, Granger and Hatanaka published the results of their research in a book on Spectral Analysis of Economic Time Series (Tukey had encouraged them to write this themselves, as he was not going to publish the research results.)[3] In 1963, Granger also wrote an article on "The typical spectral shape of an economic variable", which appeared in Econometrica in 1966. Both the book and the article proved influential in the adoption of the new methods.
Granger also became a full professor at the University of Nottingham.
In a 1969 paper in Econometrica, Granger also introduced his concept of Granger causality.
After reading a pre-print copy of the time series book by George Box and Gwilym Jenkins in 1968,[6] Granger became interested in forecasting. For the next few years he worked on this subject with his post-doctoral student, Paul Newbold; and they wrote a book which became a standard reference in time series forecasting (published in 1977). Using simulations, Granger and Newbold also wrote the famous 1974 paper on spurious regression which led to a re-evaluation of previous empirical work in economics and to the econometric methodology.[7]
Granger spent 22 years at the University of Nottingham. In 2005, the building that houses the Economics and Geography Departments was renamed the Sir Clive Granger Building in honor of his Nobel prize award.
In 1974 Granger moved to the University of California at San Diego. In 1975 he participated in a US Bureau of Census committee, chaired by Arnold Zellner, on seasonal adjustment. At UCSD, Granger continued his research on time series, collaborating closely with Nobel prize co-recipient Robert Engle (whom he helped bring to UCSD), Roselyne Joyeux (on fractional integration), Timo Teräsvirta (on nonlinear time series) and others. Working with Robert Engle, he developed the concept of cointegration, introduced in a 1987 joint paper in Econometrica;[8] for which he was awarded the Nobel prize in 2003.
Granger also supervised many PhD students, including Mark Watson (co-advisor with Robert Engle).[9]
In later years Granger also used time series methods to analyse data outside economics. He worked on a project forecasting deforestation in the Amazon rainforest.[10] In 2003, Granger retired from UCSD as a professor emeritus. He was a Visiting Eminent Scholar of the University of Melbourne and the University of Canterbury. He was a supporter of the Campaign for the Establishment of a United Nations Parliamentary Assembly, an organisation which campaigns for democratic reform of the United Nations.[11]
Granger was married to Patricia (Lady Granger) from 1960 until his death. He was survived by their son, Mark William John, and their daughter, Claire Amanda Jane.[3]
Granger died on 27 May 2009, at Scripps Memorial Hospital in La Jolla, California.[12]
In 2003, Granger and his collaborator Robert Engle were jointly awarded the Nobel Memorial Prize in Economic Sciences. He was made a Knight Bachelor in the New Year's Honours in 2005.[13]
Granger was a fellow of the Econometric Society since 1972 and a Corresponding Fellow of the British Academy since 2002. In 2004, he was voted as one of the 100 Welsh Heroes.[citation needed]
{{cite book}}: ISBN / Date incompatibility (help)Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
Sir Clive William John Granger (; 4 September 1934 – 27 May 2009) was a British econometrician known for his contributions to nonlinear time series analysis. He taught in Britain, at the University of Nottingham and in the United States, at the University of California, San Diego. Granger was awarded the Nobel Memorial Prize in Economic Sciences in 2003 in recognition of the contributions that he and his co-winner, Robert F. Engle, had made to the analysis of time series data. This work fundamentally changed the way in which economists analyse financial and macroeconomic data.
The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another, first proposed in 1969. Ordinarily, regressions reflect "mere" correlations, but Clive Granger argued that causality in economics could be tested for by measuring the ability to predict the future values of a time series using prior values of another time series. Since the question of "true causality" is deeply philosophical, and because of the post hoc ergo propter hoc fallacy of assuming that one thing preceding another can be used as a proof of causation, econometricians assert that the Granger test finds only "predictive causality". Using the term "causality" alone is a misnomer, as Granger-causality is better described as "precedence", or, as Granger himself later claimed in 1977, "temporally related". Rather than testing whether X causes Y, the Granger causality tests whether X forecasts Y. A time series X is said to Granger-cause Y if it can be shown, usually through a series of t-tests and F-tests on lagged values of X (and with lagged values of Y also included), that those X values provide statistically significant information about future values of Y. Granger also stressed that some studies using "Granger causality" testing in areas outside economics reached "ridiculous" conclusions. "Of course, many ridiculous papers appeared", he said in his Nobel lecture. However, it remains a popular method for causality analysis in time series due to its computational simplicity. The original definition of Granger causality does not account for latent confounding effects and does not capture instantaneous and non-linear causal relationships, though several extensions have been proposed to address these issues.
The Gabor–Granger method is a method to determine the price for a new product or service. It was developed in the 1960s by Clive Granger and André Gabor. It is a variant of monadic price testing. To use the Gabor-Granger method in a survey, one must find the highest price that respondents are willing to pay. There are many ways to do this but the most common is done by choosing 5 price points for the survey and then asking the respondent a 5-point purchase intent question for a random price from those 5 established price points. If the respondent answers in the top 2 choices - 'Definitely Buy' or 'Probably Buy' for this question, they are then asked the same question for a random price that is higher than was just asked. If it is not in the top 2 then the respondent is asked the same question for a random lower price. This is done until you find the highest price the respondent is in top 2 on Purchase Intent Scale. If they are not in top 2 for the lowest of the 5 prices, the respondent is usually coded as a zero or deleted from the analysis. For example, say the 5 prices chosen are $1, $2, $3, $4 and $5. A first random chosen price might be $4. If the respondent is in top 2 on purchase intent, they would then be asked if their intent to purchase at $5, the next highest price. If they are in top 2 on $5 they are coded as $5 since they signal high intent to pay the maximum price. If they are not in top 2 on $5, they are coded as $4 as this was the highest price they signaled a high intent to pay. If the respondent is not top 2 intent categories for $4, then they are asked a random lower price. This procedure continues until you have found the highest price the respondent is willing to pay among the price points. This is your Gabor-Granger variable. Once you have this Gabor-Granger variable, the results can be used to produce a demand chart (where x-axis are the prices and y axis the percentage of people willing to pay that price) and a revenue curve (where y-axis is the predicted revenue and x-axis is still price).
Robert Fry Engle III (born November 10, 1942) is an American economist and statistician. He was awarded the 2003 Nobel Memorial Prize in Economic Sciences, sharing the award with Clive Granger, "for methods of analyzing economic time series with time-varying volatility (ARCH)".
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Madam C.J. Walker