Econometrica is a peer-reviewed academic journal of economics, publishing articles in many areas of economics, especially econometrics. It is published by Wiley-Blackwell on behalf of the Econometric Society. The current editor-in-chief is Guido Imbens.
Sir Clive William John Granger 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.
In econometrics, the autoregressive conditional heteroskedasticity (ARCH) model is a statistical model for time series data that describes the variance of the current error term or innovation as a function of the actual sizes of the previous time periods' error terms; often the variance is related to the squares of the previous innovations. The ARCH model is appropriate when the error variance in a time series follows an autoregressive (AR) model; if an autoregressive moving average (ARMA) model is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH) model.
In finance, volatility clustering refers to the observation, first noted by Mandelbrot (1963), that "large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes." A quantitative manifestation of this fact is that, while returns themselves are uncorrelated, absolute returns or their squares display a positive, significant and slowly decaying autocorrelation function: corr(|rt|, |rt+τ |) > 0 for τ ranging from a few minutes to several weeks. This empirical property has been documented in the 90's by Granger and Ding (1993) and Ding and Granger (1996) among others; see also. Some studies point further to long-range dependence in volatility time series, see Ding, Granger and Engle (1993) and Barndorff-Nielsen and Shephard.
Financial econometrics is the application of statistical methods to financial market data. Financial econometrics is a branch of financial economics, in the field of economics. Areas of study include capital markets, financial institutions, corporate finance and corporate governance. Topics often revolve around asset valuation of individual stocks, bonds, derivatives, currencies and other financial instruments.
Cointegration is a statistical property of a collection (X1, X2, ..., Xk) of time series variables. First, all of the series must be integrated of order d (see Order of integration). Next, if a linear combination of this collection is integrated of order less than d, then the collection is said to be co-integrated. Formally, if (X,Y,Z) are each integrated of order d, and there exist coefficients a,b,c such that aX + bY + cZ is integrated of order less than d, then X, Y, and Z are cointegrated. Cointegration has become an important property in contemporary time series analysis. Time series often have trends—either deterministic or stochastic. In an influential paper, Charles Nelson and Charles Plosser (1982) provided statistical evidence that many US macroeconomic time series (like GNP, wages, employment, etc.) have stochastic trends.
In statistics, homogeneity and its opposite, heterogeneity, arise in describing the properties of a dataset, or several datasets. They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part. In meta-analysis, which combines the data from several studies, homogeneity measures the differences or similarities between the several studies.
Lars Peter Hansen is an American economist. He is the David Rockefeller Distinguished Service Professor in Economics, Statistics, and the Booth School of Business, at the University of Chicago and a 2013 recipient of the Nobel Memorial Prize in Economics.
Christian Gouriéroux is an econometrician who holds a Doctor of Philosophy in mathematics from the University of Rouen. He has the Professor exceptional level title from France. Gouriéroux is now a professor at University of Toronto and CREST, Paris [Center for Research in Economics and Statistics].
Tim Peter Bollerslev is a Danish economist, currently the Juanita and Clifton Kreps Professor of Economics at Duke University. A fellow of the Econometric Society, Bollerslev is known for his ideas for measuring and forecasting financial market volatility and for the GARCH model.
Sir David Forbes Hendry, FBA CStat is a British econometrician, currently a professor of economics and from 2001 to 2007 was head of the economics department at the University of Oxford. He is also a professorial fellow at Nuffield College, Oxford.
In financial econometrics, an autoregressive conditional duration model considers irregularly spaced and autocorrelated intertrade durations. ACD is analogous to GARCH. In a continuous double auction waiting times between two consecutive trades vary at random.
John Denis Sargan, FBA was a British econometrician who specialized in the analysis of economic time-series.
Anil K. Bera is an Indian-American econometrician. He is Professor of Economics at University of Illinois at Urbana–Champaign's Department of Economics. He is most noted for his work with Carlos Jarque on the Jarque–Bera test.
The LSE approach to econometrics, named for the London School of Economics, involves viewing econometric models as reductions from some unknown data generation process (DGP). A complex DGP is typically modelled as the starting point and this complexity allows information in the data from the real world but absent in the theory to be drawn upon. The complexity is then reduced by the econometrician by a series of restrictions which are tested.
William H. Greene is an American economist. He was formerly the Robert Stansky Professor of Economics and Statistics at Stern School of Business at New York University. Greene is currently a Visiting Professor of Economics at the University of South Florida.
Francis X. Diebold is an American economist known for his work in predictive econometric modeling, financial econometrics, and macroeconometrics. He earned both his B.S. and Ph.D. degrees at the University of Pennsylvania, where his doctoral committee included Marc Nerlove, Lawrence Klein, and Peter Pauly. He has spent most of his career at Penn, where he has mentored approximately 75 Ph.D. students. Presently he is Paul F. and Warren S. Miller Professor of Social Sciences and Professor of Economics at Penn’s School of Arts and Sciences, and Professor of Finance and Professor of Statistics at Penn’s Wharton School. He is also a Faculty Research Associate at the National Bureau of Economic Research in Cambridge, Massachusetts, and author of the No Hesitations blog.
High frequency data refers to time-series data collected at an extremely fine scale. As a result of advanced computational power in recent decades, high frequency data can be accurately collected at an efficient rate for analysis. Largely used in financial analysis and in high frequency trading, high frequency data provides intraday observations that can be used to understand market behaviors, dynamics, and micro-structures.
Richard T. Baillie is a British–American economist and statistician who is currently the A J Pasant Professor of Economics at the Michigan State University. He is also part time professor at King's College, London, and Senior Scientific Officer for the Rimini Center for Economic Analysis in Italy, and also on the Executive Council of the Society for Nonlinear Dynamics in Econometrics (SNDE).
In statistics, a sequence of random variables is homoscedastic if all its random variables have the same finite variance; this is also known as homogeneity of variance. The complementary notion is called heteroscedasticity, also known as heterogeneity of variance. The spellings homoskedasticity and heteroskedasticity are also frequently used. Assuming a variable is homoscedastic when in reality it is heteroscedastic results in unbiased but inefficient point estimates and in biased estimates of standard errors, and may result in overestimating the goodness of fit as measured by the Pearson coefficient.
