TY  - JOUR
AU  - Engle,Robert F.
AU  - Russell,Jeffrey R.
TI  - Forecasting Transaction Rates: The Autoregressive Conditional Duration  Model
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 4966
PY  - 1994
Y2  - December 1994
UR  - http://www.nber.org/papers/w4966
L1  - http://www.nber.org/papers/w4966.pdf
N1  - Author contact info:
Robert F. Engle, III
Department of Finance, Stern School of Business
New York University, Salomon Center
44 West 4th Street, Suite 9-160
New York, NY  10012-1126
Tel: 212/998-0710
Fax: 212/995-4220
E-Mail: rengle@stern.nyu.edu

Jeffrey R. Russell
University of Chicago
Graduate School of Business
1101 East 58th Street
Chicago, IL  60637
E-Mail: jeffrey.russell@ChicagoBooth.edu
AB  - This paper will propose a new statistical model for the analysis of data that does not arrive in equal time intervals such as financial transactions data, telephone calls, or sales data on commodities that are tracked electronically.  In contrast to fixed interval analysis, the model treats the time between observation arrivals as a stochastic time varying process and therefore is in the spirit of the models of time deformation initially proposed by Tauchen and Pitts (1983), Clark (1973) and more recently discussed by Stock (1988), Lamoureux and Lastrapes (1992), Muller et al. (1990) and Ghysels and Jasiak (1994) but does not require auxiliary data or assumptions on the causes of time flow.  Strong evidence is provided for duration clustering beyond a deterministic component for the financial transactions data analyzed.  We will show that a very simple version of the model can successfully account for the significant autocorrelations in the observed durations between trades of IBM stock on the consolidated market.  A simple transformation of the duration data allows us to include volume in the model.
ER  -