About
The department was founded in 1964 and has 35 permanent members. We are a relatively young group, all committed to a rigorous analytical approach to both teaching and research. As a consequence, we have a congenial and cooperative atmosphere in which department members take an unusually active interest in their colleagues' research. There are no social or administrative distinctions between junior and senior faculty, except on promotion decisions. Eight faculty members are Fellows of the Econometric Society, three are on the Econometric Society Council, and three are Fellows of the American Academy of Arts and Sciences. Five are NBER Research Associates, and twelve have NSF grants.
University of California, San Diego
9500 Gilman Drive
La Jolla, CA
92093-0508
USA
Department of Economics, UCSD
Recent Work (427)
Econometric Analysis of Discrete-Valued Irregularly-Spaced Financial Transactions Data Using a New Autoregressive Conditional Multinomial Model
This paper proposes a new approach to modeling financial transactions data. A new model for discrete valued time series is proposed in the context of generalized linear models. Since the model is specified conditional on both the previous state, as well as the historic distribution, we call the model the Autoregressive Conditional Multinomial (ACM) model. When the data are viewed as a marked point process, the ACD model proposed in Engle and Russell (1998) allows for joint modeling of the price transition probabilities and the arrival times of the transactions. In this marked point process context, the transition probabilities vary continuously through time and are therefore duration dependent. Finally, variations of the model allow for volume and spreads to impact the conditional distribution of price changes. Impulse response studies show the long run price impact of a transaction can be very sensitive to volume but is less sensitive to the spread and transaction rate.
Robust Covariance Matrix Estimation with Data-Dependent VAR Prewhitening Order
This paper analyzes the performance of heteroskedasticity-and-autocorrelation-consistent (HAC) covariance matrix estimators in which the residuals are prewhitened using a vector autoregressive (VAR) filter. We highlight the pitfalls of using an arbitrarily fixed lag order for the VAR filter, and we demonstrate the benefits of using a model selection criterion (either AIC or BIC) to determine its lag structure. Furthermore, once data-dependent VAR prewhitening has been utilized, we find negligible or even counter-productive effects of applying standard kernel-based methods to the prewhitened residuals; that is, the performance of the prewhitened kernel estimator is virtually indistinguishable from that of the VARHAC estimator.