Messy Data

This volume of Advances in Econometrics discusses new econometric techniques for addressing problems caused by working with incomplete data such as econometric results that are fragile due to the inclusion or omission of just a few observations in the sample.

List of contributors. Introduction (T.B. Fomby, R. Carter Hill). Testing for random individual and time effects using unbalanced panel data (B.H. Baltagi et al.). A statistical approach for disaggregating mixed-frequency economic time series data (Wai-Sum Chan, Zhao-Guo Chen). An extended Yule-Walker method for estimating a vector autoregressive model with mixed-frequency data (B. Chen, P.A. Zadrozny). Missing data from infrequency of purchase: Bayesian estimation of a linear expenditure system (W. Griffiths, M.R. Valenzuela). Messy time series: a unified approach (A. Harvey et al.). Simulation of multinomial probit probabilities and imputation of missing data (V. Lavy et al.). Temporal disaggregation, missing observations, outliers, and forecasting: a unifying non-model based procedure (M. Marcellino). Testing for unit roots in economic time-series with missing observations (K.F. Ryan, D.E.A. Giles). Influential data diagnostics for transition data (L.W. Taylor). The effects of different types of outliers on unit root tests (Yong Yin, G.S. Maddala).