Messy Data – Missing Observations, Outliers, and Mixed–Frequency Data de R. Carter Hill

Messy Data – Missing Observations, Outliers, and Mixed–Frequency Data: Advances in Econometrics

R. Carter Hill

Thomas B. Fomby

Often applied econometricians are faced with working with data that is less than ideal. The data may be observed with gaps in it, a model may suggest variables that are observed at different frequencies, and sometimes econometric results are very fragile to the inclusion or omission of just a few observations in the sample. Papers in this volume discuss new econometric techniques for addressing these problems.

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Cuprins

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).