Estimating Missing Observations in Economic Time Series

By Andrew S. Harvey, R. Pierse. 1984

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Citations in the corpus

Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction (2019, methodology): “…and uncertainty estimation, this decomposition of inference steps enables Bayes ﬁlters to be deployed in use cases beyond basic one-step-ahead prediction – with simple extensions for multistep prediction (Harvey, 1991) and prediction in the presence of missing observations (Harvey & Pierse, 1984).”
The MIDAS Touch: Mixed Data Sampling Regression Models (2004, unknown): “One often cited example is the use of the Kalman filter which is used to interpolate “missing data” (see e.g. Harvey and Pierse (1984) and subsequent work).”
Diagnostic Checking in ARMA Models With Uncorrelated Errors (2005, methodology): “The aggregation properties of ARMA models are well known (see, e.g., Amemiya and Wu 1972; Harvey and Pierse 1984; Palm and Nijman 1984).”
Modeling irregularly spaced residual series as a continuous stochastic process (2005, background): “Over the years, there has been a lot of eff olving this problem, in the time series literature [Jones, 1980; Harvey and Pierse, 1984; Little and Rubin, 1987] but more recently also focused on hydrologic problems [Bierkens et al., 1999; Koutsoyiannis, 2001; Berendrecht et al., 2003; Yi and Lee, 200”
Temporal Disaggregation By State Space Methods: Dynamic Regression Methods Revisited (2006, unknown): “As far as the latter are concerned, the illustrative examples presented in the paper provide further support for the modelling strategy set outlined in Hendry and Mizon (1978), which leads to entertain the Chow-Lin or the Fernandez model provided certain common factor restriction proves valid.”
Macroeconomics and the Reality of Mixed Frequency Data (2016, background): “2 See for example, Harvey and Pierse (1984), Bernanke et al. (1997), Zadrozny (1990), Mariano and Murasawa (2003), Mittnik and Zadrozny (2004), and more recently Aruoba et al. (2009), Ghysels and Wright (2009), Kuzin et al. (2009), Marcellino and Schumacher (2010), Foroni and Marcellino (2014) and…”
State Space Models and MIDAS Regressions (2013, methodology): “The mismatch of sampling frequency has been addressed in the context of state space models by Harvey and Pierse (1984), Harvey (1989), Bernanke, Gertler, and Watson (1997), Zadrozny (1990), Mariano and Murasawa (2003), Mittnik and Zadrozny (2004), Aruoba, Diebold, and Scotti (2009), Ghysels and…”
Forecasting Professional Forecasters (2009, unknown): “…a more structural approach in which we use the Kalman filter to estimate what forecasters would predict if they were asked to make a forecast each day, treating their forecasts as “missing data” to be interpolated (see, e.g., Harvey and Pierse 1984; Harvey 1989; Bernanke, Gertler, and Watson 1997).”
AN INDICATOR OF MONTHLY GDP AND AN EARLY ESTIMATE OF QUARTERLY GDP GROWTH (2005, unknown): “Appendix B sets up the interpolation problem described above in state-space form for comparison purposes with the approach suggested by Harvey and Pierse (1984) and Harvey (1989). The state-space reformulation may then be readily estimated using the Kalman filter but this reformulation suffers from ”
Cointegration Analysis with Mixed-Frequency Data (2007, background): “See also Harvey and Pierce (1984) and Mariano and Murasawa (2003).”
A Comparison of Missing-Data Procedures for Arima Time-Series Analysis (2005, background): “The fields of econometrics and engineering have produced more in-depth treatment of this topic (Harvey & Pierse, 1984; Kohn & Ansley, 1986).”
Temporal Disaggregation Using Multivariate Structural Time Series Models (2005, unknown): “As suggested in Harvey and Pierce (1984), one way to proceed would be to assume that the logarithms of the variables are normally distributed, then to modify accordingly the state-space representation of the model, and finally to use the extended Kalman filter to obtain an approximation to the…”
Bayesian Mixed Frequency VARs (2015, methodology): “While the MIDAS approach differs substantially from the Kalman filter approach of Harvey and Pierse (1984), it potentially suffers from the same pitfalls: handling observations that are irregularly spaced requires altering the estimated equations as in Francis et al. (2011), and larger systems may…”