Name
Abstract | ||
|---|---|---|
Among the alternative unobserved components formulations within the stochastic state space setting, the dynamic harmonic regression (DHR) model has proven to be particularly useful for adaptive seasonal adjustment, signal extraction, forecasting and back-casting of time series. First, it is shown how to obtain AutoRegressive moving average (ARMA) representations for the DHR components under a generalized random walk setting for the associated stochastic parameters; a setting that includes several well-known random walk models as special cases. Later, these theoretical results are used to derive an alternative algorithm, based on optimization in the frequency domain, for the identification and estimation of DHR models. The main advantages of this algorithm are linearity, fast computational speed, avoidance of some numerical issues, and automatic identification of the DHR model. The signal extraction performance of the algorithm is evaluated using empirical applications and comprehensive Monte Carlo simulation analysis. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.csda.2007.07.008 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
stochastic state space setting,signal extraction performance,dhr component,ordinary least squares,unobserved component models,generalized random walk,dynamic harmonic regression,associated stochastic parameter,spectral fitting,dhr model,linear dynamic harmonic regression,automatic identification,signal extraction,alternative unobserved components formulation,alternative algorithm,monte carlo methods,monte carlo simulation,state space,ordinary least square,seasonal adjustment,signal processing,harmonic analysis,random processes,optimization,random walk,time series analysis,parameter estimation,numerical methods,time series,frequency domain,moving average | Frequency domain,Econometrics,Autoregressive–moving-average model,Time series,Random walk,Stochastic process,Estimation theory,Statistics,Moving average,Mathematics,Autocorrelation | Journal |
Volume | Issue | ISSN |
52 | 2 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
4 | 0.62 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcos Bujosa | 1 | 4 | 0.95 |
| Antonio García-Ferrer | 2 | 4 | 0.62 |
| Peter C. Young | 3 | 222 | 110.94 |