Arima Processes With Arima Parameters
Bibliographic Data
| ID | 19418338 |
|---|---|
| Authors | Carlo Grillenzoni (0000-0002-9606-8127, Instituto Universitario di Architettura di Venezia, corresponding author) |
| Year | 1993 |
| Volume | 11 |
| Issue | 2 |
| Pages | 235-250 |
| Publication date | 1993-04-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.1993.10509952 |
| OpenAlex | W2010878208 |
| Language | EN |
| Citations received | 1 |
| References cited | 26 |
This article introduces a general class of nonlinear and nonstationary time series models whose basic scheme is an autoregressive integrated moving average (ARIMA). The main feature is that the parameters are assumed to behave like a vector ARIMAx model in which the exogenous (x) component is represented by the regressors of the observable process. For this class a general algorithm of identification-estimation is outlined, based on the sampling information alone. The initial estimation, in particular, consists of an iterative procedure of nonlinear regressions on recursive parameter estimates generated with the extended Kalman filter. An empirical example on real economic data illustrates the method and compares alternative criteria of estimation
Autoregressive integrated moving average · Autoregressive model · Econometrics · Estimation theory · Kalman filter · Moving average · Nonlinear system · Statistics · Time series · Advanced Control Systems Optimization · Computer Science · Forecasting Techniques and Applications · Mathematics · Target Tracking and Data Fusion in Sensor Networks · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |