REGCMPNT A Fortran Program for Regression Models with ARIMA Component Errors

RegComponent models are time series models with linear regression mean functions and error terms that follow ARIMA (autoregressive-integrated-moving average) component time series models. Bell (2004) discusses these models and gives some underlying theoretical and computational results. The REGCMPNT program is a Fortran program for performing Gaussian maximum likelihood estimation, signal extraction, and forecasting with RegComponent models. In this paper we briefly examine the nature of RegComponent models, provide an overview of the REGCMPNT program, and then use three examples to show some important features of the program and to illustrate its application to various different RegComponent models.

[1]  Tucker McElroy,et al.  MATRIX FORMULAS FOR NONSTATIONARY ARIMA SIGNAL EXTRACTION , 2008, Econometric Theory.

[2]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[3]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[4]  K. Wolter.  Introduction to Variance Estimation , 1985 .

[5]  Siem Jan Koopman,et al.  Time Series Analysis by State Space Methods , 2001 .

[6]  Jorge J. Moré,et al.  User Guide for Minpack-1 , 1980 .

[7]  A. Scott,et al.  The Application of Time Series Methods to the Analysis of Repeated Surveys , 1977 .

[8]  David F. Findley,et al.  New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program , 1998 .

[9]  Siem Jan Koopman,et al.  Statistical Software for State Space Methods , 2011 .

[10]  Steven C. Hillmer,et al.  U.S. Census Bureau , 1991 .

[11]  William Bell.  Correction: Signal Extraction for Nonstationary Time Series , 1991 .

[12]  W. Bell,et al.  Signal Extraction for Nonstationary Time Series , 1984 .

[13]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Neil Shephard,et al.  Maximum Likelihood Estimation of Regression Models with Stochastic Trend Components , 1993 .

[15]  Luiz Koodi Hotta.  IDENTIFICATION OF UNOBSERVED COMPONENTS MODELS , 1989 .

[16]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[17]  William R. Bell,et al.  State Space and Unobserved Component Models: On RegComponent time series models and their applications , 2004 .

[18]  A. Scott,et al.  Analysis of repeated surveys using time series methods. , 1974, Journal of the American Statistical Association.

[19]  R. Kohn,et al.  Estimation, Filtering, and Smoothing in State Space Models with Incompletely Specified Initial Conditions , 1985 .

[20]  Steven C. Hillmer,et al.  Modeling Time Series with Calendar Variation , 1983 .

[21]  Marc K. Francke,et al.  Likelihood functions for state space models with diffuse initial conditions , 2006 .

[22]  Robert Kohn,et al.  Signal extraction for finite nonstationary time series , 1987 .

[23]  S. Koopman,et al.  Exact Initial Kalman Filtering and Smoothing for Nonstationary Time Series Models , 1997 .