New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program

X-12-ARIMA is the Census Bureau's new seasonal-adjustment program. It provides four types of enhancements to X-ll-ARIMA—(1) alternative seasonal, trading-day, and holiday effect adjustment capabilities that include adjustments for effects estimated with user-defined regressors; additional seasonal and trend filter options; and an alternative seasonal-trend-irregular decomposition; (2) new diagnostics of the quality and stability of the adjustments achieved under the options selected; (3) extensive time series modeling and model-selection capabilities for linear regression models with ARIMA errors, with optional robust estimation of coefficients; (4) a new user interface with features to facilitate batch processing large numbers of series.

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

[2]  Wallace E. Huffman,et al.  The Sample Spectrum of Time Series with Trading Day Variation , 1989 .

[3]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[4]  Andrew G. Bruce,et al.  Non-Gaussian seasonal adjustment: X-12-ARIMA versus robust structural models , 1996 .

[5]  Richard H. Jones,et al.  Maximum Likelihood Fitting of ARMA Models to Time Series With Missing Observations , 1980 .

[6]  Masanobu Taniguchi,et al.  A Central Limit Theorem of Stationary Processes and the Parameter Estimation of Linear Processes (時系列解析の推測 : 理論と応用) , 1981 .

[7]  Clifford M. Hurvich,et al.  Regression and time series model selection in small samples , 1989 .

[8]  P. Battipaglia A comparison of indicators for evaluating x-11-arima seasonal adjustment , 1996 .

[9]  Julius Shiskin,et al.  The X-11 variant of the census method II seasonal adjustment program , 1965 .

[10]  David F. Findley,et al.  Sliding-Spans Diagnostics for Seasonal and Related Adjustments , 1990 .

[11]  Alistair G. Gray,et al.  Design of Moving-Average Trend Filters using Fidelity and Smoothness Criteria , 1996 .

[12]  Estela Bee Dagum,et al.  MONTHLY VERSUS ANNUAL REVISIONS OF CONCURRENT SEASONALLY ADJUSTED SERIES , 1987 .

[13]  J. Ledolter The effect of additive outliers on the forecasts from ARIMA models , 1989 .

[14]  Frederick Robertson Macaulay,et al.  The Smoothing of Time Series , 1931 .

[15]  L. Breiman Heuristics of instability and stabilization in model selection , 1996 .

[16]  I. Zurbenko The spectral analysis of time series , 1986 .

[17]  Julius Shiskin Seasonal Adjustment of Sensitive Indicators , 1978 .

[18]  Robert Kirchner,et al.  Auswirkungen Des Neuen Saisonbereinigungsverfahrens Census X-12-Arima Auf Die Aktuelle Wirtschaftsanalyse in Deutschland (Effects Of New seasonal adjustment method Census X-12-ARIMA In The Current Economic Analysis in Germany) , 1999, SSRN Electronic Journal.

[19]  Kenneth F. Wallis,et al.  Seasonal Adjustment and Revision of Current Data: Linear Filters for the X‐11 Method , 1982 .

[20]  G. Huyot,et al.  Analysis of Revisions in the Seasonal Adjustment of Data Using X-11-Arima Model-Based filters , 1986 .

[21]  David F. Findley,et al.  New Techniques for Determining if a Time Series can be Seasonally Adjusted Reliably , 1986 .

[22]  W. P. Cleveland,et al.  Modeling time series when calendar effects are present , 1981 .

[23]  R. Kohn,et al.  Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data , 1986 .

[24]  S. Weisberg,et al.  Residuals and Influence in Regression , 1982 .

[25]  G. Ljung,et al.  On Outlier Detection in Time Series , 1993 .

[26]  Steven C. Hillmer,et al.  Issues Involved With the Seasonal Adjustment of Economic Time Series , 1984 .

[27]  Eric Ghysels,et al.  Série Scientifique Scientific Series N o 95 s19 IS SEASONAL ADJUSTMENT A LINEAR OR NONLINEAR DATA FILTERING PROCESS ? , 1997 .

[28]  Christopher A. Sims,et al.  Seasonality in Regression , 1974 .

[29]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[30]  R. Martin,et al.  Leave‐K‐Out Diagnostics for Time Series , 1989 .

[31]  Walter E. Hoadley The Business and Economic Statistics Section , 1951 .

[32]  Steven C. Hillmer,et al.  Likelihood Function of Stationary Multiple Autoregressive Moving Average Models , 1979 .

[33]  Estela Bee Dagum,et al.  Seasonal adjustment in the eighties: Some problems and solutions , 1988 .

[34]  Anthony L Bertapelle Spectral Analysis of Time Series. , 1979 .

[35]  G. C. Tiao,et al.  Estimation of time series parameters in the presence of outliers , 1988 .

[36]  D. Findley,et al.  ON THE UNBIASEDNESS PROPERTY OF AIC FOR EXACT OR APPROXIMATING LINEAR STOCHASTIC TIME SERIES MODELS , 1985 .

[37]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[38]  H. Akaike SEASONAL ADJUSTMENT BY A BAYESIAN MODELING , 1980 .

[39]  D. B. Preston Spectral Analysis and Time Series , 1983 .

[40]  W. Cleveland Seasonal and calendar adjustment , 1983 .

[41]  J. Peter But-man,et al.  OUTLIERS IN TIME SERIES , 1988 .

[42]  Víctor Gómez,et al.  Program SEATS 'Signal Extraction in Arima Time Series'. Instructions for the User , 1994 .

[43]  B. G. Quinn,et al.  The determination of the order of an autoregression , 1979 .

[44]  Johannes Ledolter,et al.  Statistical methods for forecasting , 1983 .

[45]  William S. Cleveland,et al.  Calendar Effects in Monthly Time Series: Modeling and Adjustment , 1982 .

[46]  G. C. Tiao,et al.  Decomposition of Seasonal Time Series: A Model for the Census X-11 Program , 1976 .

[47]  C. F. Ansley,et al.  A Class of Transformations for Box‐Jenkins Seasonal Models , 1977 .

[48]  J. Durbin,et al.  Local trend estimation and seasonal adjustment of economic and social time series (with discussion) , 1982 .

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

[50]  David F. Findley,et al.  A Conversation with Hirotugu Akaike , 1995 .

[51]  Walter Vandaele,et al.  Applied Time Series and Box-Jenkins Models , 1983 .

[52]  Eric Ghysels,et al.  Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process? , 1996 .