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Resource Library
Time Series and Seasonal Adjustment Working Papers
Time Series and Seasonal Adjustment Working Papers
The following papers of interest to researchers and seasonal adjustment experts are available for downloading.
Working Paper
Model Estimation, Prediction, and Signal Extraction
A practical problem for statistical agencies and banks that publish economic data is the seasonal adjustment of mixed frequency stock and flow time series.
Working Paper
Illuminating Model-Based Seasonal Adjustment with the First Order S...
Illuminating Model-Based Seasonal Adjustment with the First Order Seasonal Autoregressive and Airline Models
Working Paper
Unit Root Properties of Seasonal Adjustment and Related Filters: Sp...
Unit Root Properties of Seasonal Adjustment and Related Filters: Special Cases
Working Paper
When are Direct Multi-step and Iterative Forecasts Identical?
Although both direct multi-step-ahead forecasting and iterated one-step-ahead forecasting are two popular methods for predicting future values of a time series.
Working Paper
Multivariate Seasonal Adjustment, Economic Identities, and Seasonal...
Multivariate Seasonal Adjustment, Economic Identities, and Seasonal Taxonomy
Working Paper
Comparing X-13ARIMA-SEATS Diagnostics for Quarterly Series
X-13ARIMA-SEATS has several diagnostics for determining whether a time series is seasonal and for detecting residual seasonality in an adjusted series.
Working Paper
Investigating the Behavior of X-13ARIMA-SEATS Seasonal Adj. Revisions
We assess the fluctuation of the seasonally adjusted estimates and how quickly they stabilize to a final value.
Working Paper
Optimal Real-Time Filters for Linear Prediction Problems
The model-based paradigm in time series analysis is rooted in the Wold decomposition of the data-generating process into an uncorrelated white noise process.
Working Paper
Signal Extraction for Non-stationary Multivariate Time Series ...
Working Paper
The Effect of Forecast Quality on Seasonal Adjustment Revisions
When data are available, the X-11 method uses symmetric moving average filters, utilizing the same amount of data before and after the point of interest.
Page Last Revised - November 22, 2021
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