By William R. Bell, Scott H. Holan, Tucker S. McElroy
Economic Time sequence: Modeling and Seasonality is a targeted source on research of monetary time sequence as relates to modeling and seasonality, offering state-of-the-art examine that will rather be scattered all through assorted peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization among the fields of time sequence modeling and seasonal adjustment, as is mirrored either within the contents of the chapters and of their authorship, with participants coming from academia and executive statistical agencies.
For more uncomplicated perusal and absorption, the contents were grouped into seven topical sections:
- Section I offers with periodic modeling of time sequence, introducing, utilising, and evaluating quite a few seasonally periodic models
- Section II examines the estimation of time sequence elements while types for sequence are misspecified in a few experience, and the wider implications this has for seasonal adjustment and enterprise cycle estimation
- Section III examines the quantification of blunders in X-11 seasonal alterations, with comparisons to blunders in model-based seasonal adjustments
- Section IV discusses a few useful difficulties that come up in seasonal adjustment: constructing uneven trend-cycle filters, facing either temporal and contemporaneous benchmark constraints, detecting trading-day results in per thirty days and quarterly time sequence, and utilizing diagnostics at the side of model-based seasonal adjustment
- Section V explores outlier detection and the modeling of time sequence containing severe values, constructing new tactics and lengthening prior work
- Section VI examines a few substitute types and inference approaches for research of seasonal fiscal time series
- Section VII bargains with elements of modeling, estimation, and forecasting for nonseasonal fiscal time series
By providing new methodological advancements in addition to pertinent empirical analyses and reports of tested equipment, the ebook offers a lot that's stimulating and essentially precious for the intense researcher and analyst of monetary time sequence.
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Additional info for Economic Time Series: Modeling and Seasonality
The possible reasons for the diﬀerent ﬁndings are as follows. First, the MPUC Model 1 has allowed for periodicity in the trend and seasonal components, while KW has no periodicity in these components. The only periodic component in the KW model is the cycle component. In our speciﬁcation, the cycle component turns out to be nonperiodic since all the periodic properties in the time series appear to be suﬃciently captured by the periodic trend and seasonal components. The periodic properties in the data can only be captured by the KW model through the cycle component.
We would like to emphasize that our current model has a basic time series decompostion structure. Residual diagnostic statistics for tests against normality and heteroskedasticity are also computed and investigated. These statistics are computed for all seven residual series. Overall, these statistics are satisfactory, although the normality assumption for the ND, C, and TTU sectors can be rejected based on their skewness and kurtosis. Such rejections are most likely caused by the existence of outlying observations in the original time series.
A. (1999). 2. com. Koopman, S. , and Doornik, J. A. (2008). 0. London: Timberlake Consultants Ltd. Krane, S. and Wascher, W. (1999). S. unemployment. Journal of Monetary Economics 44:523–53. Li, W. K. and Hui, Y. V. (1988). An algorithm for the exact likelihood of periodic autoregressive moving average models. Communications in Statistics, Simulations and Computation 17:1483–94. Lund, R. and Basawa, I. (2000). Recursive prediction and likelihood evaluation for periodic ARMA models. Journal of Time Series Analysis 21:75–93.