Econometrics

Benchmarking, Temporal Distribution, and Reconciliation by Estela Bee Dagum

By Estela Bee Dagum

In glossy economies, time sequence play an important function in any respect degrees of job. they're utilized by choice makers to devise for a greater destiny, by way of governments to advertise prosperity, via crucial banks to manage inflation, through unions to cut price for larger wages, via medical institution, college forums, brands, developers, transportation businesses, and by way of shoppers in general.

A universal false impression is that point sequence information originate from the direct and simple compilations of survey info, censuses, and administrative documents. to the contrary, prior to book time sequence are topic to statistical alterations meant to facilitate research, elevate potency, decrease bias, exchange lacking values, right blunders, and fulfill cross-sectional additivity constraints. probably the most universal alterations are benchmarking, interpolation, temporal distribution, calendarization, and reconciliation.

This ebook discusses the statistical equipment quite often utilized for such changes, starting from advert hoc approaches to regression-based types. The latter are emphasised, as a result of their readability, ease of program, and stronger effects. each one subject is illustrated with many genuine case examples. that allows you to facilitate knowing in their houses and barriers of the tools mentioned, a true facts instance, the Canada overall Retail exchange sequence, is during the book.

This ebook brings jointly the scattered literature on those subject matters and offers them utilizing a constant notation and a unifying view. The e-book will advertise greater systems by means of huge manufacturers of time sequence, e.g. statistical enterprises and important banks. additionally, figuring out what changes are made to the information and what approach is used and the way they impact the fashion, the enterprise cycles and seasonality of the sequence, will allow clients to accomplish higher modeling, prediction, research and planning.

This ebook will turn out important to graduate scholars and ultimate 12 months undergraduate scholars of time sequence and econometrics, in addition to researchers and practitioners in govt associations and company.

From the reviews:

"It is a superb reference ebook for individuals operating during this area." B. Abraham for brief booklet reports of the ISI, December 2006

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Extra info for Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series

Sample text

Deaths also occur more often on certain days of the week. Car accidents, drowning, skiing and other sporting accidents tend to occur on weekend days and on holidays. According to the Canadian Workmen Compensation Board, industrial accidents tend to occur more often on Friday afternoons when security is more lax. In principle, stock series pertaining to one day display a particular kind of trading-day variations. Among other things, inventories must anticipate the activity (flow) of the following day(s).

Appendix E provides regressors for weekly and bi-weekly daily patterns. 6 of Chapter 9 discusses the calendarization of such data. 25 for the leap and non-leap years respectively. 7 The Trading-Day Component 41 Many goods and services are affected by daily patterns of activity, which entail higher costs for producers, namely through the need of higher inventories, equipment and staff on certain days of the week. For example, there is evidence that consumers buy more gasoline on certain days of the week, namely on Thursdays, Fridays, Saturdays and holidays, which results in line-ups and shortages at the pumps.

22) are specified as random variables instead of constant coefficients (see Dagum 2001). 24b) 34 The Components of Time Series subject to constraints where is assumed white noise. 24) specifies seasonality as a non-stationary random walk process. Since , model-based seasonal adjustment method assigns to the trend and to the seasonal component. 25) which entails a volatile seasonal behaviour, because the sum is not constrained to 0 but to the value of . e. 4 months. g. Harvey 1981, Kitagawa and Gersch 1984).

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