More Eyes, (No Guns,) Less Crime: Estimating the Effects of Unarmed Private Patrols on Crime Using a Bayesian Structural Time-Series Model

2016 ◽  
Author(s):  
Paul Liu ◽  
Marco Fabbri
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Structural Time Series ◽  
Structural Time

scholarly journals Consistent Multivariate Seasonal Adjustment for Gross Domestic Product and its Breakdown in Expenditures

2019 ◽  
Vol 35 (1) ◽  
pp. 9-30
Author(s):  
Reinier Bikker ◽  
Jan van den Brakel ◽  
Sabine Krieg ◽  
Pim Ouwehand ◽  
Ronald van der Stegen
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Time Series Model ◽  
Domestic Product ◽  
Seasonal Adjustment ◽  
Calendar Effects ◽  
Structural Time Series ◽  
Level Shifts ◽  
Structural Time ◽  
Constant Prices

Abstract Seasonally adjusted series of Gross Domestic Product (GDP) and its breakdown in underlying categories or domains are generally not consistent with each other. Statistical differences between the total GDP and the sum of the underlying domains arise for two reasons. If series are expressed in constant prices, differences arise due to the process of chain linking. These differences increase if, in addition, a univariate seasonal adjustment, with for instance X-13ARIMA-SEATS, is applied to each series separately. In this article, we propose to model the series for total GDP and its breakdown in underlying domains in a multivariate structural time series model, with the restriction that the sum over the different time series components for the domains are equal to the corresponding values for the total GDP. In the proposed procedure, this approach is applied as a pretreatment to remove outliers, level shifts, seasonal breaks and calendar effects, while obeying the aforementioned consistency restrictions. Subsequently, X-13ARIMA-SEATS is used for seasonal adjustment. This reduces inconsistencies remarkably. Remaining inconsistencies due to seasonal adjustment are removed with a benchmarking procedure.

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