scholarly journals Optimal Reconciliation of Seasonally Adjusted Disaggregates Taking Into Account the Difference Between Direct and Indirect Adjustment of the Aggregate

2021 ◽  
Vol 37 (1) ◽  
pp. 31-51
Author(s):  
Francisco Corona ◽  
Victor M. Guerrero ◽  
Jesús López-Peréz

Abstract This article presents a new method to reconcile direct and indirect deseasonalized economic time series. The proposed technique uses a Combining Rule to merge, in an optimal manner, the directly deseasonalized aggregated series with its indirectly deseasonalized counterpart. The lastmentioned series is obtained by aggregating the seasonally adjusted disaggregates that compose the aggregated series. This procedure leads to adjusted disaggregates that verify Denton’s movement preservation principle relative to the originally deseasonalized disaggregates. First, we use as preliminary estimates the directly deseasonalized economic time series obtained with the X-13ARIMA-SEATS program applied to all the disaggregation levels. Second, we contemporaneously reconcile the aforementioned seasonally adjusted disaggregates with its seasonally adjusted aggregate, using Vector Autoregressive models. Then, we evaluate the finite sample performance of our solution via a Monte Carlo experiment that considers six Data Generating Processes that may occur in practice, when users apply seasonal adjustment techniques. Finally, we present an empirical application to the Mexican Global Economic Indicator and its components. The results allow us to conclude that the suggested technique is appropriate to indirectly deseasonalize economic time series, mainly because we impose the movement preservation condition to the preliminary estimates produced by a reliable seasonal adjustment procedure.

2013 ◽  
Vol 5 (8) ◽  
pp. 379-384
Author(s):  
Seuk Wai ◽  
Mohd Tahir Ismail . ◽  
Siok Kun Sek .

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.


2021 ◽  
Vol 47 ◽  
Author(s):  
Nomeda Bratčikovienė

Economic time series have repeatable or non-repeatable fluctuation. A pattern of a time series, which repeats at regular intervals every year, same direction, and similar magnitude is defined as seasonality. The seasonal component represents intra-year fluctuations that are more or less stable year after in a time series. Possible causes of these variations are a systematic and calendar related effects and include natural factors (for instance seasonalweather patterns), administrativemeasures (for example the starting and ending dates of the school year), social/cultural/religious traditions (fixed holidays such as Christmas), the length of the months (28, 29, 30 or 31 days) or quarters (90, 91 or 92 days).Analysts, economists, police makers use time series to make conclusions and decisions in respective area. They tray to identify important features of economic series such as short term changes, directions, turning points and consistency between other economic indicators. These points are usually in interest. Sometimes seasonal movements can make these features difficult to see and this type of analysis is not easy using raw time series data.Deterministic, TRAMO-SEATS and ARIMA-X-12 seasonal adjustment methods are analysed in this article. 1600 time serieswere simulated for solvingwhich seasonal adjustmentmethod is precise. TRAMOSEATS and ARIMA-X-12 both perform similarly for the simulated series. Econometric models of macroeconomic indicators of Lithuania reveal that modeling with seasonal adjusted data is more accurate.


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