scholarly journals Tests for Non-Linear Dynamics in Systems of Non-Stationary Economic Time Series: The Case of Short-Term US Interest Rates

2000 ◽  
Author(s):  
Barry E. Jones ◽  
Travis D. Nesmith
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Economic Time Series ◽  
Short Term ◽  
Linear Dynamics ◽  
Non Linear ◽  
Economic Time

scholarly journals Country macroeconomic indicators modeling features

2021 ◽  
Vol 47 ◽  
Author(s):  
Nomeda Bratčikovienė
Keyword(s):  
Time Series ◽  
Seasonal Adjustment ◽  
Economic Time Series ◽  
Short Term ◽  
Religious Traditions ◽  
School Year ◽  
Natural Factors ◽  
Macroeconomic Indicators ◽  
Economic Time ◽  
Simulated Series

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.

On a Simple Graphical Approach to Modelling Economic Fluctuations with an Application to United Kingdom Price Inflation, 1265 to 2005

2006 ◽  
Vol 1 (1) ◽  
pp. 103-128
Author(s):  
W. S. Chan ◽  
M. W. Ng ◽  
H. Tong
Keyword(s):  
Time Series ◽  
United Kingdom ◽  
Structural Changes ◽  
Linear Models ◽  
Stochastic Modelling ◽  
Economic Time Series ◽  
Price Inflation ◽  
Non Linear ◽  
Economic Time ◽  
The Impact

ABSTRACTStructural instability in economic time series is widely reported in the literature. It is most prevalent in such series as price indices and inflation related data. Many methods have been developed for analysing and modelling structural changes in a univariate time series model. However, most of them assume that the data are generated by one fixed type (linear or non-linear) of the time series processes. This paper proposes a strategy for modelling different segments of an economic time series by different linear or non-linear models. A graphical procedure is suggested for detecting the model change points. The proposed procedure is illustrated by modelling annual United Kingdom price inflation series over the period 1265 to 2005. Stochastic modelling of inflation rates is an important topic to actuaries for dealing with long-term index linked insurance business. The proposed method suggests dividing the U.K. inflation series into four segments for modelling. Inflation projections based on the latest segment of the data are obtained through simulations. To get a better understanding of the impact of structural changes on inflation projections we also perform a forecasting study.

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