Chaotic Oscillations in Real Economic Time Series Data: Evaluation of Logistic Model Fit and Forecasting Performance

1998 ◽  
pp. 73-92
Author(s):  
John Dimoticalis ◽  
Sotiris Zontos ◽  
Christos H. Skiadas
Keyword(s):  
Time Series ◽  
Logistic Model ◽  
Time Series Data ◽  
Model Fit ◽  
Data Evaluation ◽  
Series Data ◽  
Economic Time Series ◽  
Chaotic Oscillations ◽  
Forecasting Performance ◽  
Economic Time

A Relevance Study of Economic Time Series Data

LISS 2020 ◽  
2021 ◽  
pp. 405-417
Author(s):  
Lei Han ◽  
Wei Cui ◽  
Wei Zhang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Economic Time Series ◽  
Economic Time

A comparison between MS-VECM and MS-VECMX on economic time series data

2014 ◽  
Author(s):  
Seuk-Wai Phoong ◽  
Mohd Tahir Ismail ◽  
Siok-Kun Sek
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Economic Time Series ◽  
Economic Time

AN INTEGRATED DATA CHARACTERISTIC TESTING SCHEME FOR COMPLEX TIME SERIES DATA EXPLORATION

2013 ◽  
Vol 12 (03) ◽  
pp. 491-521 ◽  
Author(s):  
LING TANG ◽  
LEAN YU ◽  
FANGTAO LIU ◽  
WEIXUAN XU
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Data Exploration ◽  
Series Data ◽  
Economic Time Series ◽  
Noise Pattern ◽  
Complex Time ◽  
Testing Scheme ◽  
Sample Data ◽  
Economic Time

In this paper, an integrated data characteristic testing scheme is proposed for complex time series data exploration so as to select the most appropriate research methodology for complex time series modeling. Based on relationships across different data characteristics, data characteristics of time series data are divided into two main categories: nature characteristics and pattern characteristics in this paper. Accordingly, two relevant tasks, nature determination and pattern measurement, are involved in the proposed testing scheme. In nature determination, dynamics system generating the time series data is analyzed via nonstationarity, nonlinearity and complexity tests. In pattern measurement, the characteristics of cyclicity (and seasonality), mutability (or saltation) and randomicity (or noise pattern) are measured in terms of pattern importance. For illustration purpose, four main Chinese economic time series data are used as testing targets, and the data characteristics hidden in these time series data are thoroughly explored by using the proposed integrated testing scheme. Empirical results reveal that the natures of all sample data demonstrate complexity in the phase of nature determination, and in the meantime the main pattern of each time series is captured based on the pattern importance, indicating that the proposed scheme can be used as an effective data characteristic testing tool for complex time series data exploration from a comprehensive perspective.

scholarly journals A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data

2013 ◽  
Vol 5 (8) ◽  
pp. 379-384
Author(s):  
Seuk Wai ◽  
Mohd Tahir Ismail . ◽  
Siok Kun Sek .
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Markov Switching ◽  
Stock Index ◽  
Smooth Transition ◽  
Series Data ◽  
Var Model ◽  
Economic Time Series ◽  
Vector Autoregressive ◽  
Economic Time

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.

scholarly journals Variable Trends in Economic Time Series

1988 ◽  
Vol 2 (3) ◽  
pp. 147-174 ◽  
Author(s):  
James H Stock ◽  
Mark W Watson
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Economic Time Series ◽  
Growth Trend ◽  
Cyclical Fluctuations ◽  
Long Run ◽  
Stabilization Policies ◽  
Economic Time ◽  
The 1960S

The two most striking historical features of aggregate output are its sustained long run growth and its recurrent fluctuations around this growth path. Over horizons of a few years, these shorter cyclical swings can be pronounced; for example, the 1953, 1957, and 1974 recessions are evident as substantial temporary declines in aggregate activity. These cyclical fluctuations are, however, dwarfed in magnitude by the secular expansion of output. But just as there are cyclical swings in output, so too are there variations in the growth trend: growth in GNP in the 1960s was much stronger than it was in the 1950s. Thus, changes in long-run patterns of growth are an important feature of postwar aggregate economic activity. In this article, we discuss the implications of changing trends in macroeconomic data from two perspectives. The first perspective is that of a macroeconomist reassessing the conventional dichotomy between growth and stabilization policies. As an empirical matter, does this dichotomy make sense for the postwar United States? What is the relative “importance” of changes in the trend and cyclical swings in explaining the quarterly movements in economic aggregates? We next adopt the perspective of an econometrician interpreting empirical evidence based on data that contain variable trends. The presence of variable trends in time series data can lead one to draw mistaken inferences using conventional econometric techniques. How can these techniques -- or our interpretation of them -- be modified to avoid these mistakes?

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