The search for rebel interdependence

2017 ◽  
Vol 54 (1) ◽  
pp. 16-30 ◽  
Author(s):  
Ezra Schricker
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Entire System ◽  
Triangular System ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
Rebel Groups ◽  
Qualitative Knowledge ◽  
The Relationship

The existing conflict literature tends to treat interdependence between rebel groups as a binary category: either groups are allied or unallied, fragmented or unified, interdependent or independent. Yet much of our qualitative knowledge suggests that interdependence is better understood as a matter of degree where certain groups exert a disproportionate influence over their counterparts. The challenge is how to identify the degree of interdependence in practice. As a solution, I conceptualize interdependence as a property of a system of interactions between rebel groups and government forces within and across borders. My approach is to model the entire system of interactions in order to test hypotheses related to the directionality of influence and the potential for military coordination between groups. I demonstrate the utility of this approach by examining the relationship between Pakistan and the two major factions which make up the Taliban organization – the Afghan and Pakistani Taliban. I analyze the triangular system with a vector autoregressive model and monthly time series data on violent actions initiated by each group from January 2008 to February 2013. The substantive findings support much of the received wisdom concerning Pakistan’s disparate relationship to both groups, which is characterized by antagonism with the Pakistani Taliban and collusion with the Afghan Taliban. The results also suggest that the claims of interdependence between the two Taliban groups have been overstated.

scholarly journals Dynamic Modelling and Forecasting of Data Export of Agricultural Commodity by Vector Autoregressive Model

2020 ◽  
Vol 55 (3) ◽  
Author(s):  
L.M. Hamzah ◽  
S.U. Nabilah ◽  
E. Russel ◽  
M. Usman ◽  
E. Virginia ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Models ◽  
Time Series Data ◽  
Information Criterion ◽  
Series Data ◽  
Var Model ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
Coffee Beans ◽  
The Relationship

The Vector Autoregressive Model (VAR) is one of the statistical models that can be used for modeling multivariate time series data. It is commonly used in finance, management, business and economics. The VAR model analyzes the time series data simultaneously to arrive at the right conclusions while dynamically explaining the behavior of the relationship between endogenous variables, as well as endogenous and exogenous variables. From time to time, the VAR model is influenced by its own factors via Granger Causality. In this study, we will discuss and determine the best model to describe the relationship among data export value of Indonesia's agricultural commodities—coffee beans, cacao beans and tobacco—where the monthly data spans the years 2007-2018. Several models are applied to the data, such as VAR (1), VAR (2), VAR (3), VAR (4) and VAR (5) models. As a result, the VAR (2) model was chosen as the best model based on the Akaike’s Information Criterion with Correction, Schwarz Bayesian Criterion, Akaike’s Information Criterion and Hanna-Quinn Information Criterion for selecting statistical models. The dynamic behavior of the three export variables of Indonesian coffee beans, cacao beans and tobacco is explained by Granger Causality. Furthermore, the best model VAR (2) is used to forecast the next 10 months.

Use of Vector Autoregressive Model for Anomaly Detection in Utility Gas Turbines

2019 ◽  
Author(s):  
Vipul Goyal ◽  
Mengyu Xu ◽  
Jayanta Kapat
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Autoregressive Model ◽  
Gas Turbines ◽  
Time Series Data ◽  
Combined Cycle ◽  
Series Data ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
Multi Stage

Abstract This study is based on time-series data from the combined cycle utility gas turbines consisting of three-gas turbine units and one steam turbine unit. We construct a multi-stage vector autoregressive model for the nominal operation of powerplant assuming sparsity in the association among variables and use this as a basis for anomaly detection and prediction. This prediction is compared with the time-series data of the plant-operation containing anomalies. Granger causality networks, which are based on the associations between the time series streams, are learned as an important implication from the vector autoregressive modelling. Anomaly is detected by comparing the observed measurements against their predicted value.

scholarly journals Asymmetric Effects of the Effect of Oil Price on Stock Markets in Four Asian Countries: Markov Switching Analysis

2018 ◽  
pp. 423-428
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Business Cycle ◽  
Time Series Data ◽  
Markov Switching ◽  
Oil Price ◽  
Series Data ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
The Business Cycle

The Markov switching vector autoregressive model is a dynamic stochastic system with stochastic autoregressive parameters. This model able to measure a time varying problem when the variables undergoing regime switching. Structural change or shock is an ordinary fact in time series data. Some shocks have an important role under specific regimes in examining the business cycle contraction. Excluding changes in regime for the measurement of variance decomposition may produce biased results. Moreover, the parameters in the time series model might also have a structural change. Therefore, linear models are no longer suitable to be used in analyzing the financial model; and nonlinear time series models that are Markov switching models are proposed to solve these kinds of problems. A two regimes Markov switching vector autoregressive model is used in this study to analysis the time series data. The regime is dependent heterogeneous with varying the variance to detect every change of the business cycle. The correlations between oil price, Malaysia, Singapore, Thailand and Indonesia stock price are examining using Markov switching model. The result shows that the regimes dependent models suitable to employ in study the asymmetric business cycle; and oil price have a negative relationship with the changes of the four selected Asian stock markets.

scholarly journals PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 418-427
Author(s):  
Eko Siswanto ◽  
Hasbi Yasin ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Inverse Distance Weighted ◽  
Modeling And Forecasting ◽  
Distance Weighted ◽  
The Relationship ◽  
Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

scholarly journals A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

2021 ◽  
Vol 12 (5) ◽  
pp. 309-316
Author(s):  
Jae-Hyun Kim, Chang-Ho An
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Prediction Model ◽  
Interest Rates ◽  
Time Series Data ◽  
Information Criteria ◽  
Series Data ◽  
Call Rate ◽  
Causality Test ◽  
Vector Autoregressive

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.

scholarly journals An Empirical Analysis of Energy Consumption, FDI and High Quality Development Based on Time Series Data of Zhejiang Province

2020 ◽  
Vol 17 (9) ◽  
pp. 3321
Author(s):  
Shaolong Zeng ◽  
Yiqun Liu ◽  
Junjie Ding ◽  
Danlu Xu
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Energy Consumption ◽  
Open Economy ◽  
High Speed ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
High Quality ◽  
The Relationship

This paper aims to identify the relationship among energy consumption, FDI, and economic development in China from 1993 to 2017, taking Zhejiang as an example. FDI is the main factor of the rapid development of Zhejiang’s open economy, which promotes the development of the economy, but also leads to the growth in energy consumption. Based on the time series data of energy consumption, FDI inflow, and GDP in Zhejiang from 1993 to 2017, we choose the vector auto-regression (VAR) model and try to identify the relationship among energy consumption, FDI, and economic development. The results indicate that there is a long-run equilibrium relationship among them. The FDI inflow promotes energy consumption, and the energy consumption promotes FDI inflow in turn. FDI promotes economic growth indirectly through energy consumption. Therefore, improving the quality of FDI and energy efficiency has become an inevitable choice to achieve the transition of Zhejiang’s economy from high speed growth to high quality growth.

scholarly journals Visibility Graph Power Geometric Aggregation Operator and Its Application in Water, Energy and Food Efficiency Evaluation

2020 ◽  
Vol 17 (11) ◽  
pp. 3891
Author(s):  
Lihua Liu ◽  
Jing Huang ◽  
Huimin Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Coupling Efficiency ◽  
Aggregation Operator ◽  
Visibility Graph ◽  
Series Data ◽  
Efficiency Evaluation ◽  
Geometric Operator ◽  
The Relationship

In the real decision-making process, there are so many time series values that need to be aggregated. In this paper, a visibility graph power geometric (VGPG) aggregation operator is developed, which is based on the complex network and power geometric operator. Time series data are converted into a visibility graph. A visibility matrix is developed to denote the links among different time series values. A new support function based on the distance of two values are proposed to measure the support degree of each other when the two time series values have visibility. The VGPG operator considers not only the relationship but also the similarity degree between two values. Meanwhile, some properties of the VGPG operator are also investigated. Finally, a case study for water, energy, and food coupling efficiency evaluation in China is illustrated to show the effectiveness of the proposed operator. Comparative analysis with the existing research is also offered to show the advantages of the proposed method.

Influence of systemic banking crisis and currency crisis on the relationship of export and economic growth

2017 ◽  
Vol 10 (1) ◽  
pp. 82-110
Author(s):  
Syed Ali Raza ◽  
Mohd Zaini Abd Karim
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Financial Crises ◽  
Currency Crises ◽  
Time Series Data ◽  
Banking Crises ◽  
Series Data ◽  
Content Type ◽  
Relationship Of ◽  
The Relationship

Purpose This study aims to investigate the influence of systemic banking crises, currency crises and global financial crisis on the relationship between export and economic growth in China by using the annual time series data from the period of 1972 to 2014. Design/methodology/approach The Johansen and Jeuuselius’ cointegration, auto regressive distributed lag bound testing cointegration, Gregory and Hansen’s cointegration and pooled ordinary least square techniques with error correction model have been used. Findings Results indicate the positive and significant effect of export of goods and services on economic growth in both long and short run, whereas the negative influence of systemic banking crises and currency crises over economic growth is observed. It is also concluded that the impact of export of goods and service on economic growth becomes insignificant in the presence of systemic banking crises and currency crises. The currency crises effect the influence of export on economic growth to a higher extent compared to systemic banking crises. Surprisingly, the export in the period of global financial crises has a positive and significant influence over economic growth in China, which conclude that the global financial crises did not drastically affect the export-growth nexus. Originality/value This paper makes a unique contribution to the literature with reference to China, being a pioneering attempt to investigate the effects of systemic banking crises and currency crises on the relationship of export and economic growth by using long-time series data and applying more rigorous econometric techniques.

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