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 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.

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 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.

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.

Reforming the Labour Market in Good and Bad Times

2018 ◽  
Author(s):  
Britta Gehrke ◽  
Enzo Weber
Keyword(s):  
Time Series ◽  
Business Cycle ◽  
Labour Market ◽  
Markov Switching ◽  
Matching Theory ◽  
Market Reforms ◽  
Structural Reforms ◽  
Labour Market Reforms ◽  
Short Run ◽  
The Business Cycle

This chapter discusses how the effects of structural labour market reforms depend on whether the economy is in expansion or recession. Based on an empirical time series model with Markov switching that draws on search and matching theory, we propose a novel identification of reform outcomes and distinguish the effects of structural reforms that increase the flexibility of the labour market in distinct phases of the business cycle. We find in applications to Germany and Spain that reforms which are implemented in recessions have weaker expansionary effects in the short run. For policymakers, these results emphasize the costs of introducing labour market reforms in recessions.

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 The Presidential Political Business Cycle of 1972

1984 ◽  
Vol 44 (2) ◽  
pp. 265-271 ◽  
Author(s):  
Robert R. Keller ◽  
Ann Mari May
Keyword(s):  
Time Series ◽  
Business Cycle ◽  
Time Series Data ◽  
Political Business Cycle ◽  
Series Data ◽  
The Political ◽  
Operating Environment ◽  
Macroeconomic Conditions ◽  
Price Controls ◽  
Time Period

Previous studies of the political business cycle have examined time series data to determine whether a pattern of pre-election boom and post-election slump exists. The studies do not investigate the behavior and mechanisms by which a politician may effectuate a political business cycle. We focus on one time period, 1969 to 1972, and conclude that President Nixon's personality and operating environment explain why he manipulated the economy for political gain. The mechanisms he utilized to improve macroeconomic conditions before the 1972 election include monetary policy, fiscal policy, and wage-price controls.

scholarly journals On the run-length of the structural change in time series data

2019 ◽  
Author(s):  
Wiwik Prihartanti ◽  
Dwilaksana Abdullah Rasyid ◽  
Nur Iriawan
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Time Series Data ◽  
Series Data ◽  
Run Length
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