• Communications for Statistical Applications and Methods
• /
• v.24 no.5
• /
• pp.421-441
• /
• 2017

In this survey, estimation methods for structural vector autoregressive models are presented in a systematic way. Both frequentist and Bayesian methods are considered. Depending on the model setup and type of restrictions, least squares estimation, instrumental variables estimation, method-of-moments estimation and generalized method-of-moments are considered. The methods are presented in a unified framework that enables a practitioner to find the most suitable estimation method for a given model setup and set of restrictions. It is emphasized that specifying the identifying restrictions such that they are linear restrictions on the structural parameters is helpful. Examples are provided to illustrate alternative model setups, types of restrictions and the most suitable corresponding estimation methods.

• Smart Structures and Systems
• /
• v.10 no.4_5
• /
• pp.393-410
• /
• 2012

The 610 m high Canton Tower (formerly named Guangzhou New Television Tower) is currently considered as a benchmark problem for structural health monitoring (SHM) of high-rise slender structures. In the benchmark study task I, a set of 24-hour ambient vibration measurement data has been available for the output-only system identification study. In this paper, the vector autoregressive models (ARV) method is adopted in the operational modal analysis (OMA) for this TV tower. The identified natural frequencies, damping ratios and mode shapes are presented and compared with the available results from some other research groups which used different methods, e.g., the data-driven stochastic subspace identification (SSI-DATA) method, the enhanced frequency domain decomposition (EFDD) algorithm, and an improved modal identification method based on NExT-ERA technique. Furthermore, the environmental effects on the estimated modal parameters are also discussed.

• Smart Structures and Systems
• /
• v.17 no.6
• /
• pp.1017-1030
• /
• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

• Smart Structures and Systems
• /
• v.26 no.1
• /
• pp.23-33
• /
• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.1043-1051
• /
• 2013

We use a structural vector error correction model of the labor market to investigate the effect of shocks to Korean unemployment. We associate technology, labor demand, labor supply, and wage-setting shocks with equations for productivity, employment, unemployment, and real wages, respectively. Subsequently, labor demand and supply shocks have significant long-run and contemporaneous effects on unemployment, respectively.

• Smart Structures and Systems
• /
• v.32 no.1
• /
• pp.61-81
• /
• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• East Asian Journal of Business Economics (EAJBE)
• /
• v.1 no.1
• /
• pp.8-16
• /
• 2013

Purpose: The objective of this paper is to identify and assess the contribution of budgetary, monetary and institutional shocks affecting the Tunisian economy over the period 1976-2003. The methodology used is vector autoregressive models and structural recent techniques for the analysis of time series related. The empirical results show a significant relationship between the supply shock and institutions on the one hand, and between institutional shocks and economic activity on the other hand. Research Design, Data and Methodology: As part of this section we will try to identify and assess the contribution of various shocks to macroeconomic variables' fluctuations for the Tunisian economy. The study period is: 1976-2003 and observations are annual. Results: The real business cycle theory argues that fluctuations in aggregate economic activity are the result of the interaction of the only real factors namely agents' preferences, technological opportunities, factor endowments and possibly certain institutional constraints. Conclusions: The lowest contribution to the variability of these rights is the monetary shock. As for "civil liberties", the largest share of their variability is the shock relating to the "political rights" during the first four periods .

• Journal of Fisheries and Marine Sciences Education
• /
• v.28 no.1
• /
• pp.198-210
• /
• 2016

This study firstly aims to estimate a leading-price of Jeju flounders with various price-classes by fish weight and secondly plans to provide policy implications of flounder purchase projects by understanding dynamic changes and interactions among flounder producer price-classes caused by price impulses in the market. This study applies an unit root test for stability of data, uses a Granger causality test to estimate the leading-price among producer prices by fish weight, employs the vector autoregressive model to analyze statistical impacts among t-1 variables used in models, and finally utilizes impulse response analyses and forecast error variance decomposition analyses to understand dynamic changes and interactions among change rates of the producer prices caused by price impulses in the market. The results of the study are as follows. Firstly, KPSS, PP, and ADF tests show that the change rate of Jeju flounder monthly producer prices by fish weight differentiated by logarithm is stable. Secondly, the Granger causality test presents that the change rate of the 1kg flounder producer price strongly leads it of 500g, 700g, and 2kg flounder producer prices respectively. Thirdly, the vector autoregressive model indicates that the change rate of the 1kg producer price in t-1 period statistically, significantly influences it of own weight in t period and also slightly affects price change rates of other weights in t period. Fourthly, the impulse response analysis indicates that impulse responses of structural shocks for the change rate of the 1kg producer price are relatively more powerful in its own weight and in other weights than shocks emanating from price change rates of other weights. Fifthly, the variance decomposition analysis points out that the change rate of the 1kg producer price is relatively more influential than it of 500g, 700g, and 2kg producer prices respectively. In conclusion, the change rate of the 1kg Jeju flounder producer price leads the change rates of other ones and Jeju purchase projects need to be targeted to the 1kg Jeju flounder producer price as the purchase project implemented in 2014.

• The Journal of Industrial Distribution & Business
• /
• v.8 no.4
• /
• pp.15-21
• /
• 2017

Purpose - In this work, we examined the causal relationship between credit loans from households (CLH), loan collateralized with housing (LCH) and an interest of certificate of deposit (ICD) among others in South Korea. Furthermore, the optimal forecasts on the underlying model will be obtained and have the potential for applications in the economic field. Research design, data, and methodology - A total of 31 realizations sampled from the 4th quarter in 2008 to the 4th quarter in 2016 was chosen for this research. To achieve the purpose of this study, a regression model with correlated errors was exploited. Furthermore, goodness-of-fit measures was used as tools of optimal model-construction. Results - We found that by applying the regression model with errors component ARMA(1,5) to CLH, the steep and lasting rise can be expected over the next year, with moderate increase of LCH and ICD. Conclusions - Based on 2017-2018 forecasts for CLH, the precipitous and lasting increase can be expected over the next two years, with gradual rise of two major explanatory variables. By affording the assumption that the feedback among variables can exist, we can, in the future, consider more generalized models such as vector autoregressive model and structural equation model, to name a few.

• Management & Information Systems Review
• /
• v.35 no.3
• /
• pp.1-22
• /
• 2016

We analyzed the international comovements and structural changes in the quarterly real GDP by the Markov-switching vector autoregressive model (MS-VAR) from 1971(1) to 2016(1). The main results of this study were as follows. First, the business cycle phenomenon that occurs in the models or individual time series in real GDP has been grasped through the MS-VAR models. Unlike previous studies, this study showed the significant comovements, asymmetry and structural changes in the MS-VAR model using a real GDP across countries. Second, even if there was a partial difference, there were remarkable structural changes in the economy contraction regime(recession), such as 1988(2) ending the global oil shock crisis and 2007(3) starting the global financial crisis by the MS-VAR model. Third, large-scale structural changes were generated in the economic expansion and/or contraction regime simultaneously among countries. We found that the second world oil shocks that occurred after the first global oil shocks of 1973 and 1974 were the main reasons that caused the large-scale comovements of the international real GDP among countries. In addition, the spillover between Korea and 5 countries has been weak during the Asian currency crisis from 1997 to 1999, but there was strong transmission between Korea and 5 countries at the end of 2007 including the period of the global financial crisis. Fourth, it showed characteristics that simultaneous correlation appeared to be high due to the country-specific shocks generated for each country with the regime switching using real GDP since 1973. Thus, we confirmed that conclusions were consistent with a number of theoretical and empirical evidence available, and the macro-economic changes were mainly caused by the global shocks for the past 30 years. This study found that the global business cycles were due to large-scale asymmetric shocks in addition to the general changes, and then showed the main international comovements and/or structural changes through country-specific shocks.