[PDF] Top 20 Bayesian analysis of cointegrated vector autoregressive models
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Bayesian analysis of cointegrated vector autoregressive models
... In this chapter we deal with testing for multiple structural breaks in a vector error correction model as a problem of model selection and approximate the Bayes factors by Schwarz's Baye[r] ... See full document
144
Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models
... VAR models involving more than 1 lag were also fitted to the data, but did not indicate temporal dependence beyond lag ...the analysis based on the VAR-X component of our model, given by Z t = BX t−1 + CZ ... See full document
49
On Bayesian Identification of Autoregressive Processes
... indirect Bayesian approximate procedure to identify the orders of autoregressive moving average processes, denoted by ...exact analysis of the ARMA models, they approximated the posterior ... See full document
18
The effect of real shocks on business cycle fluctuations A Bayesian panel vector autoregressive approach
... Therefore, developing policies for stabilizing macroeconomic fluctuations has been the subject of many empirical enquiries in both advanced and developing economies. A prerequisite for building the structural ... See full document
20
Sensitivity to Prior Specification in Bayesian Identification of Autoregressive Time Series Models
... multivariate autoregressive models (Shaarawy and Ali, 2008), and multivariate moving average models (Shaarawy and Ali, ...the Bayesian time series analysis for a comprehensive and ... See full document
15
Deficit Financing and Inflation in Bangladesh: A Vector Autoregressive Analysis
... VAR models are difficult to interpret, impulse response functions and variance decompositions are generated through 1000 Monte Carlo simulations to better understand the dynamic relationships between the ... See full document
31
Generalized Impulse Response Analysis: General or Extreme?
... This note discusses a pitfall of using the generalized impulse response function (GIRF) in vector autoregressive (VAR) models (Pesaran and Shin, 1998). The GIRF is general because it is invariant to ... See full document
9
Model Uncertainty in Panel Vector Autoregressive Models
... We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of ... See full document
26
Bayesian analysis of multiple thresholds autoregressive model
... Various linear and nonlinear models have been proposed for this series. In general, for this series it seems that different data spans would suggest different models. Among others, Tsay (1989) employed a ... See full document
23
Frequentist and Bayesian Analysis of Random Coefficient Autoregressive models
... for models with random coefficients it is not clear if the penalty function should only depend on d as defined ...true models does not converge to zero as sample size goes to infinity, see Burnham ...competing ... See full document
146
Model uncertainty in panel vector autoregressive models
... We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of ... See full document
31
Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models
... noncausal models of the same order also produce the same value of the likelihood ...the Bayesian analysis of noncausal AR ...the models to be compared poses no particular problem in ... See full document
32
Partial Cointegrated Vector Autoregressive Models with Structural Breaks in Deterministic Terms
... partial cointegrated vector autoregressive models subject to structural breaks in deterministic terms, a linear trend and ...full models investigated by ...surface analysis in a ... See full document
35
Dynamic Bayesian models for vector time series analysis & forecasting
... Finally , in section 8.4 of this chapter , one of the most popular methods in the econometric literature about multivariate time series modelling and forecasting , the so called BVAR Ba[r] ... See full document
191
Time Series Analysis and Forecast of GDP in Ethiopia: Evidence from Ethiopian Data
... Ethiopia. Vector Autoregressive (VAR) Models, Testing Stationary: Unit root test, Estimating Order of the VAR, Cointegration Analysis (testing of cointegration) and Vector Error ... See full document
5
Stationary Vector Autoregressive Representation of Error Correction Models
... u by using the Model (13). An ECM (3) does not have explicit VAR form, and thus, it is not convenient for conducting such impulse response analysis. For this, we employ monthly data for the period from January ... See full document
5
Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models
... AVAR models using less assumption, particularly on the parameter’s prior distributions of popular Bayesian VAR ...deseasonalized vector of variables simulated from different data ... See full document
35
Bayesian analysis of random coefficient autoregressive models
... RCAR models are robust to model misspecification and the loss in efficiency due to more parameters is not severe as compared to AR ...RCAR models to time series data where volatility is ...For ... See full document
35
Forecasting Chinese inflation and output: A Bayesian vector autoregressive approach
... several Bayesian vector autoregressive (VAR) models for forecasting price inflation and output growth in ...that models with shrinkage and model selection priors, that restrict some VAR ... See full document
10
ARIMA-M: A New Model for Daily Water Consumption Prediction, Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction
... the analysis of the collected water consumption data, the identifiable data abnormal features includes data missing or zero, data mutation of zero, or a large data mutation, and so ... See full document
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