On the Performances of Classical VAR and Sims-Zha Bayesian VAR Models in the Presence of Collinearity and Autocorrelated Error Terms

Adenomon, Monday Osagie; Michael, V. A.; Evans, Oteng

doi:10.4236/ojs.2016.61012

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…This study uses Sims and Zha [21] prior. The Sims-Zha BVAR allows for a more general specification and can produce a tractable multivariate normal posterior distribution (Adenomon and Evans [22]). This BVAR model is based a specification of the dynamic simultaneous equation representation of the model.…”

Section: Bvar Using Sims-zha Prior (Szbvar)mentioning

confidence: 99%

Intra-Market Linkages in the Financial Sector and Their Effects on Financial Inclusion

Lidiema¹

2018

IJFBR

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The financial stability objective of any financial system authority is to maintain confidence, and promote the safety and soundness of the domestic financial system. Financial stability has been defined as the resilience of the financial system in the face of adverse shocks to enable the continued smooth functioning of the financial intermediation process. The Kenyan Financial service providers are diverse and they include 42 commercial banks, 49 insurance companies, 12 deposits taking microfinance banks, and 199 registered savings and credit cooperatives (SACCOs). This paper examined financial intramarket linkages (dynamic relationship and volatility spillovers) effects between the Commercial banks and other financial sector segments (Insurance and Capital Markets) in Kenya and the impact of this transmission on financial inclusion. The study evaluated the effect of intra-market linkages on financial inclusion using Bayesian Vector Autoregressive (BVAR) using monthly data from the Kenyan market during the period January 2004-December 2016. Impulse-response analysis and forecast error variance decomposition were used to investigate these intra-market linkages and their causal effect to financial inclusion. Results show that there are significant market interactions and interlinkages with significant shocks transmission moving from banks to other markets. Interest rates shock transmission affected all markets. This means that monetary policy transmission as expected trickles down to the entire financial sector. The study also found out that, positive shocks from credit impacts positively on lending rate and the capital markets performance implying banking mechanism to reward increased loan uptake at cheaper prices and hence creating cash-flow that spills over to more investment on the Nairobi Securities Exchange. The study recommends that policy makers design policies that help minimize the adverse impact of volatility/shocks but create opportunities for growth on each market to foster price stability and increase investments through financial inclusion.

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Section: Bvar Using Sims-zha Prior (Szbvar)mentioning

confidence: 99%

Intra-Market Linkages in the Financial Sector and Their Effects on Financial Inclusion

Lidiema¹

2018

IJFBR

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“…We shall employ the estimation of the Bayesian VAR model by Sims & Zha [27]. According to Adenomon et al [28], "The Sims-Zha prior allows for a more general specification and can produce a tractable multivariate normal posterior distribution".…”

Section: Bvar Using Sims-zha Prior (Szbvar)mentioning

confidence: 99%

Forecasting Household Credit in Kenya Using Bayesian Vector Autoregressive (BVAR) Model

Lidiema¹,

Waititu²,

Ngunyi³

2018

AJAMS

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This research paper use Bayesian VAR framework to forecast the household credit in the dynamic market of foreign remittances inflow to Kenya. The Bayesian VARs model in this study employs the sims-Zha prior to estimate. Bayesian vector autoregressive (BVAR) uses Bayesian methods to estimate a vector autoregressive (VAR). In that respect, the difference with standard VAR models lies in the fact that the model parameters are treated as random variables, and prior probabilities are assigned to them. This study employed data from the Kenyan Market for the period January 2005-December 2017. The forecast results were compared with the standard ARIMA model and the findings confirm that the BVAR approach outperforms the ARIMA model. Financial institutions can therefore use Bayesian VAR and other Bayesian models in predicting credit uptake given several micro-economic conditions. Banks should also find ways of tapping into these remittances especially those that pass through informal channels to improve their earnings from processing fees and also enhance the financial inclusion agenda through increasing account opening and loan uptake.

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On the Performances of Classical VAR and Sims-Zha Bayesian VAR Models in the Presence of Collinearity and Autocorrelated Error Terms

Cited by 2 publications

References 18 publications

Intra-Market Linkages in the Financial Sector and Their Effects on Financial Inclusion

Intra-Market Linkages in the Financial Sector and Their Effects on Financial Inclusion

Forecasting Household Credit in Kenya Using Bayesian Vector Autoregressive (BVAR) Model

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