Bayesian Vector Autoregressive Model Research Articles

Carbon Market Efficiency and Economic Policy Uncertainty: Evidence from a TVP-VAR Model

This paper examines the dynamic linkages among economic policy uncertainty (EPU), the green bond market, the carbon market, and the macroeconomy using the time-varying parameter vector autoregressive (TVP-VAR) model with monthly data spanning from January 2016 to December 2021. Additionally, it assesses the robustness and accuracy of the empirical results through the Bayesian vector autoregressive (BVAR) model. The findings indicate that EPU negatively affects the green bond market in the short term but has a positive impact in the medium and long term. Conversely, EPU has a positive impact on the carbon market in the short term but a negative impact in the medium and long term. Furthermore, the green bond market negatively influences the carbon market in both the short and medium to long term. These results suggest that emerging markets, such as the green bond and carbon markets, are influenced by EPU. The adverse impact of the green bond market on the carbon market, however, contributes to expediting China’s attainment of its low-carbon objectives. Appropriate economic policies can play a vital role in accelerating the transition to a low-carbon economy. The study also reveals that the US-China trade war has expedited the development of green capital markets in China, despite its impact on the green economic transition in the country. These findings provide insights for the government and investors to formulate suitable strategies for risk mitigation.

Bayesian Markov switching model for BRICS currencies' exchange rates

AbstractExchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time‐varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis‐à‐vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal‐gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime‐specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime‐specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out‐of‐sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model.

Analysing the Nexus between the Financial Sector and Economic Growth in Nigeria: A Comparative Investigation using, BVAR, Linear Regression (OLS), and PPML Models

Aims: This study aims to analyze the complex relationship between the financial sector and economic growth in Nigeria. The study aims to provide comprehensive insights into this nexus by employing a comparative investigation using three distinct models: Linear Regression, Poisson Pseudo Maximum Likelihood (PPML), and Bayesian Vector Autoregression (BVAR).
 Methodology: The study then applied three different models, with a specific focus on the BVAR(2) model, supported by various diagnostic tests and stability assessments. The inclusion of Linear regression analysis and Poisson Pseudo Maximum Likelihood Estimator (PPML) enhances the depth of the study, providing nuanced insights into the impact of specific financial sector variables on economic growth.
 Results: The BVAR (2) model emerges as the optimal choice, demonstrating its reliability in capturing dynamic interactions and offering a powerful tool for policymakers. Specific results, such as the significant negative impact of D(CPS) in the regression analysis and the high R-squared in PPML, provide actionable insights into areas requiring policy interventions and underscore the substantial contribution of the financial sector to economic growth.
 Conclusion: The comparative assessment of model performances, favoring the BVAR model, guides future research and policy considerations, providing a reliable framework for further investigations. The study's insights are positioned as valuable for policymakers seeking to enhance economic growth through strategic interventions in the financial sector. Overall, the abstract succinctly encapsulates the aims, methodology, results, and concluding implications of the study on the nexus between the financial sector and economic growth in Nigeria.

Economic Growth and Inflation Tradeoffs within Global Stagflation: Evidence from the Vietnam Economy

The paper analyzes the interaction of economic growth and inflation rate on accounting for both sources of capital investment and global shocks. The analysis method is a time-varying coefficient Bayesian vector autoregression (TVC-BSVAR) model applied in a quarterly data sample of the Vietnam economy over Q2/2001–Q4/2022. The global factors, including the world economic growth and the inflation rate, are the most important drivers of domestic growth and inflation rate. A greater world growth rate raises the domestic growth rate while a higher world inflation rate increases the domestic inflation rate. If the world economy falls into stagflation with declining growth and a rising inflation rate, Vietnam’s economic growth reduces while the inflation surges. The monetary and fiscal policy can be employed to recover domestic economic growth but with an associated surge in the domestic inflation rate.

Food insecurity and water management shocks in Saudi Arabia: Bayesian VAR analysis.

The existing conditions of domestic agricultural production and the resulting products will not be able to fruitfully address the increasing food demand due to the limited fertile land and water resources in Saudi Arabia. Moreover, the escalating threat of a hotter climate, the deterioration in precipitation, and harsh droughts in Saudi Arabia have reduced the predictability of water management efficiency and resulted in the exhaustion of water bodies and serious degradation of ecosystems that have directly affected agricultural systems and indirectly, food security. This study also aims to assess the impact of water efficiency on food insecurity in Saudi Arabia. The study applied the Bayesian Vector Autoregressive (BVAR) model for the reference period for the data extended from 2000-2020. Likewise, we used both impulse response functions (IRFs) and forecasting variance error decomposition (FVED) through 1000 Monte Carlo simulations according to the BVAR model to examine both the response of food insecurity to the shocks on water management efficiency used for various purposes and the decomposition of error variance in food insecurity. The results show that food insecurity was not observed throughout this study. The results of the BVAR analysis indicate that in the short run, the coefficients of water use efficiency are significant based on the Food Insecurity Multidimensional Index (FIMI). Also, the BVAR model provides a better forecast with an interdependence on water use efficiency for agricultural purposes and FIMI. Moreover, the results obtained from IRFs have shown a significant effect of water efficiency on FIMI. Water use efficiency for agriculture and industrial purposes reduces food insecurity while increasing water for services use increases food insecurity. Water use efficiency is the key factor affecting food insecurity in the short run. The results reveal that the water use efficiency shocks will decrease food insecurity. The shocks experienced by food insecurity can be predicted as self-shock over a span of ten years. Emphasis is given to the task of water management that may support food security in Saudi Arabia through implementing and enhancing the water use efficiency as an integral part of achieving the SDGs in Saudi Arabia.

Retracted: Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast

Retracted: Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast

Density forecast combinations: The real‐time dimension

AbstractEuro area real‐time density forecasts from three dynamic stochastic general equilibrium (DSGE) and three Bayesian vector autoregression (BVAR) models are compared with six combination methods over the sample 2001Q1–2019Q4. The terms information and observation lag are introduced to distinguish time shifts between data vintages and actuals used to compute model weights and compare the forecast, respectively. Bounds for finite mixture combinations are presented, allowing for benchmarking them given the models. Empirically, combinations with limited weight variation often improve upon the individual models for the output and the joint forecasts with inflation. This reflects overconfident BVAR forecasts before the Great Recession. For inflation, a BVAR model typically performs best.

A modelling framework for equity portfolio projections under different carbon price scenarios

It is recognised that climate risks (including carbon pricing policy) are a new source of risk for the financial system. We propose a modelling framework for medium-term projections of stock returns under different carbon price scenarios. First, we construct a green factor to augment the classic capital asset pricing model (CAPM) and capture a firm’s exposure to climate policy risks. Then, we project to 2040 the factors of the CAPM, conditional on different carbon price pathways, using the estimated medium-scale Bayesian vector autoregressive model (MBVAR). Finally, we project the stock returns of each firm in the portfolio. Our scenarios suggest that the impacts of a carbon price policy are not confined to the most polluting firms (mining and quarrying, transportation and storage firms) for the effect of systematic risk. Moreover, in the short term, the negative impacts of a carbon price policy are more accentuated under a disorderly than an orderly transition. In the medium term, the projections suggest that the stock market progressively adapts to new conditions and believes in the benefits of rigorous carbon policies to incentivise the transition to a low-carbon economy. The proposed toolbox may represent effective support for investors in preparing portfolios for climate transition risks in the context of uncertainty regarding the timing of the introduction of a carbon policy. For policymakers, it can help shed light on how policies to prevent or mitigate climate change effects can affect financial stability.

ANALISIS DIAGNOSTIK VARIABEL CUACA UNTUK ESTIMASI POLA CURAH HUJAN DI MEDAN MENGGUNAKAN MODEL BAYESIAN VECTOR AUTOREGRESSIVE

The territory of Indonesia is located in a strategic position in the tropics. Indonesia is in a position where the equator passes, making it vulnerable to changes in weather and climate. Big cities are increasingly facing many global challenges, so that the effects of climate change are causing urban areas to become more vulnerable to disasters. The city of Medan is one of the big cities that has been recognized as having different characteristics from the surrounding climate, which still has quite a lot of natural elements. Various community activities in Medan City can change the composition of the atmosphere which causes changes in the characteristics of the microclimate which will affect the weather and climate. Weather and climate have a dynamic relationship with other weather elements such as air humidity, air temperature and rainfall. Weather and climate patterns often do not match the pattern they should and are difficult to predict. The main elements of weather are temperature and rainfall, knowing the temperature and rainfall of an area can be used as material to describe the weather in that area. In expressing rainfall in an area, the relationship between air humidity, air temperature and wind direction and speed is very influential. Knowing the pattern of rainfall is very important to do in several activities. So that a diagnostic analysis of weather variables is needed to estimate rainfall patterns in the city of Medan using the bayesian vector autoregressive (BVAR) model. The estimation results using the Bayesian Vector Autoregressive (BVAR) model for Medan City found that the highest rainfall occurred in September at 571.87 mm and the lowest occurred in January at 54.59 mm with a method accuracy rate of 4.75% which indicates that the use of the BVAR method in estimation is very accurate

Global Demand and Supply Sentiment: Evidence From Earnings Calls*

AbstractThis paper quantifies global demand and supply conditions and compares two major global recessions: the 2009 Great Recession and the COVID‐19 pandemic. First, we compute demand and supply sentiment by applying Natural Language Processing techniques on earnings call transcripts. Second, we corroborate our sentiment measure by identifying demand and supply shocks using a structural Bayesian vector autoregression model. The results highlight sharp contrast in the size of supply and demand conditions over time and across sectors. While the Great Recession was characterized by weak demand, COVID‐19 caused sizable disruptions to both demand and supply, with varying relative importance across major sectors. Furthermore, certain sub‐sectors, such as professional and business services, internet retail, and grocery/department stores, fared better than others during the pandemic.

Application of Bayesian Vector Autoregressive Models in the Analysis of Quasi Money and Money Supply: A Case Study of Nigeria

Aims: The aim of this study is to model the relationship between Nigerian quasi money and money supply using the Bayesian Vector Autoregressive (BVAR) model.
 Study design: The study collected and analyzed monthly data from the Central Bank of Nigeria (CBN) money and credit statistics over an 8-year period (November 2015 to December 2022). The analysis utilized both Vector Autoregressive (VAR) Model and BVAR model to examine the dynamics between these variables and their implications for monetary policy.
 Methodology: The study employed the Bayesian Vector Autoregressive (BVAR) model to analyze the relationship between Nigerian quasi money and money supply. Monthly data from the Central Bank of Nigeria (CBN) over an 8-year period was collected and subjected to both Vector Autoregressive (VAR) Model and BVAR model for analysis.
 Results: The findings indicated that there is no long-run relationship between Nigerian narrow money and quasi money, but quasi money does granger cause changes in narrow money, and vice versa. This suggests a multi-directional effect between the two variables. The BVAR model consistently outperformed the VAR model in terms of higher Adjusted-R² values, indicating its stronger ability to explain the variance in the data. The BVAR model provided a more robust and accurate representation of the relationship between these variables. The model exhibited stability and the absence of heteroscedasticity in the residuals, indicating a stable relationship between the variables. The impulse response function showed an immediate impact of changes in narrow money on the overall money supply in Nigeria.
 Conclusion: This study contributes to existing knowledge by empirically examining the relationship between Nigerian narrow money and quasi money and also concluded that there existed no co-integrating relationship between narrow money and quasi money, which has important implications for effective monetary policy strategies, particularly in Nigeria.

The macroeconomic effects of exchange rate movements in a commodity-exporting developing economy

This paper empirically examines the macroeconomic effects of exchange rate movements and their transmission mechanisms (trade and financial channels) in Mongolia, a net debtor in foreign currency and a commodity-exporting developing economy, using Structural Bayesian Vector Autoregression (SBVAR) models. Our findings suggest that both the financial and trade channels of exchange rates are operative and have a notable impact at the macroeconomic level. We identify a significant financial channel, causing GDP contractions and affecting investments, particularly in sensitive sectors such as manufacturing and construction in response to DWER depreciation shocks. The traditional trade channel, driven by NEER depreciation, leads to increased net exports with pass-through to CPI. Despite these effects on key macro variables, exchange rate shocks do not substantially destabilize the economy. Foreign shocks, including federal funds rates, China's GDP, and copper prices, have a more pronounced impact. The trade channel plays a crucial role in the transmission of external demand and commodity price shocks, while the financial channel is essential in the transmission of commodity price shocks.

The influence of the Ukraine-Russia conflict on renewable and fossil energy price cycles

This study investigates the impact of the Ukraine-Russia conflict on energy cycles by extending a business cycle-based approach in the energy sector. We employ the Markov Switching Bayesian Vector Autoregressive (MS-BVAR) model to derive filtered probabilities that represent energy cycles. By utilizing the Russia Economic Policy Uncertainty Index and dummy variables derived from Google search data as conflict indicators, we conduct regression analysis and examine the generalized impulse response function to assess the effects of the conflict on energy cycles. Our research reveals notable differences in renewable and fossil energy price cycles. The renewable price cycle exhibits greater persistence, indicating a limited response to the conflict shock. In contrast, the fossil energy price cycle exhibits more significant and enduring adjustments in high- and low-volatility regimes. These findings highlight the heterogeneous impacts of the Ukraine-Russia conflict on the fossil energy market, indicating potential disruptions in supply chains and market sensitivities. Notably, fossil energy prices demonstrate higher responsiveness to the Ukraine-Russia conflict across market conditions.

Impact of Financial Factors on the Economic Cycle Dynamics in Selected European Countries

The aim of this paper was to assess the impact generated by the financial market shocks on the economic cycle in European countries. In addition to the studies from the literature, which focus more on the developed economies, this paper also considered the situation at the level of a group of emerging economies to highlight the potential differences. In this sense, it was analyzed how the shocks at the level of the banking sector, those at the level of the capital market, and those at the level of the real estate market influence the dynamics of the economic cycle. Both econometric models for the individual analyses of each state, such as the Bayesian vector autoregression model, and models at the level of groups of states, such as panel regressions, were used for the period 2007–2022. The results showed a strong connection between the dynamics of the financial system and that of the real economy. In addition, the impact of financial factors on the economic cycle tends to be much stronger and more significant in the case of developing countries, compared to developed ones. In this regard, it was recommended that fiscal and monetary policies should be coordinated to generate the expected effect on the economy.

Bayesian vector heterogeneous autoregressive modelling

The Bayesian vector autoregressive (BVAR) model with the Minnesota prior proposed by Litterman [Litterman RB. Forecasting with bayesian vector autoregressions-five years of experience. J Business Economic Statist.1986;4(1):25–38.] has been very successful in multivariate time series modelling, providing better forecasting performance. However, the conventional Minnesota prior for BVAR depends only on the latest lag; in turn, it is not suitable for multivariate long memory time series forecasting. This study extends BVAR to (possibly) high-dimensional long memory time series. To this end, we incorporate a vector heterogeneous autoregressive (VHAR) structure to accommodate long-term persistence and impose priors on distant lags as well. Our proposed Bayesian VHAR (BVHAR) models, the so-called BVHAR-S and BVHAR-L, are easy to implement by using the Normal-inverse-Wishart prior and added dummy variable approach of [Bańbura M, Giannone D, Reichlin L. Large bayesian vector auto regressions. J Appl Econom. 2010;25(1):71–92.]. A supporting simulation study also proves posterior consistency. We further apply our models to nine CBOE Volatility Indices (VIXs) and show that our BVHAR models perform best in forecasting with the narrowest forecasting region.

The Bayesian vector autoregressive model as an analysis of the government expenditure shocks while the covid-19 pandemic to macroeconomic factors

This empirical work aims to determine whether the government expenditure shocks on consumption have impacts on macroeconomic factors. This study prepares four quarterly period datasets associated with macroeconomic activities in Indonesia with 24 data started in 2017 until 2022, such as government expenditure shocks on consumption, inflation, interest rate, and gross domestic product (GDP) growth rate. This dataset is divided into two parts before and after the Covid-19 outbreak pandemic. The core findings indicate that expenditure shocks on consumption following the Covid-19 outbreak pandemic affect improving economic activity by using the Bayesian approach as the alternative model combined with the vector autoregression (VAR) model in overcoming the over-parameterization and the small dataset as the innovation of this findings. The core finding has the implication of stabilizing the unstable macroeconomic forces following the Covid-19 pandemic. It demonstrates how government expenditure shocks can be used to help the economy run appropriately and stable.

The drivers of market-based inflation expectations in the euro area and in the US

In this paper, we propose a methodology to assess the structural drivers of inflation expectations, as measured by inflation-linked swaps. To this end, we estimate a Bayesian Vector Autoregressive (BVAR) model for the euro area (EA) and the United States (US) on daily asset price movements in the two economies. Shocks are identified using sign and magnitude restrictions, taking also into account international spillovers. The inclusion of inflation expectations allows to neatly differentiate between supply and demand innovations. The results suggest that over the course of 2021–23​ inflation expectations in the US were steadily sustained by domestic demand, while in the EA they mostly reflected the role of supply shocks, and only more recently a growing strength of demand factors. Our evidence also indicates that monetary policy shocks progressively contributed in soothing inflation expectations in both jurisdictions, although with a different timing and vigour.

Measurement of the integration level between the digital economy and industry and its impact on energy consumption

The integration of the digital economy and industry is the product of economic and social development and acts as an important driving force for conserving energy and reducing emissions. This study establishes the measurement index system of the integration level between the digital economy and industry from basis, conditions, and application of integration. Taking China as the research sample, this study chose 1999–2018 as the research period. The entropy weight TOPSIS method was employed to determine the integration level between the digital economy and industry. Next, a Bayesian vector autoregressive model was utilized to quantitatively analyze the impact of China's integration level between the digital economy and industry on energy consumption. The results indicate that China's integration level between the digital economy and industry shows a fluctuating upward trend with slow growth during the study period, and still has great development potential. The improvement of China's integration level between the digital economy and industry is conducive to the improvement of industrial energy efficiency, its consumption structure and environmental protection. Among them, the integration level between the digital economy and industry contributes the most to the change in the energy consumption structure and has the lightest impact on energy efficiency. Therefore, the government might make full use of green financial services to promote the integrated development of the digital economy and industry so as to achieve energy transformation and sustainable development.

Markov-Switching Bayesian Vector Autoregression Model in Mortality Forecasting

Markov-Switching Bayesian Vector Autoregression Model in Mortality Forecasting

Investigating the asymmetric effect of income inequality on financial fragility in South Africa and selected emerging markets: a Bayesian approach with hierarchical priors

PurposeThis study aims to test the validity of the Rajan theory in South Africa and other selected emerging markets (Chile, Peru and Brazil) during the period 1975–2019.Design/methodology/approachIn this study, the researchers used time-series data to estimate a Bayesian Vector Autoregression (BVAR) model with hierarchical priors. The BVAR technique has the advantage of being able to accommodate a wide cross-section of variables without running out of degrees of freedom. It is also able to deal with dense parameterization by imposing structure on model coefficients via prior information and optimal choice of the degree of formativeness.FindingsThe results for all countries except Peru confirmed the Rajan hypotheses, indicating that inequality contributes to high indebtedness, resulting in financial fragility. However, for Peru, this study finds it contradicts the theory. This study controlled for monetary policy shock and found the results differing country-specific.Originality/valueThe findings suggest that an escalating level of inequality leads to financial fragility, which implies that policymakers ought to be cautious of excessive inequality when endeavouring to contain the risk of financial fragility, by implementing sound structural reform policies that aim to attract investments consistent with job creation, development and growth in these countries. Policymakers should also be cautious when implementing policy tools (redistributive policies, a sound monetary policy), as they seem to increase the risk of excessive credit growth and financial fragility, and they need to treat income inequality as an important factor relevant to macroeconomic aggregates and financial fragility.