Bayesian VAR Research Articles

Evaluating Enteric Fermentation-Driven Environmental Kuznets Curve Dynamics: A Bayesian Vector Autoregression Comparative Study of the EU and Least Developed Countries

Global warming and climate change, primarily driven by human activities, with agriculture playing a significant role, have become central topics of scientific research. Livestock production, especially enteric fermentation, is a major source of greenhouse gas emissions, making it a focal point for both climate change adaptation and mitigation strategies. Both the European Union (EU) and Least Developed Countries (LDCs) are highly dependent on agriculture, particularly livestock, which plays a key role in their economic growth. In developing countries, livestock systems are evolving rapidly due to various factors, while in the EU, the livestock sector remains economically and socially significant, representing 36% of total agricultural activity. This study explores the environmental impact of enteric fermentation in livestock production, alongside the economic value it generates in both the EU and LDCs. The analysis utilizes a Bayesian Vector Autoregression (BVAR) methodology, which provides a more robust performance compared to traditional models like Vector Autoregression (VAR) and the Vector-error Correction Model (VECM). This research identifies significant relationships between the variables studied, with structural breaks quantified to reflect the impact of initiatives undertaken in both regions. Interestingly, the results challenge the environmental Kuznets curve, which hypothesizes an inverted U-shaped relationship between economic growth and environmental degradation, as proposed by Stern. This suggests that stronger economic incentives may be necessary to enhance policy effectiveness and promote eco-efficiency. The distinctive characteristics of livestock production in the EU and LDCs should be carefully considered when shaping agricultural policies, with a strong emphasis on farmer education as a critical factor for success. Additionally, corporate management practices must be tailored to address the unique needs, strengths, and challenges of livestock businesses in these two diverse regions.

Large quarterly Bayesian vector autoregression model for the modern Russian economy

It is often difficult to choose the necessary variables when we are forecasting the economic dynamics. On the one hand, vector autoregression (VAR) models can solve this problem by taking into account a sufficiently large number of variables. However, on the other hand, excessive parametrization of such models is not always justified, because it is often possible to choose such a small combination of variables the predictive power of which will be no worse than unlimited VARs with a large number of variables and lags. Bayesian methods help to solve this problem by introducing a priori restrictions on the VAR coefficients. The main goal of this work is to build a Large quarterly Bayesian vector autoregression (QBVAR) for the modern Russian economy. The hypotheses of the study: 1) BVAR(p) models reveal their maximum potential with a small number of variables, but with a large number of lags, 2) The Minnesota prior distribution is not always the best option for the modern Russian economy in the BVAR(p) model. As a result of the analysis, it turned out that for a number of the most important macroeconomic variables (GDP, inflation, key rate, unemployment, wages), Normal-Flat / Wishart-type priors turned out to be the optimal. In case of 29 input variables and two lags, the predictive power of the QBVAR(2) model is worse than the equivalent frequency VAR(2) or VECM(2). However, when it was possible to find the optimal combinations of variables, QBVAR(p) turned to be several times more accurate than its analogous frequency VAR(p) for all important macro indicators. In addition, the stiffness parameters of the considered a priori distributions decreases with increasing lags entering the model, i. e. the higher the order of the model with the minimum optimal set of variables, the tighter the distribution is necessary to obtain more accurate predictions. For the Russian realities, it is necessary to use very tight priors, 6–8 variables and 9–12 lags in a quarterly representation.

Inflation forecasting in turbulent times

AbstractIn the recent years many countries were hit by a series of macroeconomic shocks, most notably as a consequence of the COVID-19 pandemic and Russia’s invasion in Ukraine, raising inflation rates to multi-decade highs and suspending well-documented macroeconomic relationships. To capture these tail events, we propose a mixed-frequency Bayesian vector autoregressive (BVAR) model with Student t-distributed innovations or with stochastic volatility. Whereas inflation, industrial production, as well as oil and gas prices are available at monthly frequencies, real gross domestic product (GDP) is observed at a quarterly frequency. Thus, we apply a mixed-frequency setup using the forward-filtering–backward-sampling algorithm to generate monthly real GDP growth rates. We forecast inflation in those euro area countries that extensively import energy from Russia and therefore have been heavily exposed to the recent oil and gas price shocks. To measure the forecast performance of the mixed-frequency BVAR model, we compare our inflation forecasts with those generated by a battery of competing inflation forecasting models. The proposed BVAR models dominate the competition for all countries in terms of the log predictive density score.

Forecasting with the Optimal Choice of Hyperparameters for Bayesian VARs

Forecasting with the Optimal Choice of Hyperparameters for Bayesian VARs

Bayesian Vector Autoregression Analysis of Chinese Coal-Fired Thermal Power Plants

Considering the dataset of information related to Chinese coal-fired thermal power plants during the 2005–2017 period, we initially investigated the orthogonalized response of the carbon emission to energy consumption and power generation by using Bayesian vector autoregressions and feedback solutions for impulse control technology. The results showed that the effects of energy consumption and power generation on carbon emissions were significant. The Chinese government has launched a program aimed at curbing carbon emission peaks and neutralizing or decreasing carbon emissions. The causal relationship concludes that China still needs further investment in emission abatement, improvement related to the level of openness to the outside world, and the strengthening of the construction of green zones for industrial transfer to mitigate carbon emissions.

Labour at risk

We propose a Bayesian VAR model with stochastic volatility and time varying skewness to estimate the degree of labour at risk in the euro area and in the United States. We model the asymmetry of the shocks to changes in the unemployment rate as a function of real activity and financial risk factors. We find that the conditional distribution of the changes in the unemployment rate displays time-varying volatility and skewness, with peaks coinciding with the Global Financial Crisis and the COVID-19 pandemic, in both areas. We also take advantage of the multivariate nature of our parametric model to measure the joint risk of large increases in the unemployment rate together with large annual rates of inflation, a proxy for “stagflation” risks. The model captures an increase of the risk of stagflation with the surge in inflation that followed the recent energy crisis in 2022. Nevertheless, stagflation risks were contained by the resilient performance of the labour market in both areas. Labour at risk is therefore important for the assessment of the inflation-unemployment trade-off.

A Bayesian vector-autoregressive application with time-varying parameters on the monetary shocks–production network nexus

ABSTRACT Transmission channels from monetary shocks might be identified by studying the features of the production network. The main aim of this paper is to provide insights about the role of production network into the propagation of monetary policy shocks in G7 economies. Time-varying Bayesian vector-autoregressions were built to compute impulse response functions of output to monetary policy shocks in these countries. Panel Auto-Regressive Distributed Lag Bound Approach based on Mean-Group estimator was used to assess the long and short-run connections between production network structure and various shocks associated to monetary policy in the period 2000–2018 and during the Great Recession (2007–2009). The results show that upstreamness is more significant than downstremness in the period 2000–2018, while the financial sector significantly contributed to the spread of various monetary shocks during the Great Recession.

Public debt determinants: A time-varying analysis of core and peripheral Euro area countries

This study employs a Bayesian Interacted Panel VAR model to estimate time-varying Generalized Forecast Error Variance Decomposition, analyzing how key determinants affect debt in Core and Peripheral Euro Area countries. Results highlight varying effects of determinants across periods and subgroups.

Analysing Economic Growth and Environmental Quality: A Classical and Bayesian Approach

Abstract This empirical study investigates the intricate relationship between the ecological environment and economic growth within the context of Zhejiang Province, China - a region characterised by its rapid urbanisation and significant economic development. By analysing data spanning from 2011 to 2020, the research applies the Environmental Kuznets Curve (EKC) model, which hypothesises an inverted U-shaped relationship between environmental degradation and economic growth, within both classical and Bayesian statistical frameworks to examine the province’s per capita GDP. Findings from both statistical approaches reveal a distinct correlation between economic progression and environmental conditions, underscoring the Environmental Kuznets Curve hypothesis. Additionally, this study conducts a comparative analysis between Vector Autoregression (VAR) and Bayesian Vector Autoregression (BVAR) models to evaluate their predictive capabilities concerning the interplay between ecological health and economic advancement in Zhejiang. The BVAR model, with its incorporation of Bayesian statistics, demonstrates superior forecasting precision, providing valuable insights into the dynamics governing the relationship between economic growth and the ecological environment in Zhejiang Province.

Impact of Globalization on Macroeconomic Dynamics Using a Time-varying Bayesian VAR

Impact of Globalization on Macroeconomic Dynamics Using a Time-varying Bayesian VAR

Fiscal multipliers in recession and expansion. An analysis for the Italian regions

This paper estimates the multipliers of two types of government spending in the 20 Italian administrative regions throughout 1960–2017, distinguishing between phases of expansion and recession. We derive regime- and region-specific multipliers through a nonlinear Bayesian heterogeneous panel threshold VAR model and provide a wide-ranging sensitivity analysis. We find that both government consumption and government investment multipliers are higher in recession than in expansion. In almost every region, government investment multipliers exceed unity in recession phases and are generally higher than their government consumption counterparts regardless of the business cycle. An exploratory analysis of the region-specific multipliers suggests that the difference between the region-specific multipliers in recession and expansion is positively associated with structural labour slack and negatively associated with trade openness. Factors related to the quality of local institutions, such as corruption and the relative size of the informal economy, also have a negative impact.

Resilience of developing economies to external shocks: empirical evidence from CEMAC countries

PurposeThis study aims to investigate the effects of three significant events – the 1994 CFA currency depreciation, the 2008 Global Financial Crisis (GFC), and instances of political coups – on the relationships between FDI inflow, economic growth, and governance within the Central African Economic and Monetary Community (CEMAC) countries. It seeks to evaluate how these events influence the linkages between FDI, economic growth, and governance, to aid the understanding of responses to external shocks and internal political disruptions.Design/methodology/approachThe study employs a panel Vector Autoregression (VAR) analysis using data from 1990 to 2019 by exploring the dynamic relationships among FDI inflow, economic growth, and aggregate governance indicators within the CEMAC sub-region. The analysis was conducted utilizing the EViews software package, facilitating robust examination through the introduction of the Bayesian VAR to facilitate the interpretation of parameters and the data.FindingsThe results indicate that, contrary to initial hypotheses, growth and governance do not emerge as determinants for attracting FDI within the CEMAC sub-region. However, governance stands out as a crucial determining factor for economic growth. Furthermore, the study suggests that the 1994 CFA currency depreciation, the 2008 GFC, and instances of political coups did not significantly impact FDI, growth, and governance within these countries. Despite the potential vulnerability of the CEMAC countries to external shocks, the effects of these events on the dynamics of FDI, economic growth, and governance were not apparent. Notably, political instability, as evidenced by coups, emerges as a significant factor shaping the interactions between FDI, growth, and governance in CEMAC countries.Research limitations/implicationsThese findings have significant implications for policymakers and stakeholders in the CEMAC countries. Understanding that governance has a central role in driving economic growth places great importance of prioritizing governance reforms to foster sustainable development. Moreover, the identification of political instability as a key determinant affecting the relationships between FDI, growth, and governance emphasizes the need for political stability and effective governance structures to attract and sustain FDI inflows as well as foster economic growth.Originality/valueThis study contributes to the existing literature by offering insights into the linkages between FDI, economic growth, governance, and external shocks within the CEMAC sub-region. By examining the specific impacts of the 1994 CFA currency depreciation, the 2008 GFC, and political coups on these dynamics, the study provides original perspectives on the resilience of CEMAC countries to external and internal disruptions.

Energy shocks in the Euro area: Disentangling the pass-through from oil and gas prices to inflation

We develop a Bayesian Structural VAR model to study the relationship between different energy shocks and inflation dynamics in Europe. Specifically, we model the endogenous transmission from shocks identified by the global market of crude oil and the European natural gas market to two target macroeconomic variables, i.e. inflation expectations and realized headline inflation rate. Our results demonstrate that, since the post-pandemic recovery, inflation in the Euro area is mostly driven by energy price shocks and aggregate supply factors. In particular, the high peaks of the Eurozone inflation are mostly associated with natural gas supply shocks.

The macroeconomic impact of euro area labor market reforms: evidence from a narrative panel VAR

Using quarterly narrative evidence, the paper examines the macro-economic impact of reforms of unemployment benefits (UB) and employment protection legislation (EPL) in the euro area from a Bayesian narrative panel VAR. The approach complements micro-econometric approaches by assessing aggregate effects and aligning short- and medium-term effects in a unified framework. I find that liberalizing reforms of UB and regular contract EPL resulted in temporary wage declines and persistent increases in economic activity and employment.

Uncertainty, labour force participation and job search

This study examines the impact of labour force participation (LFP) on search behaviour and unemployment in response to uncertainty shocks. Using a Bayesian vector autoregression model, this study shows that an increase in uncertainty raises unemployment, lowers inflation and decreases LFP and search intensity. Then, a New Keynesian model that incorporates endogenous LFP and variable search intensity is constructed. Results show that both search intensity and participation are procyclical, which suggests a discouragement effect. However, with habit formation, the wealth effect outweighs the discouragement effect and induces an increase in LFP, resulting in a more significant decline in aggregate search intensity due to the expanded searcher pool. In the exogenous LFP model, search intensity is countercyclical, which dampens the unemployment rate. In the endogenous LFP model, the increased entry of non-participants cancels out the countercyclical search intensity of unemployed workers due to the large number of searchers.

South African inflation response to fiscal policy shocks

ABSTRACT The research employs Bayesian Vector Autoregressions with hierarchical priors to analyze the intricate economic implications of fiscal policy shocks on inflation, monetary policy, and fiscal authorities in the context of South Africa. The study explores data spanning from 1979 to 2022. Contrary to conventional economic theories, our analysis demonstrates that unexpected increases in national government expenditure led to counterintuitive initial decreases in inflation. This highlights the complexity of inflation dynamics and challenges existing paradigms. Moreover, the lagged response of inflation to changes in government revenue emphasizes the role of inflation expectations and market dynamics. Clear communication by fiscal authorities is crucial for shaping these expectations and understanding their impact on inflation.

A Macroeconomic View on Banks’ Deposits after Monetary Interventions

Abstract We study the evolution of bank deposits in response to monetary policy shocks. The objective is to assess whether a significant deposit channel, that is, an outflow of deposits when monetary conditions tighten (Drechsler et al. 2017), emerges at the macro level. The analysis relies on the estimation of a large Bayesian structural VAR for the USA, and monetary policy shocks are identified with Arias et al. (2019)’s method. We find some elements of the deposit channel, but our results suggest that its relevance for the transmission of monetary policy at the macro level is limited.

China's monetary policy framework and global commodity prices

This paper examines the effects of China's monetary policy on global commodity prices over the quarterly period 1996:Q1–2021:Q4. Using a Bayesian Structural VAR model, we evaluate the impact of interest rate shocks (as a price rule) and shocks to the monetary aggregate (M2) (as a quantity rule) on those commodity prices. Our main findings are fourfold. First, a positive interest rate shock has a negative and persistent effect on commodity prices, with beverages and metals commodity prices falling the most in response to such shock. Second, a positive shock to the growth rate of M2 has a strong impact on the prices of non-fuel commodities, agricultural raw materials, and metals. Still, the highly volatile food and fuel (energy) commodity prices are less affected. Third, while both the growth rate of M2 and the interest rate seem relevant macroeconomic stabilizers, the quantity instrument appears more effective than the price instrument in explaining commodity prices. Finally, while interest rate hikes are linked with a persistent rise in world uncertainty, monetary expansions lead to a long-lived fall in this variable. All in all, our study provides new evidence about the impact of China's monetary policy on global commodity prices using a rich dataset and an econometric setup that accounts for uncertainty.

Energy uncertainty, geopolitical conflict, and militarization matters for Renewable and non-renewable energy development: Perspectives from G7 economies

Conflict can increase the risk profile of fossil fuel-oriented projects due to disruptions in the energy supply chain and potentially impact the clean energy transition in G7 economies. Thus, the paper investigates the unexplored connections between energy uncertainty, geopolitical conflict, and the militarization index with renewable and non-renewable energy in G7 (1997–2022). The application of advanced econometric algorithms, notably the Methods of Moment Quantile Regression, reveals that energy uncertainty has a positive effect on renewable energy consumption and a negative effect on non-renewable energy consumption. Furthermore, the current geopolitical landscape poses unfavorable conditions for both the expansion of renewable energy usage and the utilization of non-renewable energy sources. The global militarism index relies on non-renewable rather than renewable energy sources. The study employed the Impulse Response Function using Bayesian vector autoregression. The study discovered that variations in energy uncertainty initially enhance the usage of renewable energy but ultimately have adverse consequences for renewable and non-renewable energy consumption. Geopolitical risk unfavorably affects both types of energy, but trade positively influences the growth of renewable energy use in G7. The study recommends transparent economic strategies as essential for governments to reduce uncertainty and facilitate a transition to clean energy.

On the forecasting performance of small-scale DGSE models: a Monte Carlo evaluation and an application to UK

This article investigates the forecasting performance of a new small-scale dynamic stochastic general equilibrium (DSGE) model. To this end, this article first conducts a Monte Carlo study for the evaluation of the new model against another DSGE model , a standard vector autoregression (VAR), and a Bayesian VAR, using root mean square error and directional accuracy measures. An empirical application to UK quarterly data of output gap, inflation, and interest rates over the period 1986–2019 is also carried out for point and density forecasts. The empirical findings unveil a better performance of the new model for directional accuracy than for root mean square error, while density forecasts indicate no statistical differences among the new model and the VAR models.