Bayesian VAR Research Articles

Local projections vs. VARs: Lessons from thousands of DGPs

We conduct a simulation study of Local Projection (LP) and Vector Autoregression (VAR) estimators of structural impulse responses across thousands of data generating processes, designed to mimic the properties of the universe of U.S. macroeconomic data. Our analysis considers various identification schemes and several variants of LP and VAR estimators, employing bias correction, shrinkage, or model averaging. A clear bias–variance trade-off emerges: LP estimators have lower bias than VAR estimators, but they also have substantially higher variance at intermediate and long horizons. Bias-corrected LP is the preferred method if and only if the researcher overwhelmingly prioritizes bias. For researchers who also care about precision, VAR methods are the most attractive—Bayesian VARs at short and long horizons, and least-squares VARs at intermediate and long horizons.

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.

Have credit card services become important to monetary aggregation? An application of sign restricted Bayesian VAR

ABSTRACT The purpose of this paper is to estimate the relationship among a primary set of economic variables, including two types of monetary aggregates: simple sum M2 and credit-card-augmented Divisia inside money services. The importance of that comparison has grown as the use of credit cards in purchase transactions has expanded. The data period includes the Great Recession, which was heavily associated with finance and thereby especially relevant to this study. The basic methodology in this paper is VAR-Sign Restrictions estimation. VAR is a well-known method to analyze inter-dependency among economic variables. By applying VAR-Sign Restrictions, we analyze how economic variables behave, positively or negatively, toward differently defined shocks. Imposing signs on the direction of economic variable responses to shocks is based on economic prior beliefs, using Bayesian estimation. Our results provide deeper insights into the relative merits of the two types of monetary aggregates as indicators.

The link between electricity consumption and stock market during the pandemic in Türkiye: a novel high-frequency approach

This article examines the relationship between electricity consumption and the stock market in the Turkish economy during the COVID-19 pandemic. A novel high-frequency model is used, incorporating the hourly energy consumption and Borsa Istanbul (BIST) National stock market index variables. To determine the effect of electricity consumption on the stock market index and vice versa, a high-frequency VAR-based spillover approach, time-varying Granger causality, and time-varying Bayesian VAR analysis are employed. The findings reveal a positive and weak relationship between electricity consumption and the stock market but it has a time-varying nature in an emerging market context in the post-COVID-19 period in the Turkish economy.

A note on the dynamic effects of supply and demand shocks in the crude oil market

ABSTRACT In this paper, we investigate whether key relations in the crude oil market have been stable over time. This is done by estimating hybrid time-varying parameter structural Bayesian VAR models using monthly data ranging from February 1973 to May 2023. Model selection suggests that while stochastic volatility is preferred over homoscedasticity, the dynamics of the model are best described by constant parameters in all equations.

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

Nexus Between Asset Class Volatility and the Output Gap in Nigeria: A Bayesian Var Approach

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">Excessive volatility in financial markets can disrupt economic activity, affect investor and consumer confidence, and potentially lead to financial crises in an economy. Due to this backdrop, this study examined the link between asset class volatility and the output gap in Nigeria. The asset classes were categorized into stock, crude, gold, and bitcoin. The study adopted the GARCH and Bayesian VAR approach and found that all share index has an initial negative impulse with output gap while other asset classes have a positive impulse on output gap. The outcome of this study revealed to both policymakers and economists the potential risks and vulnerabilities of asset class volatility in the economy. Based on this result, recommendations are made amongst which is the strengthening of the Nigerian stock market to help with the inflationary pressures this is because the Nigerian stock market hurt the output gap also, the government should prioritize investing in crude, gold, and bitcoin to push the actual output to full capacity, which brings about employment.</span></p>

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.

Economic Policy Uncertainty and Equity Fund Flows to India: A Bayesian Approach

We compare the impacts of economic policy uncertainty (EPU) and global economic policy uncertainty (GEPU)-related shocks on equity fund flows (EFFs) to India using a Bayesian vector autoregression approach. We find that (a) Indian EPU and GEPU are strongly and negatively related to EFF; (b) EFF are more sensitive to GEPU relative to Indian EPU; (c) evidence of trend-chasing behaviour by fund managers in India; and (d) GEPU is an important factor for forecasting returns on the Bombay Stock Exchange. Taken together, our findings indicate that EPU is important to understanding equity allocation decisions and returns in India. JEL Codes: F21, F39, G11

Investigating unemployment hysteresis in South Africa

AbstractThis paper investigates hysteresis in South Africa's unemployment. First, we test the presence of hysteresis in unemployment using traditional stationarity tests and non‐linear transformation methods to identify two further characteristics of hysteresis, namely, remanence and selective memory. In the second part of the paper, we estimate a simple insider–outsider model using a Bayesian vector autoregression methodology to identify the shocks driving unemployment dynamics. The main finding is that mark‐up shocks and negative productivity shocks are the main drivers of unemployment, with demand shocks playing a secondary role. Nominal wages are not responsive to real shocks and are an important component of inflation. These results point to the difficulty of absorbing the current level of unemployment without a significant increase in the flexibility of goods and labour markets. At the same time, the evidence suggests that, if reforms are being implemented, demand policies can play a significant role in improving employment and growth, reversing the structural unemployment evident in the data.

Variational Inference for Large Bayesian Vector Autoregressions

We propose a novel variational Bayes approach to estimate high-dimensional Vector Autoregressive (VAR) models with hierarchical shrinkage priors. Our approach does not rely on a conventional structural representation of the parameter space for posterior inference. Instead, we elicit hierarchical shrinkage priors directly on the matrix of regression coefficients so that (a) the prior structure maps into posterior inference on the reduced-form transition matrix and (b) posterior estimates are more robust to variables permutation. An extensive simulation study provides evidence that our approach compares favorably against existing linear and nonlinear Markov chain Monte Carlo and variational Bayes methods. We investigate the statistical and economic value of the forecasts from our variational inference approach for a mean-variance investor allocating her wealth to different industry portfolios. The results show that more accurate estimates translate into substantial out-of-sample gains across hierarchical shrinkage priors and model dimensions.

The Effects of the Pandemic on Households' Financial Savings: A Bayesian Structural VAR Analysis

The Effects of the Pandemic on Households' Financial Savings: A Bayesian Structural VAR Analysis

Asymmetries in the Transmission of Monetary Policy Shocks Over the Business Cycle: A Bayesian Quantile Factor Augmented VAR

Asymmetries in the Transmission of Monetary Policy Shocks Over the Business Cycle: A Bayesian Quantile Factor Augmented VAR

Variational Inference for Bayesian Panel VAR Models

Variational Inference for Bayesian Panel VAR Models

Applied Forecasting of the US Economy with Covid Volatility in Large Bayesian VARs

Applied Forecasting of the US Economy with Covid Volatility in Large Bayesian VARs

Fiscal and Monetary Policies Interaction in Crisis: An Insight Into Japan

In this study, we explore the interaction between fiscal and monetary policies in Japan over a period including the Global Financial Crisis (GFC) and a phase of the Covid-19 pandemic under the presupposition that policy regimes are fixed and there are random switches between the two regimes. Using the Regime switching model, the results show that fiscal policy is more active in Japan than monetary policy, especially during the GFC and the Covid-19 pandemic. By using the Bayesian VAR model, we continue to further analyze the response of macroeconomic factors to an expansionary fiscal policy to verify the above findings once again. The results indicate that government expenditure helps boost the economy in the short-run. The positive impact of a government expenditure shock on GDP, PPI, and Investment lasts about three to four quarters, while private consumption only increases in one next quarter. These positive responses can increase the inflation rate and make the local currency overvalued, reflected in the increase in the effective exchange rate. A fiscal policy shock does not have much impact on the monetary policy, specifically, the money supply and interest rates of long-term government bonds still tended to decrease as expected by Japan. This is the basis for studying policy implications for some countries pursuing similar policies to Japan. JEL Classification: C23, C54, E42, E62, E63

한국의 에너지 소비와 경제성장의 탈동조화에 대한 분석

Purpose - This study analyzed the decoupling phenomenon between energy consumption and economic growth in Korea from 1990 to 2021. The main purpose of this study is to suggest policy implications for achieving a low-carbon society and decoupling that Korea must move forward in the face of the climate change crisis.
 Design/methodology/approach - This study investigated the relationship between energy consumption and economic growth by energy source and sector using the energy-EKC (EEKC) hypothesis which included the energy consumption on the traditional Environmental Kuznets Curve (EKC), and the impulse response function (IRF) model based on Bayesian vector auto-regression (BVAR).
 Findings - During the analysis period, the trend of decoupling of energy consumption and economic growth in Korea is confirmed starting from 1996. However, the decoupling tendency appeared differently depending on the differences in energy consumption by sources and fields. The results of the IRF model using data on energy consumption by source showed that the impact of GDP and renewable energy consumption resulted in an increase in energy consumption of bio and waste, but a decrease in energy consumption by sources, and the impact of trade dependence was found to increase the consumption of petroleum products.
 Research implications or Originality - According to the main results, efficient distribution by existing energy source is required through expansion of development of not only renewable energy but also alternative energy. Additionally, in order to increase the effectiveness of existing energy policies to achieve carbon neutrality, more detailed strategies by source and sector of energy consumption are needed.

FORECASTING INBOUND TOURISM FLOW TO TÜRKİYE: BVAR APPROACH

The tourism sector is a significant revenue and growth source for Türkiye’s economy due to its foreign exchange provision and employment creation capacity and its connections with other sectors. Because of its increasing share and the potential it promises, it is expected that the sector will gain further importance in the following years. However, the growth potential depends on many factors, which are internal and external, as well as economic and social. Recognizing these factors as given, it is important to obtain realistic tourism demand forecasts for both the intra-sector players and political decision-makers. In this study, such a forecasting practice has been attempted. To this end, by using the Bayesian VAR method, forecasts obtained for the 2016–2023 period for the five most tourist sending countries to Türkiye and evaluation of the forecast success has been made depending on various criteria. Forecasts reveal that it is difficult to reach the government’s target of 60 million tourists in 2023. However, it is important not to ignore the impact of the COVID-19 pandemic, whose devastating impact has not yet been fully compensated, and the loss of purchasing power experienced all over the world.