Vector Autoregression Research Articles

Bank fundamental dynamics: the role of optimal bank management

Purpose This study aims to investigate the dynamic relationships between profitability, credit risk, liquidity risk and capital in Indonesian banking industry. Design/methodology/approach The authors use a panel vector autoregression model that incorporates macroeconomic variables: growth, interest rate, foreign exchange. The analysis is based on a monthly panel data set of 88 banks spanning from January 2012 to September 2021, which comprises 10,296 bank-month observations. Findings Our key findings highlight (i) permanent credit cost and liquidity cost pass through practices, (ii) complementary function of liquidity and capital, (iii) earning management motivated asset write off and (iv) credit risk-liquidity risk neutrality. In addition, the authors observe that the banks demonstrated resilience to macroeconomic shocks. Research limitations/implications Our study have shown some interesting dynamic patterns of fundamentals; nevertheless, unified theoretical underpinning of the process is still unavailable. This should be an important future reasearch avenue. Practical implications The study brings significant implications for regulatory and supervisory practices aimed at enhancing the financial stability of banks. Originality/value We conduct estimation of Indonesian banks system in dynamic perspective and perform impulses responses.

Sanctions as a non-monetary factor of inflation in the conduct of monetary policy

Aim. To determine the degree of influence of the sanctions policy on the inflation dynamics of Russia within the framework of the monetary policy.Objectives. To review the current trends of sanctions policy as a non-monetary factor of inflation; to assess the degree of impact of sanctions on the inflation rate in Russia, taking into account a number of other factors on the basis of macroeconometric modeling in the short term; to substantiate the recommendations for taking into account non-monetary factors of inflation for the implemented monetary policy.Methods. To analyze the current state of sanctions policy as a non-monetary factor of inflation, the study applies comparative analysis and econometric modeling based on the vector autoregression model.Results. On the basis of econometric modeling of the impact of sanctions on inflation dynamics, based on the data of Rosstat and Investing.com on the inflation rate, ruble/US dollar exchange rate, RTS index, oil prices from 2000 to 2023, we obtain the following results. Sanctions as a non-monetary factor has a significant impact on inflation dynamics. In turn, oil prices do not affect inflation dynamics in the short run.Conclusions. Sanctions should be taken into account in the formation and forecasting of inflation. Oil prices do not have a significant impact on inflation, but it is important to take into account the volatility of the oil market when forecasting inflation dynamics. The struggle of the Central Bank of the Russian Federation (RF) with non-monetary inflation by monetary methods does not always lead only to a positive result. Therefore, the control of non-monetary factors of inflation should be carried out in the comprehensive work of macroeconomic policy, not only monetary policy.

Asymmetric connectedness between conventional and Islamic cryptocurrencies: Evidence from good and bad volatility spillovers

This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.

Comparing sin and ethical stocks: a spillover dynamics perspective

ABSTRACT We delve into the comparison between sin and ethical stocks through a spillover dynamics analysis using a quantile vector autoregression framework. The findings reveal that: (1) Sin and ethical stocks exhibit strong connectivity, with total spillovers being notably asymmetric under different market conditions. (2) There is a consistent pattern of alternating roles between transmitter and receiver for both sin and ethical stocks across various quantiles and time. (3) Ethical stocks consistently show dominance in transmitting return spillovers, and the COVID-19 pandemic has little influence on spillover dynamics for both sin and ethical stocks.

Determinants of Economic Growth in the Republic of Kosovo

Abstract This paper examines the factors influencing Kosovo's economic growth from 2009 to 2022, specifically investigating the relationship between export, capital formation, consumption, and economic growth using co-integration analysis and the Vector Autoregressive Model (VAR). The findings indicate that exports of goods and services, as well as household consumption, negatively affect economic growth. Conversely, gross capital formation positively impacts economic growth. The study underscores the complexity of economic growth, highlighting the varied significance of different determinants in different contexts. Key findings reveal that while export and gross capital formation are significant contributors to economic growth, household consumption shows an insignificant relationship to GDP. This research contributes to the ongoing debate on the critical factors influencing economic growth, providing empirical evidence from the context of Kosovo and enhancing our understanding of these dynamics, thus offering new insights for policymakers.

Comparative Analysis of Univariate SARIMA and Multivariate VAR Models for Time Series Forecasting: A Case Study of Climate Variables in Ninahvah City, Iraq

This study involves a comparison between the application of the univariate SARIMA model and the utilization of VAR methods (vector autoregressive models) for multivariate time series analysis. The analysis is conducted using three-time series variables derived from data representing the monthly average of Humidity (H), Rainfall (R), and Temperature (T) in Ninahvah City, Iraq. Both univariate and multivariate time series approaches are employed to model these series. The paper also outlines the implementation of vector autoregressive, structural vector autoregressive, and structural vector error correction models using the 'vars' package. Additionally, it provides functions for diagnostic testing, estimation of constrained models, prediction, causality analysis, impulse response analysis, and forecast error variance decomposition. Furthermore, it introduces three fundamental functions, VAR, SVAR, and SVEC, for estimating these models. The comparison between the methods is based on evaluating the mean error produced by each approach. The findings of the study indicate that univariate linear stationary methods outperform multivariate models. The analysis of the data was carried out using the R software platform. The primary objective is to assess the performance of univariate and multivariate time series models in handling the given data. The research gap lies in the need for a comparative evaluation of SARIMA and VAR methods for time series analysis in the context of monthly environmental variables. These models were chosen due to their effectiveness in capturing temporal dependencies and interactions among multiple variables in time series data, providing a comprehensive analysis of climatic patterns in Ninahvah City, Iraq. The study aims to address the research gap by comparing these models and justifying their selection based on their capabilities to analyze the specified time series data.

Global Supply Chain Disruptions, Commodity Price Shocks and Inflation in Japan

In recent years, the concurrent occurrences of the COVID-19 pandemic and the Russian invasion of Ukraine have led to global disruptions in supply chains and a surge in commodity prices. Major advanced economies have experienced an increase in inflation rates and a decline in economic activity. Against this economic backdrop, this paper aims to address the strained condition of supply chains by employing the Global Supply Chain Pressure Index (GSCPI) alongside commodity price indices. Furthermore, it seeks to examine how fluctuations in these variables affect Japan’s macroeconomic landscape through local projection and structural Vector Autoregression (VAR). In the local projection analysis, it has been observed that the strain on supply chains induces sustained increases in consumer and producer prices, further contributing to long-term declines in production. Within the structural VAR model framework, the identification of structural shocks is conducted through sign restrictions, affirming the significance of supply chain shocks and energy and food price shocks as crucial factors contributing to recent price increases. This confirmation is supported by variance decomposition and historical decomposition analyses.

Statistical investigation on the relationship between climate change, food availability, agricultural productivity, and economic expansion

This study examined the connections between Benin's economic expansion, food production, agricultural productivity, and climate change. Using yearly statistics between 1961 and 2021, and R software version 4.2.2, we aim to: (1) Analyze how agricultural added value affects economic expansion; (2) analyze the effects of food production and temperature lagged values on economic growth; (3) investigate the different causality relationships between food production, temperature variation, agricultural added value and economic growth. To achieve these goals, statistical and econometric techniques such as Autoregressive Distributed Lags (ARDL) and the Toda-Yamamoto Granger causality framework were employed. The ARDL model verifies that there is a positive correlation between economic growth and the added value of agriculture based on empirical data. In addition, the Vector Autoregressive (VAR) model highlights the favorable impact of lagged food production values and the adverse effect of temperature fluctuations on economic growth. Granger causality analysis, employing the Toda-Yamamoto approach, unveils unidirectional links between food production and economic growth, as well as between temperature variation and agricultural added value. Interestingly, the study comes to the conclusion that there are no direct causal links between economic expansion and agricultural growth or between economic growth and temperature variance. Notably, bidirectional causality is established between livestock production and both economic growth and agricultural added value. These insights have significant implications for understanding climate change impacts on agriculture and suggest the need for adapted strategies to mitigate climate effects. Future research could focus on evaluating existing policies, exploring social and economic impacts, investigating market dynamics, and utilizing integrated assessment modeling to inform decision-making and foster sustainable economic growth in Benin's agricultural sector.

UTJECAJ ODABRANIH MAKROEKONOMSKIH VARIJABLI NA CIJENE STAMBENIH OBJEKATA U HRVATSKOJ

Recognizing the macroeconomic determinants that have a statistically significant influence on the formation of house prices is in the interest of the general public due to its informative effect on various interest groups involved in housing construction. Thus, real estate buyers, investors, as well as local government and self-government units should be interested in this information because monitoring of standard macroeconomic variables can provide conclusions about the possible development of residential property prices. Previously conducted research can be classified into four distinctive categories depending on what they take as determining variables: microelements specific to individual micro location, the impact of the COVID-19 pandemic, standard macroeconomic variables, or tourism development. The aim of this study is to analyze the influence of selected macroeconomic variables on residential property prices in Croatia. This study employs Autometrics, i.e. an automatic computer implementation of general-to-specific VAR (vector autoregressive) modelling framework and quarterly data on real GDP, domestic credit, consumer prices, interest rate, tourist arrivals and house prices in the period from March 2005 to September 2022. Performed Granger causality tests and impulse response functions analysis indicate that an increase in real GDP, domestic credit and tourist arrivals increases house prices in Croatia while an increase in consumer prices and interest rate reduces them.

How Financial Risk Affects Indonesia Sovereign Sukuk Market Development

Global bond market attention is starting to witness very interesting developments in the development of Sukuk. However, the academic literature on the determinants of the development of the Sukuk market is still new and very limited. Using the Vector Autoregression (VAR)/Vector Error Correction Model (VECM) method, this study examines how the financial risk component influences the development of the Indonesian sovereign Sukuk market. Data was collected from Bank Indonesia and Indonesia’s Directorate General of Financing and Risk Management on the External Debt Statistics database for ten years from 2013Q1-2023Q1. The results of this study indicate that the foreign Debt Service Ratio (DSR) and Exchange Rate (EXC) significantly affect the development of the sovereign Sukuk Market. The foreign DSR has a negative effect, while the EXC positively affects the development of the sovereign Sukuk market. Meanwhile, the ratio of foreign debt to Gross Domestic Product (GDP) has an insignificant effect on the development of the sovereign Sukuk Market. The findings advocate policymakers in Indonesia on the importance of strengthening financial stability and regulating the issuance of Sukuk in accordance with the associated financial risks to develop the Sukuk market properly.

Обзор подходов к оценке влияния новых вызовов на экономику и отдельные аспекты налогообложения

The article presents a review of publications devoted to the approaches to assessing the impact of external shocks on the economy in general and on certain aspects of taxation in particular. It considers both studies that assess this impact by analyzing macroeconomic indicators (less often — budget indicators) and building econometric models (mainly SVAR and VAR models are used in this case), and studies in which various authors propose their own indicators to assess this impact (most often they are either sustainability indicators or stress indicators). Special attention is paid to publications the subject of which is the impact of new challenges on the tax system. A separate block includes publications in which the above issues are considered in relation to the Russian tax system. These are not only scientific articles on the topic, but also documents of international organizations, such as the International Monetary Fund, OECD, World Bank, etc. In addition, the article provides an overview of publications of foreign scientific institutes and research centers. Sanctions imposed on the economy of the Russian Federation are a particular case of an external shock. Thus, a review of publications assessing the impact of new challenges (by the example of sanctions) on the tax system of the Russian Federation is presented. The authors conclude that to assess the impact of new challenges on the tax system it is more advisable to use indicators based on statistical tax reporting.

Synthesis of realistic non-homogeneous non-Gaussian turbulent wind fields

This paper presents a sequential method for generating synthetic non-homogeneous non-Gaussian turbulent wind fields with a prescribed time-space covariance structure. The proposed methodology is based on the optimisation of restricted multivariate autoregressive (VAR) models, and the quantile-to-quantile transform between statistical distributions. The considered case study is a non-homogeneous non-Gaussian turbulent wind field over the roof of a high rise building simulated with LES. Results show a notably good matching in terms of the reproduced wind statistical distributions, Covariance Matrix Function (CMF) and Cross Power Spectral Density Matrix (CPSDM). In addition, the synthetic wind field reproduced accurately the recirculation bubble close to the roof. The main advantages of the proposed method are that, once the VAR model is computed, the synthesis of several realisations is computationally very cheap, which is useful for performing several aeroelastic simulations of the same analysis case, as suggested by the standards. The critical point is that, to characterise the statistical features for a specific case study (such as wind turbine wakes or turbulence due to obstacles), an LES simulation of the wind field is required as input. The software employed in this work is open source and it is available on GitHub.

The connectedness and structural changes among green and conventional energy markets with CO2 emissions in the United States

As the world's biggest oil producer and second-biggest polluter, the United States has set a strict target to cut net CO2 emissions by 50–52 percent from 2005 levels by 2030 and a net zero emission by 2050 through various strategies, particularly clean energy initiatives. This research attempts to examine the effects of five specific renewable and conventional energy subsectors with carbon emission across five sectors, including power (POW), industry (IND), ground transportation (GT), domestic aviation (DA), and residential (RE). The data illustrate the decline and subsequent increase in CO2 emissions throughout the COVID-19 pandemic, the Russia-Ukrainian conflict, and the USA recession periods from January 1 2021 to September 30 2022. Employing the Vector Autoregressive (VAR) model and a novel quantile connectedness framework, it was found that risk transmission between different markets has been exceptionally high, exceeding 80% both in the COVID-19 outbreak and in the aftermath. The finding suggests that the COVID-19 pandemic shock and the Russian-Ukrainian conflict have intensified the connections between carbon emissions and clean and non-clean energy markets. Policymakers should adjust net zero targets to align with changing dynamics, prioritize wind and geothermal energy, and promote energy independence due to rising oil prices. Leverage lithium for electric vehicles and create specific policies for power, industry, transportation, aviation, and residential sectors to manage CO2 emissions effectively.

On the Association Between Demographic Structural Change and the Effectiveness of Nurse Staffing Policy for Inpatient Care: Evidence from Taiwan.

This study investigates the influence of demographic changes on the effectiveness of hospital nurse staffing policy, measured by the cumulative response of inpatient care quality to adjustments in hospital nurse staffing levels in Taiwan. The research design utilized in this study aligns with the observational time-series methodology, and a total of 99 monthly time-series observations were collected from multiple databases administered by the Taiwan government over the period from January 2015 to March 2023. Specifically, the time-varying parameter vector autoregressive and autoregressive distributed lag models were employed to investigate the association between age distribution and nurse staffing policy effectiveness. The time-varying impulse responses of the unplanned 14-day readmission rate after discharge to changes in nurse staffing levels indicate a positive association between patient-to-nurse ratios and unplanned 14-day readmission rates across various types of hospitals. Nevertheless, the effectiveness of hospitals' nurse staffing policy is observed to diminish with population aging, particularly evident in medical centers and regional hospitals. Policymakers should establish lower mandated patient-to-nurse ratios, grounded in practical nurse workforce planning, to address the needs of an aging society and enhance inpatient care quality through improved nurse staffing in hospitals.

Promoting the sustainable development of traditional villages: Exploring the comprehensive assessment, spatial and temporal evolution, and internal and external impacts of traditional village human settlements in hunan province

The protection and development of traditional villages are crucial for improving the human settlement suitability (HSS). The paper takes 703 traditional villages in Hunan Province as the research object and establishes the HSS evaluation system by using the pressure-state-response model. Then this paper introduces the vector autoregressive model to explore the interactions and contributions within the three major subsystems. Finally, this paper adopts Geodetector model and GTWR model to study the external driving effects and temporal-spatial influence mechanisms. The main findings of this paper are as follows. First, the overall trend of the composite index of traditional villages is upward. Its spatial pattern transitions from a low index in the northwest to a medium index in the central region and a high index in the southeast. Second, the state system becomes the main driver of the response system change and it is highly influenced by the pressure system. Distance from medical facilities, Distance from educational institutions, Distance from the intangible cultural heritage sites, and Degree of relief are the four most important driving factors affecting the HSS in Hunan Province. At the same time, Distance to medical facilities and Distance to intangible cultural heritage sites have a positive impact, while Distance to educational institutions and Degree of relief have a negative impact. Fourly, these four factors have a significant spatiotemporal impact on the HSS in the Xiangxi region. This paper provides a scientific basis for the sustainable development and conservation of traditional villages in Hunan from multiple perspectives.

SIGNIFICANCE OF MONETARY INSTRUMENTS IN REGULATING THE ACTIVITIES OF BANKS

In this article, the impact of monetary policy instruments on the activity of commercial banks, in particular on bank liquidity, loan percentage and loan portfolio, is analyzed on the basis of econometric models. He used two different models in the econometric analysis assessing the impact of monetary policy instruments on commercial banks. The first model is a least square model, while the second is a structural vector autoregression model. In studying the impact of the monetary policy of the Central Bank on the activity of commercial banks, it analyzed two different types of banks.

The Effects of Shocks on the Real Economy in Romania. A Bayesian FAVAR Approach

Abstract The last five years have marked the global economy with a series of shocks, such as the COVID-19 pandemic, the war in Ukraine, supply chain bottlenecks, the surge in inflation, and the energy crisis, whose magnitude and duration were challenging to predict. Moreover, the impact of those shocks significantly affected the accuracy of macroeconomic forecasts. Thus, assessing the effects of the shocks became extremely important and conditioned by the econometric models that can capture a large amount of information. This paper proposes a Bayesian Factor-Augmented Vector Autoregressive Model (FAVAR) to assess the effects of the most prominent shocks that recently hit the real Romanian economy. We use a high-dimensional data set of quarterly indicators from 2005Q1 to 2023Q3, covering the real sector, price indexes, sentiment indicators and financial variables. Our main results suggest that tightening monetary policy shock decreases both the real economic activity and prices, while a supply shock produces a delayed negative and persistent effect on real economic activity and on the majority of the indicators introduced in the model. The results of the uncertainty shocks are characterized by wider confidence intervals and the effects are estimated to have a lower magnitude.

A nonparametrically corrected likelihood for Bayesian spectral analysis of multivariate time series

A novel approach to Bayesian nonparametric spectral analysis of stationary multivariate time series is presented. Starting with a parametric vector-autoregressive model, the parametric likelihood is nonparametrically adjusted in the frequency domain to account for potential deviations from parametric assumptions. A proof of mutual contiguity of the nonparametrically corrected likelihood, the multivariate Whittle likelihood approximation and the exact likelihood for Gaussian time series is given. A multivariate extension of the nonparametric Bernstein-Dirichlet process prior for univariate spectral densities to the space of Hermitian positive definite spectral density matrices is specified directly on the correction matrices. An infinite series representation of this prior is then used to develop a Markov chain Monte Carlo algorithm to sample from the posterior distribution. The code is made publicly available for ease of use and reproducibility. With this novel approach, a generalisation of the multivariate Whittle-likelihood-based method of Meier et al. (2020) as well as an extension of the nonparametrically corrected likelihood for univariate stationary time series of Kirch et al. (2019) to the multivariate case is presented. It is demonstrated that the nonparametrically corrected likelihood combines the efficiencies of a parametric with the robustness of a nonparametric model. Its numerical accuracy is illustrated in a comprehensive simulation study. Its practical advantages are illustrated by a spectral analysis of two environmental time series data sets: a bivariate time series of the Southern Oscillation Index and fish recruitment and a multivariate time series of windspeed data at six locations in California.

Portfolio balance effect of the U.S. QE between commodities and financial assets in commodity-exporting countries

This study analyses the portfolio balance channel of the U.S. quantitative easing (QE) by assessing the dynamic spillover effect between commodities and financial assets in commodity-exporting countries during QE. This study integrates the generalized spillover index initially proposed by Diebold and Yilmaz (2012) for the fractional integration VAR model. Then, we estimate the multivariate framework of the Westerlund and Narayan (2015) (WN)-based predictive model to quantify the effect of the portfolio balance channel on the net pairwise spillover index from the U.S. to other countries. Our results show: first, for bond yields, that Asian and Pacific bond yields are impacted by both commodity price indices returns and the U.S. bond yields across the sample periods. However, mixed evidence is found for both Latin America and Others; second, for equity, dynamic net return spillovers contribute mixed evidence across regional groups during QE. The diverse results are partly explained by the average percentage of commodity exports to total exports of the country and the degree of close interrelationship between countries. Additionally, dynamic return spillover analyses show that most foreign exchange returns are negative net spillovers during QE, supporting the behavior of “commodity currencies.” Last, the WN-based predictability models show pronounced differences in predictability across the selected commodity-exporting countries.

On a Partially Non-Stationary Vector AR Model with Vector GARCH Noises: Estimation and Testing

On a Partially Non-Stationary Vector AR Model with Vector GARCH Noises: Estimation and Testing