Vector Autoregression Research Articles

Big Data Analytics for Forecasting Tourism Recovery in Bali Island Using Multivariate Time Series

Bali is a famous tourist area and can significantly contribute to the Indonesian tourism sector. The COVID-19 pandemic has made Indonesian tourism, including Bali tourism, experience a decline. In March 2022, COVID-19 cases decreased, and the government began to relax some policies. The tourism sector is vital in economic recovery efforts after the COVID-19 pandemic. Therefore, it is necessary to identify tourism recovery to determine strategies and policies related to Indonesian tourism, especially in Bali. Multivariate time series forecasting of tourism demand can be used to identify tourism recovery using several significant data sources. The methods used are Vector Autoregressive (VAR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data used are the monthly official number of tourists, room occupancy rate, Google Trends, number of booking.com user reviews, and nighttime light intensity in Bali Province from January 2019 to December 2022. The results show that the best forecasting method is VAR, and modeling with multivariate time series forecasting can improve the performance of forecasting results. In addition, big data can be used as a source of supporting data that can provide better forecasting results, and the size of the dataset affects the selection of the best model. Furthermore, the descriptive and forecasting analysis results show that Bali tourism has experienced post-pandemic tourism recovery. The strategies and policies of the Bali government to restore Bali tourism faster are good enough.

The Effect of Food Price Stabilization on Inflation in West Nusa Tenggara 2019-2023

One of the primary contributors to the inflation rate in West Nusa Tenggara Province is the fluctuation of prices for food commodities. The objective of this study is to examine the extent to which fluctuations in food commodity prices, including those of rice, red chili, and shallots, contribute to inflationary trends in West Nusa Tenggara. The data used is monthly data on inflation and food commodity prices from 2019-2023 obtained from BPS (Central Bureau of Statistics), DKP (Food Security Agency), and the Agriculture Office of West Nusa Tenggara Province. The analysis method used is vector autoregression (VAR), which includes stationarity test, VAR stability test, optimal lag test, cointegration test, VAR estimation, impulse response function (IRF), and variance decomposition. The results of this study indicate that in the short term premium rice prices, curly red chili prices, and shallots do not affect inflation. Conversely, in the long run, the variable price of shallots does not affect inflation, while the variable price of premium rice and curly red chili prices exert an influence on inflationary pressures in West Nusa Tenggara Province. The Variance Decomposition Test indicates that the price of curly red chili contributes 37.24 percent to inflation in the fifth period, while premium rice has an effect of 8.66 percent on inflation in the second period and shallots have an effect of 7.93 percent on inflation in the fifth period.

Predicting Tourist Arrivals in Greece With a Novel Tourism-Specific Business Leading Indicator

We introduce a novel tourism-specific business expectations sentiment index and explore whether it can operate as a leading indicator for international tourist arrivals in Greece. Using monthly data spanning 2002‐2021 and employing a VAR model, we document that this newly introduced tourism-specific business expectations index serves as a leading indicator, whose higher levels foreshadow increased demand for international travel. We also find that its inclusion in a tourism-oriented model increases forecasting accuracy, which can be utilized by travel agent businesses, local government officials, and policymakers in their efforts to predict tourist arrivals in Greece.

The Spillover Effects of Public Attention and Real Estate Market: A Case Study from Shanghai, China

The real estate industry is closely linked to the national economy, and fluctuations in house prices have long been a major focus of public attention. This study explores the relationship between public attention and the real estate market, using public attention as a focal point. It systematically explains how public attention affects investment decisions in real estate. Taking Shanghai as a case, this study empirically examines the dynamic time-varying relationship between public attention and house prices, sales, investment and floor space completed by building a Time-Varying Parameter Vector Autoregression (TVP-VAR) model with data from 2011 to 2024.The research shows that the spillover index of house prices on public attention is positive and house prices acting as the sender of the shock, with rising house prices boost public attention. Conversely, the spillover index of public attention on house prices is negative, with public attention acting as the receiver of the shock. Based on the empirical results, it is recommended to incorporate public attention into the real estate market monitoring system, observe the real estate industry using multiple indicators, and enhance the regulation of online media to ensure policy transparency.

Effects of Global Supply Chain Pressure on Sentiment, Expectation, and Uncertainty: A VAR Approach

This paper studies the relationship of global supply chain pressure with consumer sentiment, inflation expectation, and monetary policy uncertainty in the United States. A sample from January 1998 to January 2024 is used, and this paper uses a Vector Autoregression (VAR) approach based on the method proposed by Toda and Yamamoto (1995). The Granger causality test suggests that the predictions of inflation expectation based on its own past values and the past values of the global supply chain pressure are better predictions of inflation expectation than just using the past observations of inflation expectation. In contrast, Impulse Response Functions suggest that surprise increases in global supply chain pressure lead to increased inflation expectation and monetary policy uncertainty; this shock lasts up to two years. Meanwhile, the Impulse Response Functions suggest that surprise increases in the global supply chain pressure decrease consumer sentiment (confidence), lasting up to two and a half years. Afterward, the impact converges back to zero. Additionally, the Variance Decomposition results suggest that by the final period, the impulses of the global supply chain pressure explain over 22%, 7%, and 44% of the variation of consumer sentiment, monetary policy uncertainty, and inflation expectation, respectively.

Interconnectedness of financial markets in crises in the case of the enlarged BRICS

The article presents a study of the connectivity and integration of sovereign bond and stock markets in 10 BRICS+ countries in the context of crisis instabilities in 2019−2024. Financial markets are becoming more integrated, and an increasing share of public investments are carried out across borders, which increases not only the opportunities for participants, but also the risks of a new crisis. The work used data on central bank rates of the considered countries, yield indices of 10-year government bonds, gold and Brent oil prices. The methods include the analysis of exchange rate dynamics, connectivity estimates based on the multivariate concordance coefficient and two-factor Friedman rank variance analysis, VAR models, Granger predictability and cointegration. The objective of this study is to analyze the interrelationship and cointegration between the sovereign bond and equity markets of selected BRICS+ countries during crisis periods. Our findings indicate that market interrelationship intensifies during crises, which in turn amplifies volatility. Additionally, we observed that none of the economies within the BRICS+ group can be classified as fully integrated or entirely isolated markets. The disruption of the interrelationship in the sovereign bond markets of the group is primarily reflected in the inconsistency of dynamic changes between Russia, China, and India. During the global shock of 2019–2020, the crisis spread from China, followed by Indonesia, and later to the other countries of the group. The financial and debt markets of the sampled countries were able to quickly cope with the severe shocks of the COVID-2019 period. The 2022–2024 crisis, which lasted significantly longer, began in Russia before spreading to countries across Asia and Africa. By 2024, Russia’s sovereign bond yields showed a marked decline. The increased market volatility following 2022 disrupted the integration and interrelationship of the stock and debt markets within the BRICS+ countries.

Assessing the Fiscal Implications of Flood Disasters in India: Insights for General and Special Category States

Abstract This study provides a comprehensive analysis of the fiscal consequences arising from flood disasters in India, with a specific focus on the challenges encountered by General Category States (GCS) and Special Category States (SCS). Recognizing India's vulnerability to floods due to its diverse geographical and climatic landscape, the research emphasizes the need for a coordinated disaster response framework involving both central and state governments. Utilizing a panel vector autoregression (VAR) methodology alongside impulse response functions (IRFs), we reveal that flood disasters significantly impact fiscal variables over a medium-term horizon of 3–5 years. Our findings indicate that fiscal deficits widen for up to three years post-disaster, while expenditures on flood control surge in the following years. Notably, SCS face a disproportionate fiscal strain, exacerbated by their geographical disadvantages and heightened susceptibility to disasters, leading to a decline in non-tax revenue after flood shocks. The study advocates for tailored fiscal policies that enhance the resilience and recovery capacity of both GCS and SCS in addressing flood-induced fiscal challenges.

Does high volatility increase connectedness? A study of Asian equity markets

Financial market integration metrics based upon variance decompositions from vector autoregression (VAR) models have become quite popular, and prior literature shows how these metrics change over time. However, only a few studies explain what is driving connectedness to change. Using stock market volatilities from eight Asian equity markets, we postulate that volatility may be a driving force behind increased connectedness. We use a two-step VAR estimation procedure to analyze the dynamics between equity market volatility and connectedness. The first step estimates the VAR model over rolling windows; this yields a sequence of connectedness-measuring spillover indices. The second step re-estimates the VAR with the spillover index sequence as an endogenous variable. Impulse response analysis from the two-step procedure confirms that volatility shocks increase connectedness. The two-step procedure indicates that volatility shocks from Singapore, Hong Kong, and China affect connectedness the most, whereas Malaysia affects it the least. China’s direct effect on the volatilities of the other markets is small. However, since China has a considerable impact on connectedness, China may have an important indirect effect on the other markets by increasing the speed and ease with which volatility spreads from market to market.

Oil price shocks and bond risk premia: Evidence from a Panel of 15 Countries

We study the effect of oil price shocks on bond risk premia. Based on Baumeister and Hamilton (2019), we identify the different sources of oil price shocks using a structural vector autoregressive (SVAR) model of the global market for crude oil. These structural factors are then used as unspanned factors in an affine term structure model based on the representation of Joslin et al. (2014). This is done for a total of 15 countries. Unspanned factors are responsible for most of the variability in bond risk premia for short holding periods, while spanned factors dominate the variance decomposition for longer holding periods. In both cases, global oil supply and global economic activity are clearly the most important unspanned shocks. A historical decomposition around the outbreak of the COVID-19 crisis shows the clear influence of global economic activity shocks during the months of February and March 2020, increasing bond risk premia significantly.

CANVAS: A Canadian Behavioral Agent-Based Model for Monetary Policy

We develop the Canadian behavioral Agent-Based Model (CANVAS) that complements traditional macroeconomic models for forecasting and monetary policy analysis. CANVAS represents a next-generation modelling effort featuring enhancements in three dimensions: introducing household and firm heterogeneity, departing from rational expectations, and modelling price and quantity setting heuristics within a production network. The expanded modelling capacity is achieved by harnessing large-scale Canadian micro- and macroeconomic datasets and incorporating adaptive learning and simple heuristics. The out-of-sample forecasting performance of CANVAS is found to be competitive with a benchmark vector auto-regressive (VAR) model and a DSGE model. When applied to analyze the COVID-19 pandemic episode, our model helps explain both the macroeconomic movement and the interplay between expectation formation and cost-push shocks. CANVAS is one of the first macroeconomic agent-based models applied by a central bank to support projection and alternative scenarios, marking an advancement in the toolkit of central banks and enriching monetary policy analysis.1

Monetary and Fiscal Policy Impacts on the Indian Sovereign Bond Market: A VAR Approach

This study analyses the dynamic relationship between yield curve behaviour and the macroeconomy in India, with a focus on both monetary and fiscal policy impacts. Using the monthly data on yields of Treasury bills and government securities from April 2000 to March 2020, we employ principal component analysis to extract the three yield curve factors—level, slope and curvature. We then use an unrestricted vector autoregressive model to analyse the relationship between these three yield curve factors and macroeconomic variables: inflation, GDP growth, exchange rate and monetary and fiscal policy indicators. Our findings reveal a bidirectional causality between yield curve factors and macroeconomic variables. Monetary policy plays an important role in shaping the yield curve, with short-term rates having more pronounced effects than long-term rates. Notably, this study is among the first to incorporate fiscal policy indicator in the Indian context, providing a more comprehensive understanding of policy impacts on the sovereign bond market. Our results offer valuable insights for policymakers and investors, showing the interrelationship of fiscal and monetary policies with bond market dynamics in emerging economies. This research contributes to the growing macrofinance literature and enhances our understanding of financial market dynamics in a developing economy. JEL Classification: E430, C32, G120, E44

Exercise moderates longitudinal group psychopathology networks in individuals with eating disorders

Individuals with eating disorders (EDs) often engage in exercise no matter potential negative long-term outcomes (e.g., weight loss, injury). Yet exercising may temporarily attenuate ED symptoms, but whether exercise also affects network structure and pairwise associations of ED symptoms remained unclear. We used a novel approach called Moderated Multilevel Graphical Vector Autoregression to estimate changes in psychopathology networks from before to after exercising in ecological momentary assessment data from 102 individuals with EDs across multiple days (M = 22.14, SD = 5.40; range: 6–22 days) at 4 times daily. Between-person and within-person temporal networks were computed, obtaining stable centrality coefficients for temporal networks only. In those, autoregressive effects of several symptoms, including binge-eating, overeating, or weighing oneself, were attenuated when participants previously exercised. Exercise mostly downregulated temporal effects of ED symptoms on other symptoms, including effects of binge eating and other compensatory behaviors on feeling guilty after the most recent meal, vomiting on weighing oneself, and overeating on fear of weight gain. Our study highlights the complex dynamic effects of exercise on ED symptoms in daily life and calls for novel studies investigating mechanisms of exercise to inform treatments targeting detrimental long-term effects of exercise in EDs.

주식시장 수익률과 개인 투자자의 매매 성향

This study examines the relationship between individual investors and the KOSPI stock market using long-term data spanning 25 years from 1999 to 2023. However, this study analyzes the relationship between individual investors' net buys and KOSPI returns by dividing the entire period into five sun-periods of five years and applying two-sided regression and structural vector autoregressive (SVAR) models. The main findings are as follows: First, compared to foreign investors, both institutional and individual investors exhibit relatively higher turnover ratios. Second, cross-sectional analysis shows that KOSPI returns decrease monotonically with the individuals' net buys. Third, the results of the two-sided regressions indicate a Granger causality, where past returns negatively influence individual investors' net buys. In addition, today's individual net buys negatively Granger cause future returns. Fourth, the SVAR analysis also shows that KOSPI returns have a strong negative effect on individual net buys. Finally, according to the results of SVAR impulse response function, net buys response negatively to KOSPI shocks, and this response dissipates almost entirely by the third day. In summary, individual investors tend to engage in net selling in response to positive news in the KOSPI market, and this tendency may be a key reason for their lower performance.

The Research of the Correlation Between USD Index and S&P500

This paper investigates existence of equilibrium relationships among the USD index and S&P500 in the long term in the United States. By applying correlation matrix analysis to daily data for the 2016:07:302023:08:01 period, this paper finds that there was a slight negative correlation between the USD index and the S&P 500. Basing on the Augmented Dickey-Fuller test (ADF test), this paper shows that that the correlation between the USD index and the S&P 500 is stable while vector autoregressive model (VAR model) proving that changes in the USD index can cause changes in the S&P 500 index. Firstly, using ADF test and VAR model to investigate the relationships between the USD index and A&P500, this paper reveals not only judge the negative relationship between this two, but also studying the stability of this relationship, proving the validity to explain the changes of S&P500 by the changes of the USD index. This conclusion can act as a good judging basic for US leaders to make economic decisions such as raising or lowering interest rates.

Using AI in the informal currency market: evidence from Cuba

ABSTRACT This article explains the various steps of a novel methodology designed to analyse the informal currency market and calculate a parallel exchange rate (ER). We utilize artificial intelligence algorithms for Natural Language Processing to extract data from social network groups and classified websites. The methodology has been implemented since 2021 amidst Cuba’s complex financial and political landscape. Given that the official exchange rate remains fixed and overvalued, the calculated ER has become vital for assessing the real-time effects of regulatory changes, economic shocks, and policies. We conduct a statistical and econometric analysis using vector autoregressive models and spillover indexes to validate the economic and financial consistency of the informal market’s time series.

Monetary shocks and house prices in Europe

PurposeThe aim of this study is to analyse the effect of conventional and unconventional monetary policy shocks on housing price dynamics in Europe (2000–2020). We propose a pan-European comparative analysis at a city market level, contrary to the previous literature.Design/methodology/approachWe build a quarterly market dataset for 13 European cities (Paris, Lyon, Marseille, Berlin, Munich, Frankfurt, Amsterdam, Madrid, Barcelona, Seville, London, Birmingham and Manchester). We proceed in two steps. First, we develop a structural VAR (vector autoregression) model. Second, we conduct a forecast error variance decomposition analysis.FindingsWe show that a contractionary policy rate has a negative influence on house prices with relevant differences. A balance sheet shock displays a heterogeneous effect on housing prices. Globally, we observe that a conventional monetary policy shock explains a larger share of total housing price variance than an unconventional monetary policy shock. Finally, our results report that conventional and unconventional monetary policy shocks have a greater impact in more liberalized credit markets.Originality/valueWe develop a pan-European analysis of house prices at a market level for a sample of 13 European cities. A parsimonious structural VAR model is used to study the dynamics of conventional and unconventional monetary policies on house prices in major European markets: Paris, Lyon, Marseille, Berlin, Munich, Frankfurt, Amsterdam, Madrid, Barcelona, Seville, London, Birmingham and Manchester. Our results highlighting the relative importance of conventional and unconventional monetary shocks, identify the existence of heterogeneous effects of monetary policies in European city markets.

ANALYSIS OF THE INFLUENCE OF MONETARY INSTRUMENTS ON THE SIZE OF THE DOMESTIC PUBLIC DEBT

The article is devoted to highlighting the results of research aimed at improving existing approaches to conducting a comprehensive analysis of the nature of the relationships between the parameters of the state's monetary and debt policy. Among other things, such indicators as the size of the domestic public debt, the size of international reserves, the level of inflation, the exchange rate and the discount rate are taken into account.The proposed approach to the analysis of the influence of managed monetary indicators on the size of the internal state debt proved that the use of the ARDL model gives slightly better results than the VECM in terms of the adequacy of the description and forecasting of the studied process. At the same time, both models proved their ability to describe the change in the size of the internal state debt, and have high values of coefficients of determination and low values of the indicator of the average absolute percentage error. In the future, it is recommended to use it for the analysis of the causality of the influence, discriminant analysis and the expediency of changing monetary instruments independently of other instruments.On the basis of the developed models, the results were obtained, which allow us to draw conclusions that the system continues to remain stable after fluctuations of key factors capable of destabilizing it and will be able to return to its previous state after a shock for some time. This is confirmed by the fact that, on the basis of statistical tests, the existence of a long-term relationship between the investigated indicators and the impulse response function together with the decomposition of the variance of the VAR model was proved.

Effects of foreign direct investment on climate change in the Asian region

Purpose ― This research aims to empirically investigate and compare the effects of foreign direct investment on climate change in five South Asian nations.Methodology ― This research uses yearly data covering 1980–2020 in five South Asian nations: Bangladesh, India, Nepal, Pakistan, and Sri Lanka. Vector Autoregressive (VAR) methods with variance decomposition and impulse response function provide the basis of the empirical data for comparison analysis. Findings ― This research shows that foreign direct investment's impact on pollution ranges from 1% to 10% in four countries and 16.13% in Pakistan. This indicates that in five South Asian states, there is little endogenous correlation between foreign direct investment and pollution. Furthermore, a shock to foreign investment improves the environmental conditions in Bangladesh and India while harming the growth of other nations.Implications ― The impact of foreign direct investment on pollution may vary based on each country's economic situation. Public efforts to enhance capital goods, education, health, and infrastructure are essential for reducing pollution and attracting foreign investment. Therefore, improved transparency and governance are essential for a positive relationship between growth and foreign investment.Value/Originality ― This research contributes to analyzing and comparing the effects of foreign direct investment on climate change in five South Asian nations using Vector Autoregressive (VAR) methods.

The European energy crisis and the US natural gas market dynamics: a structural VAR investigation

The Russian invasion of Ukraine triggered severe disruptions in the European energy market, causing also significant shifts in global natural gas flows. We investigate how this European shock has affected the dynamics and altered the estimates of the elasticities on the US natural gas market. We use the Bayesian Structural Vector Autoregression framework proposed by Baumeister and Hamilton (Am Econ Rev 109(5):1873–1910, 2019, BH) for the crude oil market and applied by Rubaszek et al. (Energy Econ 103:105526, 2021, RSU) to analyze the dynamics of the US natural gas market. By extending the RSU model for natural gas exports and imports and deriving model’s posterior using most recent data, we approximate the impact of the European energy crisis on the US natural gas market. We show that the estimates of the US natural gas market elasticities change due to our modification, while simply updating the same prior beliefs with most recent data impacts the posterior estimates to a very limited extent. We also find that a shock even as major as the European energy crisis has only marginally affected the US natural gas market, thus confirming the results from the literature that the EU and US natural gas markets evolve independently.

Climate pattern effects on global economic conditions

This study investigates how El Niño–Southern Oscillation (ENSO) climate patterns affect global economic conditions. Prior research suggests that ENSO phases, particularly El Niño, influence economic outcomes, but with limited consensus on their broader macroeconomic impacts. Using a novel monthly dataset from 20 economies, covering 80% of global output from 1999 to 2022, we employ a global augmented vector autoregression with local projections (GAVARLP) model. The empirical findings suggest that El Niño boosts output with minimal inflationary effects, reducing global economic policy uncertainty, while La Niña raises food inflation, which can amplify aggregate inflation as a “second-round” effect, amplifying uncertainty. These findings shed light on the transmission channels of climate shocks and highlight the significant role of ENSO in shaping global economic conditions, emphasizing why climate shocks should be a concern for policy markers.