Vector Autoregressive Estimation Research Articles

Poverty Inflation, FDI Consumption, and Economic Growth in Indonesia in the Vector Autoregressive Model Analysis

The difference in the results from previous studies on the impact of inflation on poverty, coupled with an increase in the money supply from direct investment, inspired this research. This study aimed to ascertain how poverty, inflation, and foreign investment affect consumption spending in Indonesia based on 1997-2021 time series data. This study used Vector Autoregression estimation, utilizing data sources from the World Bank. The data will be processed with econometric models. From the study’s findings, it may be inferred that overall consumption affects inflation, which can be seen from the Granger causality test that shows that this variable has a one-way causal relationship. The results of the same test also show that FDI has an effect on total consumption and conversely consumption has an effect on FDI. However, the poverty variable does not affect total consumption and vice versa, because according to the causality test, the variable obtains an insignificant probability value. However, the VECM results explain that poverty has an effect on total consumption, and that the effect of consumption on inflation is due to the larger t-statistic value and has a positive relationship. This implies that the rate of inflation will increase the lower the level of consumption. However, the Impulse Reason test shows that poverty has a negative trend, as does the FDI variable. Keywords: poverty, inflation, FDI, consumption, economic growth, Indonesia

Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations

This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused sectors. We find significant evidence of dependence and connectedness between the stock returns of AI and those of the energy-focused sectors, especially during intermediate and long-term investment horizons. The relationship has become stronger since the COVID-19 pandemic. More specifically, results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions. TVP-VAR results show that since the COVID-19 outbreak, AI has become more of a net shock receiver from the energy market. Our study offers crucial implications for investors and policymakers.

Impact of Macroeconomic Indicators on The Indonesia Stock Exchange (IDX) Composite Index Performance

This study examines the impact of macroeconomic indicators on the performance of the Indonesia Stock Exchange (IDX) Composite Index from 2014 to 2023, focusing on four key variables: interest rates, inflation, money supply (M2), and Gross Domestic Product (GDP). Using a Vector Autoregressive (VAR) model, the research investigates the short-term dynamics between these variables. The findings show that in the VAR estimation, only the lag 1 M2 value has a statistically significant coefficient. However, the Impulse Response Function (IRF) analysis reveals that shocks to the Bank Indonesia rate, inflation, money supply, and GDP initially cause significant fluctuations in the Jakarta Stock Exchange price index, but these effects are short-lived, with the index stabilizing around zero after several periods. These results provide valuable insights for policymakers and investors, emphasizing the importance of understanding macroeconomic conditions for making informed stock market decisions.

The nexus between revenue decentralization reforms and economic growth

AbstractRevenue decentralization represents a framework that facilitates enhanced fiscal autonomy for subnational governments, thereby enabling the tailoring of services to meet local needs and potentially fostering economic growth. In Kenya, the ratification of the 2010 constitutional formalized novel revenue frameworks aimed at supporting the operations, management, and sustainability of devolved county governments. However, there remains a paucity of empirical analysis examining the relationship between the revenue decentralization reforms and county‐level economic growth. We begin by identifying the main sources of county revenues within the decentralization frameworks, then investigate the dynamic interrelation with county economic growth. Through panel vector autoregression estimation techniques, our analysis reveals a positive and statistically significant association between own‐source revenue and conditional grants with county‐level economic growth. We discuss the implications of our findings and call for policymakers to reassess the fiscal instruments with the aim of strengthening the roles and responsibilities of counties.

Exchange Rate Pass-Through to Domestic Prices: Is Inflation Targeting an Optimal Policy Choice for Pakistan?

This study investigates the effect of exchange rate dynamics and its impact on consumer prices in Pakistan. Using monthly data for the period 2004- 2022, the Recursive Vector Autoregressive (VAR) technique has been employed for the empirical analysis. The recursive VAR estimation technique yields impulse response functions that demonstrate the incompleteness of exchange rate pass-through (ERPT). The results of the VAR reflect that the consumer prices bring most of the error variance in itself for the period under consideration. The exchange rate has been established to be the second most significant source of variations in consumer prices. The empirical results suggest that the lower ERPT reduces the risk of exchange rate volatility. Hence the monetary authorities may take steps to adopt inflation targeting in Pakistan.

An analysis of long-term and short-term impact of climate change on rice production in India

PurposeClimate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.Design/methodology/approachThis study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.FindingsThe study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.Originality/valueThe present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

A comparative analysis of locally sourced feeding systems on chicken body weight gain in Malaysia

The surge in global poultry popularity has led to increased chicken meat consumption. The pandemic-induced rise in imported chicken feed prices has adversely affected the profitability of the chicken meat business. This situation prompted an investigation into the impact of four locally sourced feeding systems on chicken body weight gain, aiming to enhance the profitability of Malaysia’s chicken meat industry. The study, conducted on a Malaysian farm with 200 chickens, analyzed Feeding 1 (Premier Starter Feed (PSF) - Control Feed), Feeding 2 (PSF + SK Gold yellow powder), Feeding 3 (PSF + crude palm kernel oil) and Feeding 4 (PSF + PERFAT Pfi-7 organic acid). Over 14 weeks and 2,800 observations, Engle & Granger’s Vector Autoregression estimation procedure was employed, revealing a positive correlation between chicken age and feeding systems impacting chicken body weight. Feeding 3, incorporating crude palm kernel oil, exhibited the most significant influence on chicken body weight gain, emerging as the optimal and effective choice for improving the chicken meat business. The model’s reliability was affirmed by consistent experimental results, rendering the findings applicable to the commercial market. In conclusion, this research establishes that Feeding 3 is the most efficient, offering valuable insights for commercial applications.

Herding behaviour and sentiment: evidence from emerging markets

PurposeBased on the theoretical framework, this paper analyzes the sentiment-herding relationship in emerging stock markets (ESMs). First, it aims to examine the effect of investor sentiment on herding. Second, it seeks the direction of causality between sentiment and herding time series.Design/methodology/approachThe present study applies the Exponential Generalized Auto_Regressive Conditional Heteroskedasticity (EGARCH) model to capture the volatility clustering of herding on the financial market and to investigate the role of the investor sentiment on herding behaviour. Then the vector autoregression (VAR) estimation uses the Granger causality test to determine the direction of causality between the investor sentiment and herding. This study uses a sample consisting of stocks listed on the Shanghai Composite index (SSE) (348 stocks), the Jakarta composite index (JKSE) (118 stocks), the Mexico IPC index (14 stocks), the Russian Trading System index (RTS) (12 stocks), the Warsaw stock exchange General index (WGI) (106 stocks) and the FTSE/JSE Africa all-share index (76 stocks). The sample includes 5,020 daily observations from February 1, 2002, to March 31, 2021.FindingsThe research findings show that the sentiment has a significant negative impact on the herding behaviour pointing out that the higher the investor sentiment, the lower the herding. However, the results of the present study indicate that a higher investor sentiment conducts a higher herding behaviour during market downturns. Then the outcomes suggest that during the crisis period, the direction is one-way, from the investor sentiment to the herding behaviour.Practical implicationsThe findings may have implications for universal policies of financial regulators in EMs. We have found evidence that the Emerging investor sentiment contributes to the investor herding behaviour. Therefore, the irrational investor herding behaviour can increase the stock market volatility, and in extreme cases, it may lead to bubbles and crashes. Market regulators could implement mechanisms that can supervise the investor sentiment and predict the investor herding behaviour, so they make policies helping stabilise stock markets.Originality/valueThe originality of this paper lies in investigate the sentiment-herding relationship during the Surprime crisis and the Covid-19 epidemic in the EMs.

Panel data analysis of foreign direct investment, control of corruption, and economic growth: Evidence from ASEAN-6 countrie

This research is to examine the role of management of foreign direct investment and control of corruption in economic growth. The research data were collected from the ASEAN-6 countries including Indonesia, Malaysia, Thailand, Singapore, Philippines, and Vietnam during the period of 2002-2021. The research utilizes the panel vector autoregressive (PVAR) method developed by Abrrigo and Love (2015) [Abrigo, M. R. M., & Love, I. (2016). Estimation of panel vector autoregression in Stata.] to estimate the research model. The estimation results show that foreign direct investment and corruption control play an important role in promoting economic growth in the ASEAN-6 countries. Furthermore, foreign direct investment and corruption control are closely related to each other, indicating that economic growth is not only directly affected by foreign direct investment and corruption control but also indirectly influenced by each of these factors. This is a new finding of this research compared to previous studies. These findings provide significant empirical evidence for the ASEAN-6 countries, particularly in managing foreign direct investment and controlling corruption to promote economic growth. The implication of these results is that these countries identify appropriate policies to manage FDI and corruption control in order to maximize the level of economic growth.

Panel Vector Autoregressive Modeling of Macroeconomic Interaction in Nigeria, Ghana and Cameroon

Aims: The aim of this study is to apply Panel VAR (Vector Autoregressive) modeling and estimation to analyze the macroeconomic interaction and interdependence within the context of Nigeria, Ghana, and Cameroon. The study aims to understand the trends of key macroeconomic variables, namely gross domestic product (GDP), exchange rate, and foreign reserves.
 Methodology: The study adopted three macroeconomic variables—GDP, exchange rate, and foreign reserve—and utilized annual secondary data from the World Bank spanning from 1960 to 2022. Pretests, including unit root and cointegration tests, were conducted on the variables. The panel unit root tests (Levin, Lin, and Chu t, Augmented Dickey-Fuller Fisher Chi-Square, and Phillips-Perron Fisher Chi-Square) indicated that the series had unit roots at levels but were stationary at first difference, implying integration of order one. The absence of co-integration in the panel co-integration test established the necessary conditions for estimating a panel vector autoregressive model.
 Results: The trend analysis revealed that the variables were relatively low in the 1960s and 1970s but exhibited an increasing and fluctuating pattern afterward. Descriptive statistics showed variations among the countries, with Cameroon having higher GDP per capita and greater standard deviation, indicating more significant fluctuations. Ghana, in contrast, displayed lower per capita income with a lower standard deviation. The foreign exchange rate varied, with Cameroon having the highest and Ghana the lowest mean rates.Conclusion: The fixed effect model was estimated after the Hausman Test rejected the random effect model. The results indicated that foreign exchange rates had joint significance on GDP per capita, while foreign reserves did not. The study concludes that the economies of Nigeria, Ghana, and Cameroon are responsive to GDP per capita, foreign exchange rates, and foreign reserves. The policy implication is that economic practitioners in these countries should closely monitor these variables to anticipate changes in economic indicators. Therefore, the study recommends active monitoring of the economic variables used in this research to facilitate informed decision-making.

Spillover effects and transmission of shocks in Visegrad equity markets

PurposePaper aims to investigate the interdependencies and spillover effects that the Visegrad (V4 hereafter) Equity Markets hold on each other. The V4 group stands for the political alliance of four Central European countries: Poland, the Czech Republic, Hungary and Slovakia.Design/methodology/approachThe study uses Wavelet coherence, dynamic conditional correlation GARCH (1, 1) and unrestricted vector autoregression (VAR) methodologies. Daily data series (covering the period from January 2, 2006, to February 2, 2023) are analyzed to assess coherence, time-varying conditional correlation and shock transmission among the V4 Equity Markets.FindingsWavelet analysis reveals that the Slovak equity market does not maintain coherence with three other equity markets. The time-varying conditional correlation documents for the high interdependence during the COVID-19 outbreak of the four indexes. The VAR estimates reveal that shocks in the Warsaw equity market are easily transmitted in Prague and Budapest exchanges but not in Bratislava. The results show that the Slovak equity market tends to be isolated from the influence of other three V4 exchanges. This isolation is attributed to its size, limited volume and adoption of the euro in 2009. The study emphasizes the Slovak financial system’s gravitation toward the Eurozone after euro adoption.Originality/valueNotably, the findings provide important signals for local and international investors as the results cover four significant international shocks. The global meltdown of 2008/09, the Greek debt crisis of 2010/11, the COVID-19 pandemic and the Russia-Ukraine war.

Testing omitted variables in VARs

A procedure is outlined aiming at testing the bias due to omitted variables in vector autoregressions. The procedure consists first of filtering a vector of omitted variables and then testing the bias. The test does not rely on the availability of the omitted variables, and is based on a comparison between maximum-likelihood with Kalman filter vector autoregression and linear vector autoregression estimates. The empirical part considers two illustrative examples: a univariate regression analysis, based on the rational expectation-augmented Phillips curve; and a VAR with output, inflation and interest rates where a “price puzzle” arises.

Impact of Google searches and social media on digital assets’ volatility

Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.

More Accurate Climate Trend Attribution by Using Cointegrating Vector Time Series Models

Adapting to human-induced climate change is becoming an increasingly important aspect of sustainable development. To be able to do this effectively, it is important to know how much human influence has contributed to observed climate trends. Climate detection and attribution (D&A) studies achieve this by estimating scaling factors usually obtained by performing a least squares regression of the observed trending climate variable on the equivalent variable simulated by a climate model. This study proposed instead to estimate scaling factors by using the econometric approach of dynamically modelling the time series as a cointegrating Vector Auto-Regressive (VAR) time series process. It is shown that a 2nd-order cointegrating VAR(2) model is theoretically justified if the observed and simulated variables can be represented as a one-box AR(1) response to a common integrated forcing. The VAR(2) model can be expressed as a Vector Error-Correction Model (VECM) and then fitted to the data to obtain the cointegration relationship, the stationary linear combination of the two variables, from which the scaling factor is then easily obtained. Estimates of the scaling factor from the VAR(2) model are critically compared to those from Ordinary Least Squares (OLS) and Total Least Squares (TLS) for annual Global Mean Surface Temperature (GMST) data simulated by a simple stochastic model of the carbon–climate system and for historical simulations from 16 climate models in the Coupled Model Intercomparison Project 5 (CMIP5) experiment. Results from the toy model simulations show that the slope estimates from OLS are negatively biased, TLS estimates are less biased but have high variance, and the VAR(2) estimates are unbiased and have lower variance and provide the most accurate estimates with smallest mean squared error. Similar behaviour is noted in the CMIP5 data. Hypothesis tests on the VAR(2) fits found strong evidence of a cointegrating relationship with the observations for all the CMIP5 simulations.

Measuring the varying relationships between sustainable development and oil booms in different contexts: An empirical study

Sustainable development, as a global reform that takes into account economic, social and environmental aspects, requires a focus on energy activities, especially those of the oil industry, as they usually have varying degrees of impact on the three aspects mentioned. We, therefore, explore the nexus between sustainable development and oil booms based on data from 38 countries and seven specific countries (China, the USA, Russia, Norway, Brazil, Libya and Kuwait) between 1990 and 2019. First of all, the Bai-Perron tests confirm that structural changes in the oil production growth are more frequent, while the sustainable development index growth is relatively more stable. Then, we examine the performance of this nexus in different subperiods using the panel vector autoregression models and observe significant associations only in the subperiod 2012 to 2019. At last, we find that the Granger causalities and long-run dynamic relationships between sustainable development and oil booms are considerably different across economies in the vector autoregression estimations for each of the seven countries. In a word, the effect of oil production on sustainable development could be positive or negative over time and across economies, and there is no consistent conclusion, and vice versa. Thus, in the sustainable development practice and the green transformation of the oil industry, targeted strategies should be adopted according to the current development stage and the realistic background of specific objects.

W kierunku ekonomii politycznej energii odnawialnej: czy demokracja i globalizacja mają znaczenie dla krajów Europy Środkowej i Wschodniej (CEEC)

Renewable energy policy is one of the remarkable parts of the sustainable development path. However, the political-economic dimension of renewable energy policies is not so much widely discussed. Besides, democracy and globalization are essential factors affecting renewable energy. Hence, this paper examines the relationship between renewable energy consumption, democracy, and globalization in the Central and Eastern European Countries (CEECs) during the period 1995-2021. Economic growth and CO2 emissions are used as control variables in the model. The study employs the panel vector autoregressive (PVAR) estimation technique to quantify the relationship between renewable energy consumption, democracy, and globalization by including economic growth and CO2 emissions. The findings from the PVAR analysis suggest that participatory democracy and globalization positively affect renewable energy consumption, while liberal democracy, economic growth, and CO2 emissions have a negative impact on it. Furthermore, the PVAR Granger causality test outcomes indicate an interactive causal relationship between variables.

Measuring the response of the property price index to monetary policy shock: Evidence from emerging market economies

This study examined the response of the residential property price index (RPPI) to monetary policy shocks. The analysis utilized a panel vector autoregressive (VAR) estimation covering the markets of 12 emerging countries over a quarterly period from 2000 Q1 to 2022 Q4. In a dynamic data model, the panel VAR estimation could be biased when the coefficients of the endogenous variables differ across countries. The results of the pooling assumption showed that the models contain heterogeneity among samples, indicating the need for a solution to address this problem. Consequently, a mean group estimation for the panel VAR was performed to resolve the heterogeneity issue. The residential property price index negatively responded to changes in housing loan (HL) and central bank (CB) interest rates. In contrast, it positively responded to changes in inflation, gross domestic product, and population. This research provided policymakers with recommendations on emerging market demand. To successfully interfere in the property market, policymakers must pay greater attention to formulating monetary policy, notably central bank interest rates. A steady central bank policy rate prevents growth in the property sector. Future research should include macroprudential policy as an endogenous component in the model.

Determinants of farm investment in Somalia: A var model application

The main objective of this study is to examine the determinants of farm investment in Somalia using quarterly time-series data from 2015 to 2021. The study selected financing assets (FA), farm output (FO), credit for commercial banks (CCB), and saving (S) as macroeconomic variables. The augmented Dickey–Fuller (ADF) test was employed to evaluate the stationarity of the variables, and the Granger causality test was used to assess the causal relationship between the study variables. It was found that all variables became stationary at the second difference, with a trend for each of the three critical levels of 1%, 5%, and 10%. The Granger causality test indicates a unidirectional causal connection between farm investment, farm output, financial assets for commercial banks, savings for commercial banks, and loans for commercial banks in Somalia. The vector autoregressive (VAR) estimation revealed that the coefficient of determination explains 98% of the model, and the ordinary least squares (OLS) estimation showed a highly significant P-value. The results show that financing assets and savings will increase farm output if managed effectively and efficiently. Based on the findings, the study recommends that the government focus on the overall institutional framework in Somalia to facilitate economic growth, poverty reduction, and sustainable development.

Managing natural resource prices in a geopolitical risk environment

In this paper, we analyze the potential impact of geopolitical risks on various natural resources such as oil, coal, copper, and zinc, from January 2005 to September 2022. Firstly, we employ an event study analysis to detect the impact of selected geopolitical events by assessing the significance of the abnormal returns and their cumulative effect. Secondly, we use the Time-Varying Parameter Vector-Autoregression estimation method to investigate the possible time-varying impact. Our empirical results from both methods indicate a significant impact of the ongoing Russia–Ukraine conflict on the energy commodity markets. Moreover, our findings reveal that metal prices exhibit a more stable behavior in terms of stochastic volatilities compared to natural gas and oil prices. Furthermore, the response curves of oil and natural gas prices to geopolitical index shocks suggest a downward trend, indicating a negative linear relationship between the geopolitical index and the energy market. In contrast, copper and zinc prices show a non-linear and negative relationship with the geopolitical index, although they are not very responsive to it. Overall, our study provides important implications for policymakers, investors, managers, and consumers to consider geopolitical risks while managing natural resource prices.

Implication of electricity taxes and levies on sustainable development goals in the European Union

The current high electricity prices in the European Union (EU) are in part due to the high electricity taxes. United Nations’ Sustainable Development Goals (SDGs) Agenda with its global vision of attaining sustainable development especially seeks “to ensure universal access to affordable, reliable and modern energy services” (SDG 7). We investigate the synergy and trade-off effects of electricity taxes on sustainable development goals (SDGs) for the EU. Using panel data and panel vector autoregressive estimation approach, we find that higher household electricity taxes reduce both carbon emission and unemployment. Higher levels of industry electricity taxes, increase responsible production and consumption (SDG12) and reduces unemployment (SDG8). Furthermore, there is evidence for a strong synergy effect between electricity taxes, unemployment and carbon emission but a trade-off between tax and SDG9 (innovation and sustainable infrastructure). The taxes contribute more to the future variation of unemployment and responsible production and consumption in the EU, but these contributions are much larger for the industry as compared to the household sector. Our results confirm the double-dividend hypothesis, which implies that the policymakers can achieve environmental goals with higher electricity taxes, especially on household electricity. In the industrial sector, our findings suggest that there is a need for tax reform, to encourage innovation and adopt production processes that are less polluting to the environment.