Academic literature on the topic 'Panel Vector Autoregressive Model (panel VAR)'

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Journal articles on the topic "Panel Vector Autoregressive Model (panel VAR)"

1

Arı, Yakup. "The CARR-volatility connectedness between USD/TRY and foreign banks in Turkey: Evidence by TVP-VAR." Applied Econometrics 67, no. 3 (2022): 5–26. http://dx.doi.org/10.22394/1993-7601-2022-67-5-26.

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This study focuses on the volatility spillover between the stock prices of foreign banks having business in Turkey and the exchange rate. More particularly, it analyzes the connectedness between the USD-TRY exchange rate volatility and the foreign banks’ stock price volatility in their own country’s stock markets. We select ten foreign banks with the biggest total assets and divide them into two panels: eastern and western capitalized banks. The dataset contains weekly data from 2016-01-04 to 2022-01-17. We estimate volatilities utilizing the Conditional Autoregressive Range (CARR) model and then apply the Time-Varying Parameter- Vector Autoregressive (TVP-VAR) based Diebold–Yilmaz Connectedness Index to reveal the transition and connectedness of volatility. The total connectedness indices show that 26.72 and 54.75% of the forecast error variance originate from other assets included in the spillover analysis for eastern and western panels, respectively. We also explore net pairwise comovements and find that shocks in USD-TRY have dominated on the forecast error variance of bank stocks in the eastern panel, while it is a net volatility receiver in the western panel.
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2

Shapor, Maria Alexandrovna, and Rafael Rubenovich Gevogyan. "Features of the vector autoregression models application in macroeconomic research." Mezhdunarodnaja jekonomika (The World Economics), no. 8 (August 10, 2021): 634–49. http://dx.doi.org/10.33920/vne-04-2108-05.

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In this paper, we analyzed articles by foreign authors that use various vector autoregression models to calculate the impact of qualitative indicators on the economic processes of countries or a group of countries. In particular, the article analyzed the classical model of vector autoregression (VAR), panel model of autoregressive (PVAR), Bayesian model of autoregressive (BVAR), structural model of autoregressive (SVAR), and the global model of autoregressive (GVAR). Among the works using vector autoregressive models, the main emphasis is on financial indicators. Moreover, articles with non-trivial variables are rare. This is because financial macroeconomic variables in most cases have a direct impact on economic processes in the country. The analysis of financial indicators and the results obtained can play a significant role in the development of economic strategies in different states, since the results obtained with the help of vector autoregression models are usually quite accurate. The studied articles analyze the data of both developed and developing states or groups of states in different periods. The studied articles were classified according to several criteria, which were selected by the author to structure the work. Note that among the works using vector autoregressive models, the main emphasis is on financial indicators. Moreover, articles with non-trivial variables are rare. This is since financial macroeconomic variables in most cases have a direct impact on economic processes in the country. The analysis of financial indicators and the results obtained can play a significant role in the development of economic strategies in different states, since the results obtained with the help of vector autoregression models are usually quite accurate. In the conclusion of this study, the author presented conclusions based on the analysis of autoregressive models.
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3

Paibi, Bunting Boruku, Didi Essi Isaac, and Zorle Dum Deebom. "Exchange Rate, Interest Rate and Agricultural Export Earnings: An analysis Using Panel Data Vector Autoregressive Model." INTERNATIONAL JOURNAL OF APPLIED SCIENCE AND MATHEMATICAL THEORY 8, no. 2 (2022): 14–37. http://dx.doi.org/10.56201/ijasmt.v8.no2.2022.pg14.37.

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The study modeled the dynamic interaction between exchange rate, interest rate and agricultural export earnings using panel VAR Model. The specific objectives of the study include to; interdependencies in the dynamic interaction between exchange rate, interest rate and agricultural export earnings, parameters of panel VAR model using PVAR Stata code developed by Abrigo and love, determine the shocks associated with their dynamic interactions between these variables, investigate direction of causality between interest rate, exchange rate and agricultural export earnings from six African countries and make appropriate recommendations. The data used for the study was secondary data extracted from index mundi website and world data indicators for the period of 40 years (1980-2020). The data was on exchange rate, interest rate and agricultural export earnings. Geographically, the six African countries include; Algeria, Angola, Egypt, Libya, Gabon and Nigeria. The study uses vector Autoregressive model estimation results with PVAR Stata code developed by Abrigo and love. The post estimation test on the Vector Autoregressive (VAR) model shows a contemporary Co-efficient of Correlation analysis. It was found that lending interest rate and exchange rate are negatively associated with Co-efficient of Correlation of (-0.0873). Also, it was found that there exist a positive association between exchange rate and agricultural export earnings. Also, there is a positive association between lending interest rate and agricultural export earnings. The inverse roots of a characteristic polynomial of the estimated Panel VAR model satisfied the stability condition (of the diagnostic test) since no root lied outside the unit root circle. Therefore, the estimated VAR is stable. However, it was confirmed that there is no directional relationship that exist between the variables. Also, the results show that exchange rate and lending rate have positive on agricultural export earnings, whereas exchange rate is likely to reduce the level of lending interest rate slightly. Therefore, it is recommended that in estimating the dynamic interaction between variables in a panel data system, there is need for the inclusion of the lags of the response variable among the determinants to measures the dynamic interaction as well capture heterogeneities in the series and also, policies should be formulated to stabilized exchange and lending rates in order to improve and strengthen the countries’ agricultural economy amongst others
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4

Anwar, Cep Jandi, and Indra Suhendra. "Measuring Response of Stock Market to Central Bank Independence Shock." SAGE Open 13, no. 1 (2023): 215824402311521. http://dx.doi.org/10.1177/21582440231152135.

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This paper examines the responses of stock traded and economic activity in relation to central bank independence shocks. The analysis is based on a panel Vector Autoregressive estimation covering 25 developing countries for a quarterly period between 1990 Q1 and 2021 Q4. Panel VAR estimation is known to be potentially biased in a dynamic panel data model if the coefficient on the endogenous variables differs across countries. To test the pooling assumption of the model, this paper applies the Chow and Roy-Zellner tests. The results establish that the models contain the heterogeneity among samples; thus, mean-group estimation for panel VAR is performed as a solution for heterogeneity problem. The results show that there is a delayed effect of CBI to increase stock traded. This demonstrates that CBI takes around six quarters to strengthen the financial market. By dividing the sample into two groups, we find that CBI has a different effect on stock traded for countries that have high and low stock traded.
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5

Ariutama, I. Gede Agus, and NFN Syahrul. "ANALISIS PANEL VAR: TINGKAT PENDIDIKAN, TINGKAT KESEHATAN, DAN KETIMPANGAN PENDAPATAN DI INDONESIA." INFO ARTHA 1 (June 7, 2017): 1–16. http://dx.doi.org/10.31092/jia.v1i1.74.

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By utilizing Panel Vector Autoregressive (PVAR) model, this study aims to identify the reciprocal effect of the level of education, health, and income inequality in Indonesia. Data of 33 provinces from 2009-2013 specifically means of school years for education level, life expectancy for health level, and gini ratio for the income inequality are explored. Furthermore, Granger Causality Test is employed to test the causality of the variables. The results showed that the levels of education, health, and income inequality in Indonesia shared mutual relationship by applying one lag as the best PVAR model in this study.
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6

Kovacevic, Radovan. "The effects of FDI net inflow on the current account of southeast Europe countries - a panel causality analysis." Ekonomski anali 67, no. 235 (2022): 95–122. http://dx.doi.org/10.2298/eka2235095k.

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This paper examines the relationship between the current account (CA) and the foreign direct investment (FDI) net inflow in the Southeast Europe (SEE) countries. The panel data framework of five SEE countries for the period 2000- 2020 are used. Our research has three main findings. First, using the vector autoregressive VAR(2) model, a long-run relationship between the CA and the net FDI inflow is identified (a 1% increase in the net FDI inflow leads to a 1.011% increase in the CA deficit). This suggests that FDI stock will put upward pressure on the CA of the SEE countries in the long run. Second, applying the panel VAR model Granger causality test, we found that there is a two-way directional Granger causality. Third, our results from the vector error correction (VEC) model suggest that about 26% of the dynamics of the CA deficit adjusts to the long-run equilibrium path with the net FDI inflow each year.
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7

Pacifico, Antonio. "Structural Panel Bayesian VAR Model to Deal with Model Misspecification and Unobserved Heterogeneity Problems." Econometrics 7, no. 1 (2019): 8. http://dx.doi.org/10.3390/econometrics7010008.

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This paper provides an overview of a time-varying Structural Panel Bayesian Vector Autoregression model that deals with model misspecification and unobserved heterogeneity problems in applied macroeconomic analyses when studying time-varying relationships and dynamic interdependencies among countries and variables. I discuss what its distinctive features are, what it is used for, and how it can be analytically derived. I also describe how it is estimated and how structural spillovers and shock identification are performed. The model is empirically applied to a set of developed European economies to illustrate the functioning and the ability of the model. The paper also discusses more recent studies that have used multivariate dynamic macro-panels to evaluate idiosyncratic business cycles, policy-making, and spillover effects among different sectors and countries.
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8

Gao, Ang. "Transmission Machine of International Financial Crisis Based on VAR Model." Frontiers in Business, Economics and Management 5, no. 1 (2022): 48–52. http://dx.doi.org/10.54097/fbem.v5i1.1429.

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The financial crisis has always been accompanied by human economic activities, and its outbreak has a serious impact on people's normal life, and it has no warning, which is a problem of great concern to all countries in the current economic development. Based on the monthly data of long-term bond yields of eight countries (six European countries, the United States and Japan) from December 2009 to February 2012, through correlation test and Granger causality analysis, the results show that there is no Granger causality between the bond yields of various countries and the bond yields of Greece before the European debt crisis, but Granger causality exists between the bond yields of Germany, France and Italy and Greece after the crisis. Finally, it puts forward the process of China's complete opening of capital account and the problems that should be paid attention to after that. The latest development direction of vector autoregressive model is the identification of exogenous policy shocks and the estimation of panel vector autoregressive model. This paper studies and analyzes the transmission mechanism of international financial crisis. Vector autoregressive model is used to describe the transmission mechanism of international financial crisis. The enhancement of the ability to resist the contagion of financial crisis is an important factor to promote the sound development of China's economy. Starting with the related concepts of the transmission mechanism of international financial crisis, this paper probes into the specific transmission modes of international financial crisis and the suppression measures of the transmission mechanism of international financial crisis.
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9

Kristianto, Doddy. "CREDIT FOR MSMES, PRIVATE CREDIT, AND REGIONAL ECONOMIC GROWTH IN INDONESIA: A PANEL VECTOR AUTOREGRESSIVE ANALYSIS." INFO ARTHA 3, no. 2 (2019): 85–100. http://dx.doi.org/10.31092/jia.v3i2.576.

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This study aims to analyse the dynamic between credit to micro, small, and medium enterprises (MSMEs), private credit, and regional economic growth in Indonesia. A panel vector autoregression model is employed to understand the dynamic in the model. Unlike previous cross-country studies, this paper is using provincial panel data. The relationship between variables in the model are connected under the same regulation, monetary authority, and fiscal institution. Thus, there will be no effects that appear from the differences of institutions. The results suggest that the interaction between credit to MSMEs and private credit is bi-directional. Also, credit to MSMEs and private credit does affect regional economic growth. However, the result does not provide strong evidence for causality from regional economic growth to credit for MSMEs or private credit. Penelitian ini bertujuan untuk menganalisis hubungan dinamis antara kredit kepada UMKM, kredit kepada sektor privat, dan pertumbuhan ekonomi regional di Indonesia. Untuk memahami hubungan ketiga variable tersebut, penelitian ini menggunakan panel vector autoregression (Panel VAR). Penelitian ini berbeda dengan penelitian antar negara yang telah ada sebelumnya karena penelitian ini menggunakan data pada level provinsi. Data pada level provinsi memiliki keunggulan karena berada pada sistem regulasi perbankan dan regulasi fiskal yang sama sehingga tidak terdapat efek dari adanya perbedaan institusional. Dari penelitian ini diketahui bahwa interaksi antara kredit kepada UMKM dan kredit kepada sektor privat adalah dua arah. Kredit kepada UMKM dan kredit kepada sektor privat mempengaruhi pertumbuhan ekonomi regional, namun tidak terdapat bukti kuat yang menjelaskan hubungan sebaliknya.
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10

Sekolah Tinggi Ilmu Ekonomi Y.A.I. Jakarta, Zara Tania Rahmadi, and Adji Suratman Sekolah Tinggi Ilmu Ekonomi Y.A.I. Jakarta. "JUMPA Vol.3 No.1 Feb 2016 ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI DIVIDEND PAYOUT RATIO (DPR) DI PERUSAHAAN GO PUBLICK YANG TERDAFTAR DI BURSA EFEK INDONESIA SELAIN JASA KEUANGANDENGAN MODEL VECTOR AUTOREGRESIVE PERIODE 2009 -2013." Jurnal Manajemen dan Perbankan (JUMPA) 3, no. 1 (2016): 69–86. http://dx.doi.org/10.55963/jumpa.v3i1.192.

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This research aimed to analyze the influence of factors such as current ratio, debt to asset ratio, debt to equity ratio, return on assets and return on equity of the company dividend policy with vector autoregressive models as a tool of analysis, Sample of this research is the company to go public in addition to the financial services listed in the Indonesia Stock Exchange (BEI) as many as 25 companies were observed for five years (2009-2013).The analytical method used in this research is panel data regression model (a combination of time series and cross section) and vector autoregressive models (VAR) by outlining this research model into several sub-structural.
 The research results with the VAR model shows that the current ratio, debt to asset ratio, return on assets have a significant influence on the dividend payout ratio and debt to equity ratio, return on equity does not have a significant influence on the dividend payout ratio.so itcan be concluded that this is dueif therate of return on asset sincreased currentm ratio automatically rises and dividend payments can be smoothlypaid outso as to provide symmetry between these variables.
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