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Journal articles on the topic 'Cointegrated VAR'

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Published: 4 June 2021
Last updated: 13 February 2022

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1

Warne, Anders. "Inference in Cointegrated VAR Systems." Review of Economics and Statistics 79, no. 3 (August 1997): 508–11. http://dx.doi.org/10.1162/003465300556922.

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2

Juselius, Katarina. "HAAVELMO’S PROBABILITY APPROACH AND THE COINTEGRATED VAR." Econometric Theory 31, no. 2 (July 8, 2014): 213–32. http://dx.doi.org/10.1017/s0266466614000279.

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Some key econometric concepts and problems of great importance to Trygve Haavelmo and Ragnar Frisch are discussed within the general framework of a cointegrated VAR. The focus is on problems typical of time-series data such as multicollinearity, spurious correlation and regression, time dependent residuals, model selection, missing simultaneity, autonomy, and identification. The paper argues that the more recent development of unit root econometrics has been instrumental for a solution to the above problems.
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3

Engsted, Tom. "Explosive bubbles in the cointegrated VAR model." Finance Research Letters 3, no. 2 (June 2006): 154–62. http://dx.doi.org/10.1016/j.frl.2006.03.004.

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4

Saikkonen, Pentti, and HELMUT Lütkepohl. "Infinite-Order Cointegrated Vector Autoregressive Processes." Econometric Theory 12, no. 5 (December 1996): 814–44. http://dx.doi.org/10.1017/s0266466600007179.

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Estimation of cointegrated systems via autoregressive approximation is considered in the framework developed by Saikkonen (1992, Econometric Theory 8, 1-27). The asymptotic properties of the estimated coefficients of the autoregressive error correction model (ECM) and the pure vector autoregressive (VAR) representations are derived under the assumption that the autoregressive order goes to infinity with the sample size. These coefficients are often used for analyzing the relationships between the variables; therefore, they are important for applied work. Tests for linear restrictions on the coefficients of both the ECM and the pure VAR representation are considered under the present assumptions. It is found that they have limiting x2 distributions. Tests are also derived under the assumption that the number of restrictions goes to infinity with the sample size.
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5

Johansen, Søren, and Morten Ørregaard Nielsen. "Nonstationary Cointegration in the Fractionally Cointegrated VAR Model." Journal of Time Series Analysis 40, no. 4 (December 2, 2018): 519–43. http://dx.doi.org/10.1111/jtsa.12438.

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6

Dolado, Juan J., and Helmut Lütkepohl. "Making wald tests work for cointegrated VAR systems." Econometric Reviews 15, no. 4 (January 1996): 369–86. http://dx.doi.org/10.1080/07474939608800362.

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7

SACHDEVA, J. K., and Jyoti Nair. "Cointegration of East Asian, Indian and European Markets– A Study of Impact on Indian Bourses." Journal of Global Economy 14, no. 1 (November 8, 2018): 3–27. http://dx.doi.org/10.1956/jge.v14i1.490.

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With huge investments flowing from all over the world to India, FIIs (Foreign Institutional Investors) and DIIs (Domestic Institutional Investors), retail investors, investment advisors, brokers and portfolio consultants keep abreast with latest research on fundamentals and technicals. Interdependence between stock markets is an important aspect of international portfolio management. In this paper, impact of Asian Indices like Hang Sang, KOSPI, SET SIT and TSEC on opening prices of Indian index Nifty was studied with various tools like Johansen Cointegration Test, VAR Granger Causality and Pairwise Granger Causality test. Similarly impact of European indices like CAC, FTSE, Euronext, DAX and SMI on Nifty closing prices were studied with same tools. The 3 months, 6 months, one year and 5 year data were subjected to experiment whether series are cointegrated. It was observed that series are cointegrated at very short-term level but for longer period they are not cointegrated, however, they influence others. VAR Granger Causality Test and Pairwise Granger Causality reveal that Hang Sang, KOSPI, SIT and TW (TSEC of Taiwan) impact Nifty Open prices. Nifty influences only TW. KOSPI influences Hang Sang and SET. SET influences KOSPI and TW. Similarly, VAR Granger Causality Test and Pairwise Granger Causality also reveal Nifty closing prices influence CAC, DAX, FTSE
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8

Hansen, Henrik, and Søren Johansen. "Some tests for parameter constancy in cointegrated VAR‐models." Econometrics Journal 2, no. 2 (December 1, 1999): 306–33. http://dx.doi.org/10.1111/1368-423x.00035.

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9

Pétursson, Thórarinn G., and Torsten Sløk. "Wage formation and employment in a cointegrated VAR model." Econometrics Journal 4, no. 2 (December 1, 2001): 191–209. http://dx.doi.org/10.1111/1368-423x.00062.

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10

Camarero, M., J. Ordónez, and C. R. Tamarit. "Monetary transmission in Spain: a structural cointegrated VAR approach." Applied Economics 34, no. 17 (November 2002): 2201–12. http://dx.doi.org/10.1080/00036840210138419.

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11

Crowder, William J., and Mark E. Wohar. "A cointegrated structural VAR model of the Canadian economy." Applied Economics 36, no. 3 (February 20, 2004): 195–213. http://dx.doi.org/10.1080/0003684042000175325.

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12

Dolado, Juan J. "A note on weak exogeneity in VAR cointegrated models." Economics Letters 38, no. 2 (February 1992): 139–43. http://dx.doi.org/10.1016/0165-1765(92)90044-y.

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13

Moon, Choon-Geol, and Parul Jain. "Macroeconomic aspects of Korea's liberalization policy: A cointegrated VAR study." Journal of Asian Economics 6, no. 4 (December 1995): 469–92. http://dx.doi.org/10.1016/1049-0078(95)90025-x.

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14

Pelipas, Igor. "Money demand and inflation in Belarus: Evidence from cointegrated VAR." Research in International Business and Finance 20, no. 2 (June 2006): 200–214. http://dx.doi.org/10.1016/j.ribaf.2005.09.002.

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15

Boug, Pål, and Andreas Fagereng. "Exchange rate volatility and export performance: a cointegrated VAR approach." Applied Economics 42, no. 7 (March 2010): 851–64. http://dx.doi.org/10.1080/00036840802600491.

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16

Di Sanzo, Silvestro, Mariano Bella, and Giovanni Graziano. "Tax Structure and Economic Growth: A Panel Cointegrated VAR Analysis." Italian Economic Journal 3, no. 2 (June 7, 2017): 239–53. http://dx.doi.org/10.1007/s40797-017-0056-0.

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17

Bellini, Tiziano. "The forward search interactive outlier detection in cointegrated VAR analysis." Advances in Data Analysis and Classification 10, no. 3 (September 18, 2015): 351–73. http://dx.doi.org/10.1007/s11634-015-0216-8.

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18

Kivedal, Bjørnar Karlsen. "A DSGE model with housing in the cointegrated VAR framework." Empirical Economics 47, no. 3 (November 22, 2013): 853–80. http://dx.doi.org/10.1007/s00181-013-0765-7.

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19

Park, Joon Y., and Peter C. B. Phillips. "Statistical Inference in Regressions with Integrated Processes: Part 2." Econometric Theory 5, no. 1 (April 1989): 95–131. http://dx.doi.org/10.1017/s0266466600012287.

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This paper continues the theoretical investigation of Park and Phillips. We develop an asymptotic theory of regression for multivariate linear models that accommodates integrated processes of different orders, nonzero means, drifts, time trends, and cointegrated regressors. The framework of analysis is general but has a common architecture that helps to simplify and codify what would otherwise be a myriad of isolated results. A good deal of earlier research by the authors and by others comes within the new framework. Special models of some importance are considered in detail, such as VAR systems with multiple lags and cointegrated variates.
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20

Lange, Ronald Henry. "Monetary Policy Behaviour over the Long Run in a Small Open Economy: A Markov-Switching Vector Error-Correction Approach." Research in Applied Economics 10, no. 3 (August 25, 2018): 69. http://dx.doi.org/10.5296/rae.v10i3.13223.

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This study identifies a long-run equilibrium relationship among important information variables with stochastic trends for monetary policy in Canada. The variables serve as both target policy variables for the domestic macroeconomy and reaction variables to external economic disturbances. The parameters of the cointegrated vector of information variables are found to be quite stable. A Markov-switching cointegrated VAR model captures two stochastic policy regimes with low- and high-variances. The weighting matrix for the error-correction terms for both inflation and output are found to be relatively stable across regimes, while the monetary policy rate is found to exhibit asymmetric behavior with error-correction adjustment only in the current low-variance regime.
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21

Kurita, Takamitsu. "Likelihood-Based Inference for Weak Exogeneity inI(2) Cointegrated VAR Models." Econometric Reviews 31, no. 3 (May 2012): 325–60. http://dx.doi.org/10.1080/07474938.2011.607346.

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22

todani, k. r. "LONG-RUN M3 DEMAND IN SOUTH AFRICA: A COINTEGRATED VAR MODEL." South African Journal of Economics 75, no. 4 (December 2007): 681–92. http://dx.doi.org/10.1111/j.1813-6982.2007.00150.x.

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23

Jones, Maggie E. C., Morten Ørregaard Nielsen, and Michał Ksawery Popiel. "A fractionally cointegrated VAR analysis of economic voting and political support." Canadian Journal of Economics/Revue canadienne d'économique 47, no. 4 (November 2014): 1078–130. http://dx.doi.org/10.1111/caje.12115.

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24

Pétursson, Thórarinn G. "The representative household's demand for money in a cointegrated VAR model." Econometrics Journal 3, no. 2 (December 1, 2000): 162–76. http://dx.doi.org/10.1111/1368-423x.00044.

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25

Signoretto, M., and J. A. K. Suykens. "Convex Estimation of Cointegrated VAR Models by a Nuclear Norm Penalty." IFAC Proceedings Volumes 45, no. 16 (July 2012): 95–100. http://dx.doi.org/10.3182/20120711-3-be-2027.00322.

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26

Nielsen, Heino Bohn. "Influential observations in cointegrated VAR models: Danish money demand 1973–2003." Econometrics Journal 11, no. 1 (March 2008): 39–57. http://dx.doi.org/10.1111/j.1368-423x.2007.00226.x.

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27

Ejem, Chukwu Agwu, Bonaventure Ofasia Oriko, and Ogechi Blessing Nwakodo. "Does Recurrent Expenditure Drive Growth In Nigeria? A Cointegrated Var Approach." International Journal of Scientific and Research Publications (IJSRP) 9, no. 6 (June 12, 2019): p9085. http://dx.doi.org/10.29322/ijsrp.9.06.2019.p9085.

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28

Assenmacher-Wesche, Katrin. "Modeling Monetary Transmission in Switzerland with a Structural Cointegrated VAR Model." Swiss Journal of Economics and Statistics 144, no. 2 (January 2, 2008): 197–246. http://dx.doi.org/10.1007/bf03399253.

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29

Hetland, Andreas. "The Stochastic Stationary Root Model." Econometrics 6, no. 3 (August 21, 2018): 39. http://dx.doi.org/10.3390/econometrics6030039.

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We propose and study the stochastic stationary root model. The model resembles the cointegrated VAR model but is novel in that: (i) the stationary relations follow a random coefficient autoregressive process, i.e., exhibhits heavy-tailed dynamics, and (ii) the system is observed with measurement error. Unlike the cointegrated VAR model, estimation and inference for the SSR model is complicated by a lack of closed-form expressions for the likelihood function and its derivatives. To overcome this, we introduce particle filter-based approximations of the log-likelihood function, sample score, and observed Information matrix. These enable us to approximate the ML estimator via stochastic approximation and to conduct inference via the approximated observed Information matrix. We conjecture the asymptotic properties of the ML estimator and conduct a simulation study to investigate the validity of the conjecture. Model diagnostics to assess model fit are considered. Finally, we present an empirical application to the 10-year government bond rates in Germany and Greece during the period from January 1999 to February 2018.
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30

Hashim, Emilda, Norimah Rambeli, Asmawi Hashim, Norasibah Abdul Jalil, Shahrun Nizam Abdul Aziz, and Noor Al Huda Abdul Karim. "Dynamic Relationship Between Real Export, Real Import, Real Exchange Rate, Labor Force and Real Gross Domestic Product in Malaysia." Research in World Economy 10, no. 5 (December 24, 2019): 20. http://dx.doi.org/10.5430/rwe.v10n5p20.

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This study examined short run and long run relationship between endogenous and exogenous variables. Specifically, it studied the relationship between real export, real import, labor force participation and real effective exchange rate (REER) and real GDP in Malaysia from 1988 to 2017. These variables were tested in various tests, namely, unit root test, granger causality test, vector autoregressive (VAR), Johansen Juselius test and Error Correction Term (ECT). The result revealed that all variables were non-stationary at the level form and stationary at first difference in ADF unit root test. The findings also exhibited the existence of bilateral relationships between real export and real GDP, real import and real GDP, as well as labor and real GDP. Nonetheless, there were no relationship found between REER and real GDP. On the other hand, in VAR, the lag optimum was lag 10 because it indicated the smallest value of AIC. Moreover, for Johansen Juselius cointegration test, it showed two cointegrated vector at both, 5% and 1%, level in trace test. In addition, Max-Eigen value test indicated two cointegrated vector at 0.05 and one cointegrated vector at 0.01. As for the Wald test, there were long run cointegration relationship between real GDP and its determinants, namely real export, real import, labor and REER. Apparently, Malaysia, as a small open economy, has relied heavily on foreign trade. Consequently, our domestic economic performance is susceptible to the changes in international markets and exchange rate. Therefore, suitable international policy implementation is vital to ensure Malaysian economy will be able to adjust to current global changes.
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31

Arai, Yoichi, and Taku Yamamoto. "Alternative representation for asymptotic distributions of impulse responses in cointegrated VAR systems." Economics Letters 67, no. 3 (June 2000): 261–71. http://dx.doi.org/10.1016/s0165-1765(99)00278-5.

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32

Martin, Bernhard, and Svetlozar T. Rachev. "A Stable Cointegrated Var Model for Credit Returns with Time-Varying Volatility." IFAC Proceedings Volumes 34, no. 20 (September 2001): 207–12. http://dx.doi.org/10.1016/s1474-6670(17)33066-5.

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33

Giese, Julia V. "Level, Slope, Curvature: Characterising the Yield Curve in a Cointegrated VAR Model." Economics: The Open-Access, Open-Assessment E-Journal 2, no. 2008-28 (2008): 1. http://dx.doi.org/10.5018/economics-ejournal.ja.2008-28.

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34

Dolatabadi, Sepideh, Morten Ørregaard Nielsen, and Ke Xu. "A Fractionally Cointegrated VAR Analysis of Price Discovery in Commodity Futures Markets." Journal of Futures Markets 35, no. 4 (October 27, 2014): 339–56. http://dx.doi.org/10.1002/fut.21693.

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35

Dolatabadi, Sepideh, Paresh Kumar Narayan, Morten Ørregaard Nielsen, and Ke Xu. "Economic significance of commodity return forecasts from the fractionally cointegrated VAR model." Journal of Futures Markets 38, no. 2 (September 18, 2017): 219–42. http://dx.doi.org/10.1002/fut.21866.

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36

Doornik, Jurgen A. "Accelerated Estimation of Switching Algorithms: The Cointegrated VAR Model and Other Applications." Scandinavian Journal of Statistics 45, no. 2 (February 20, 2018): 283–300. http://dx.doi.org/10.1111/sjos.12311.

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37

Belke, Ansgar, Ingo G. Bordon, and Torben W. Hendricks. "Global liquidity and commodity prices–a cointegrated VAR approach for OECD countries." Applied Financial Economics 20, no. 3 (February 2010): 227–42. http://dx.doi.org/10.1080/09603100903282713.

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38

Tang, Bo. "Real exchange rate and economic growth in China: A cointegrated VAR approach." China Economic Review 34 (July 2015): 293–310. http://dx.doi.org/10.1016/j.chieco.2014.12.002.

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39

Belke, Ansgar, and Joscha Beckmann. "Monetary policy and stock prices – Cross-country evidence from cointegrated VAR models." Journal of Banking & Finance 54 (May 2015): 254–65. http://dx.doi.org/10.1016/j.jbankfin.2014.12.004.

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40

van Garderen, Kees Jan, and H. Peter Boswijk. "Bias correcting adjustment coefficients in a cointegrated VAR with known cointegrating vectors." Economics Letters 122, no. 2 (February 2014): 224–28. http://dx.doi.org/10.1016/j.econlet.2013.12.003.

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41

Dal Colle, Alessandra. "Finance–growth nexus: does causality withstand financial liberalization? Evidence from cointegrated VAR." Empirical Economics 41, no. 1 (December 29, 2010): 127–54. http://dx.doi.org/10.1007/s00181-010-0439-7.

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42

Kurita, Takamitsu. "A note on potential one-way policy instruments in cointegrated VAR systems." Economic Analysis and Policy 58 (June 2018): 55–59. http://dx.doi.org/10.1016/j.eap.2017.12.004.

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43

Lütkepohl, Helmut, and Hans-Eggert Reimers. "Granger-causality in cointegrated VAR processes The case of the term structure." Economics Letters 40, no. 3 (November 1992): 263–68. http://dx.doi.org/10.1016/0165-1765(92)90002-g.

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44

Papaikonomou, Dimitrios, and Jacinta Pires. "Are US output expectations unbiased? A cointegrated VAR analysis in real time." Economics Letters 92, no. 3 (September 2006): 440–46. http://dx.doi.org/10.1016/j.econlet.2006.03.036.

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45

Kitamura, Yuichi. "LIKELIHOOD-BASED INFERENCE IN COINTEGRATED VECTOR AUTOREGRESSIVE MODELS." Econometric Theory 14, no. 4 (August 1998): 517–24. http://dx.doi.org/10.1017/s0266466698144067.

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Since the notion of cointegration was established by Engel and Granger (1987), many statistical methods have been suggested to estimate and test cointegrated models. Undoubtedly the Gaussian likelihood–based method advocated by Johansen (1988, 1991) is one of the most popular choices among practitioners. In his 1988 paper, Johansen applied Anderson's (1951) maximum likelihood estimation procedure for reduced rank regression (RRR) models to isolate common stochastic trends in multiple time series. This was a remarkable breakthrough, which he and other authors have extended into various directions in the last decade. Johansen's approach is attractive in that it provides a unified set of tools of estimation, cointegration rank testing, and parametric hypothesis testing, based on the Gaussian likelihood for a vector autoregression (VAR). Although this book and other papers of Johansen are mostly concerned with reduced form models, the statistical information provided by his method is useful for applied econometricians, especially in fields where tractable dynamic structural models are not available. This book presents a concise yet comprehensive treatment of his methodology.
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46

Belke, Ansgar,, and Robert Czudaj. "Is Euro Area Money Demand (Still) Stable? Cointegrated VAR Versus Single Equation Techniques." Applied Economics Quarterly 56, no. 4 (October 2010): 285–315. http://dx.doi.org/10.3790/aeq.56.4.285.

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47

Carlini, Federico, and Paolo Santucci de Magistris. "On the Identification of Fractionally Cointegrated VAR Models With the F(d) Condition." Journal of Business & Economic Statistics 37, no. 1 (June 12, 2017): 134–46. http://dx.doi.org/10.1080/07350015.2017.1294077.

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48

Heinlein, Reinhold, and Hans-Martin Krolzig. "MONETARY POLICY AND EXCHANGE RATES: A BALANCED TWO-COUNTRY COINTEGRATED VAR MODEL APPROACH." Macroeconomic Dynamics 23, no. 5 (September 20, 2017): 1838–74. http://dx.doi.org/10.1017/s1365100517000475.

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We study the exchange rate effects of monetary policy in a balanced macroeconometric two-country model for the United States and United Kingdom. In contrast to the empirical literature, which consistently treats the domestic and foreign countries unequally in the modeling process, we consider full model feedback, allowing for a thorough analysis of the system dynamics. The problem of model dimensionality is tackled by invoking the approach by Aoki (1981). Assuming country symmetry in the long run allows to decouple the two-country macrodynamics of country averages and differences such that the cointegration analysis can be applied to smaller systems. Second, the econometric modeling is general-to-specific, a graph-theoretic approach for the contemporaneous effects combined with automatic general-to-specific model selection. We find delayed overshooting of the exchange rate in the case of a Bank of England monetary shock but instantaneous response to a Fed shock. Altogether the response is more pronounced in the former case.
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49

Shukur, Ghazi, and Panagiotis Mantalos. "A simple investigation of the Granger-causality test in integrated-cointegrated VAR systems." Journal of Applied Statistics 27, no. 8 (November 2000): 1021–31. http://dx.doi.org/10.1080/02664760050173346.

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50

Mangee, Nicholas, and Michael D. Goldberg. "A Cointegrated VAR Analysis of Stock Price Models: Fundamentals, Psychology and Structural Change." Journal of Behavioral Finance 21, no. 4 (November 28, 2019): 352–68. http://dx.doi.org/10.1080/15427560.2019.1692844.

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