Structural vector autoregressive analysis for cointegrated variables

By Helmut Lütkepohl. 2006

298 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/f81422859d27ea45c8325916a7242dbf6542568d

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Citations in the corpus

The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development (2016, background): “It is reported by Lütkepohl (2006) that the dynamic link between the series can be well captured with an appropriate selection of lag length.”
Is Energy Consumption Effective to Spur Economic Growth in Pakistan? New Evidence from Bounds Test to Level Relationships and Granger Causality Tests (2012, background): “It is argued by Lütkepohl (2005) that AIC has superior predicting properties when data sample is small like in our case of Pakistan.”
The environmental Kuzents Curve and the role of coal consumption in India: cointegration and causality analysis in an open economy (2012, unknown): “Inappropriate lag length selection provides biased results and in resulting, decision about cointegration is worthless (Lütkepohl, [58]).”
Natural Gas Consumption and Economic Growth: The Role of Foreign Direct Investment, Capital Formation and Trade Openness in Malaysia (2015, background): “It is reported by Lütkepohl, [69] that the dynamic link between the series can be captured if appropriate lag length is chosen.”
Does financial development reduce CO2 emissions in Malaysian economy? A time series analysis (2013, methodology): “Narayan (2005) pointed out that the critical bounds developed by Pesaran et al.”
Non-Stationary Dynamic Factor Models for Large Datasets (2016, methodology): “(20) where λ̂′i is the i-th row of Λ̂, k̂j is the j-th column of K̂ (see also Lütkepohl, 2006, for an explicit expression as function of the VECM parameters).”
Environmental Kuznets Curve in an Open Economy: A Bounds Testing and Causality Analysis for Tunisia (2014, background): “It is pointed by Lütkepohl, [30] that AIC has superior power properties for small sample data compared to any lag length criterion.”
How urbanization affects CO2 emissions in Malaysia? The application of STIRPAT model (2016, unknown): “We followed Akaike information criteria to select an appropriate lag length since AIC has superior power properties for small sample data compared to other lag length criteria, as argued by Lütkepohl, (2006). Besides, AIC provides efficient and consistent results compared to final prediction error (”
Financial Development and Income Inequality: Is There Any Financial Kuznets Curve in Iran? (2014, unknown): “…Hannan-Quinn Information criterion (HQ) to choose appropriate lag order but we prefer to take decision about appropriate lag after using AIC as it provides reliable and consistent information about lag order (Lütkepohl 2006) in the presence of structural breaks stemming in the mentioned series.”