On the Asymptotic Distribution of Impulse Response Functions With Long-Run Restrictions

Vlaar, Peter

doi:10.1017/s0266466604205047

Cited by 26 publications

(21 citation statements)

References 25 publications

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“…This section provides a brief summary of Vlaar (1998). Let x t be a n−dimensional vector of variables which is generated by an unrestricted vector autoregressive (VAR) process of order k…”

Section: Appendix C S-vecm Methodologymentioning

confidence: 99%

“…A structural vector error correction model (SVECM) is estimated using the methodology suggested by Vlaar (1998). Based on the system with five endogenous variables the transmission of five shocks to the economic variables are considered, i.e.…”

Section: Identifying Restrictionsmentioning

confidence: 99%

“…Therefore, the analysis presented here extends the multivariate approach applied to a German macroeconomic system in Hubrich (1999Hubrich ( , 2001) employing the long-run relationships analysed there, that is a money demand relation, a Fisher relation as well as an interest rate spread, as a basis for modelling a dynamic cointegrated system for Germany. For this purpose the structural vector error correction -6 -model (S-VECM) approach suggested by Vlaar (1998), that incorporates restrictions on the cointegration vectors, will be applied to analyse the transmission of shocks in the economy. The results from the analysis of the transmission of monetary policy in Germany will be compared with analyses on the basis of aggregated European data prior to EMU, especially with Vlaar and Schuberth (1999) and Coenen and Vega (1999).…”

Section: Introductionmentioning

confidence: 99%

“…In the second step contemporaneous as well as long-run restrictions can be imposed to identify the shocks that affect the economy. Moreover, Vlaar (1998) derives the correct asymptotic distribution of the confidence bands in case of long-run restrictions for identifying the VAR using an idea proposed by Mittnik and Zadrozny (1993). This approach is chosen in the present paper.…”

Section: Introductionmentioning

confidence: 99%

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Monetary transmission in Germany: Lessons for the Euro area

Hubrich

Vlaar

2004

Empirical Economics

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This study analyses the transmission of monetary policy in Germany for the EMS period in the framework of a structural vector error correction model (S-VECM). Three stable cointegration relationships are found: a money demand relation, an interest rate spread and a stationary real interest rate. Based on both contemporaneous and long-run restrictions, five structural shocks to the economy are identified.In contrast to analyses for the euro area, we find that output and inflation are not independent in the long run for Germany. In accordance with results previously found for Europe, we do not find strong support for monetary targeting for Germany. Our analysis indicates that uncertainties remain concerning the controllability of money and its usefulness as a leading indicator with respect to inflation. Stability of the money demand relationship does not seem to be problematic.

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Section: Appendix C S-vecm Methodologymentioning

confidence: 99%

Section: Identifying Restrictionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monetary transmission in Germany: Lessons for the Euro area

Hubrich

Vlaar

2004

Empirical Economics

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“…According to Vlaar (2004), if the reduced form parameters are asymptotically Normal, the estimated impulse responses will also have an asymptotic Normal distribution since the impulse responses are a nonlinear function of the reduced form VEC parameters, x Barro argues that the government spending multiplier has never exceeded one, even during WWII.…”

Section: Statistical Properties Of Multipliersmentioning

confidence: 99%

Calculating and interpreting multipliers in the presence of non-Stationary time series: The case of U.S. federal infrastructure spending

Stevans¹,

Sessions²

2010

AJSMS

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In this analysis, we discuss two measures that have been used by economists to measure longrun changes in macroeconomic policies: the dynamic multiplier and the impulse response function. In the presence of (1) I variables, if the multipliers/impulse responses are generated from a VAR model, there can be substantial biases at both the long and short-run horizons. In this paper, the influence that infrastructure spending will have on U.S. economy from 2009 to 2010 is examined by modeling selected macroeconomic variables as a Vector Error Correction (VEC) model. The impulse response function will then be used to generate government spending multipliers and bootstrapped confidence intervals. Findings indicate a Federal infrastructure spending multiplier of 3.33 at the end of a two year period.

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Structural FECM: Cointegration in large‐scale structural FAVAR models

Banerjee

Marcellino

Masten

2017

J of Applied Econometrics

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Starting from the dynamic factor model for nonstationary data we derive the factor-augmented error correction model (FECM) and its moving-average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long-run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a factor-augmented vector autoregressive (FAVAR) model is positively related to the strength of the error correction mechanism and the cross-section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified permanent real (productivity) and monetary policy shocks. 1 Forni, Giannone, Lippi, and Forni et al. (2009) provide an empirical illustration of the stochastic trends analysis of King, Plosser, Stock and Watson (1991) in the context of large stationary panels. Eickmeier (2009) works with a nonstationary panel and identification of structural shocks with sign restrictions. Forni,

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On the Asymptotic Distribution of Impulse Response Functions With Long-Run Restrictions

Cited by 26 publications

References 25 publications

Monetary transmission in Germany: Lessons for the Euro area

Monetary transmission in Germany: Lessons for the Euro area

Calculating and interpreting multipliers in the presence of non-Stationary time series: The case of U.S. federal infrastructure spending

Structural FECM: Cointegration in large‐scale structural FAVAR models

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