135 research outputs found
Uncovered Interest Rate Parity and the Expectations Hypothesis of the Term Structure: Empirical Results for the U.S. and Europe
A system of U.S. and euro area short- and long-term interest rates is analyzed. According to the expectations hypothesis of the term structure the interest rate spreads should be stationary and according to the uncovered interest rate parity the difference between the U.S. and euro area longterm interest rates should also be stationary. If all four interest rates are integrated of order one, one would expect to find three linearly independent cointegration relations in the system of four interest rate series. Combining German and European Monetary Union data to obtain the euro area interest rate series we find indeed the theoretically expected three cointegration relations, in contrast to previous studies based on different data sets.Expectations hypothesis of the term structure, uncovered interest rate parity, unit roots, cointegration analysis
Calculating Joint Confidence Bands for Impulse Response Functions using Highest Density Regions
This paper proposes a new non-parametric method of constructing joint confidence bands for impulse response functions of vector autoregressive models.The estimation uncertainty is captured by means of bootstrapping and the highest density region (HDR) approach is used to construct the bands. A
Monte Carlo comparison of the HDR bands with existing alternatives shows
that the former are competitive with the bootstrap-based Bonferroni and
Wald confidence regions. The relative tightness of the HDR bands matched
with their good coverage properties makes them attractive for applications.
An application to corporate bond spreads for Germany highlights the potential
for empirical work
Comparison of Methods for Constructing Joint Confidence Bands for Impulse Response Functions
In vector autoregressive analysis confidence intervals for individual
impulse responses are typically reported to indicate the sampling uncertainty
in the estimation results. A range of methods are reviewed and a
new proposal is made for constructing joint confidence bands, given a prespecified coverage level, for the impulse responses at all horizons considered simultaneously. The methods are compared in a simulation experiment and recommendations for empirical work are provided
Testing for the Cointegrating Rank of a VAR Process with Level Shift and Trend Break
A test for the cointegrating rank of a vector autoregressive (VAR) process with a possible shift and broken linear trend is proposed. The break point is assumed to be known. The setup is a VAR process for cointegrated variables. The tests are not likelihood ratio tests but the deterministic terms including the broken trends are removed first by a GLS procedure and a likelihood ratio type test is applied to the adjusted series. The asymptotic null distribution of the test is derived and it is shown by a Monte Carlo experiment that the test has better small sample properties in many cases than a corresponding Gaussian likelihood ratio test for the cointegrating rank.Cointegration, structural break, vector autoregressive process, error correction model
Comparison of Model Reduction Methods for VAR Processes
The objective of this study is to compare alternative computerized model-selection strategies in the context of the vector autoregressive (VAR) modeling framework. The focus is on a comparison of subset modeling strategies with the general-to-specific reduction approach automated by PcGets. Different measures of the possible gains of model selection are considered: (i) the chances of finding the `correct' model, that is, a model which contains all necessary right-hand side variables and is as parsimonious as possible, (ii) the accuracy of the implied impulse-responses and (iii) the forecast performance of the models obtained with different specification algorithms. In the Monte Carlo experiments, the procedures recover the DGP specification from a large VAR with anticipated size and power close to commencing from the DGP itself when evaluated at the empirical size. We find that subset strategies and PcGets are close competitors in many respects, with the forecast comparison indicating a clear advantage of the PcGets algorithm.Model selection, Vector autoregression, Subset model, Lag order determination, Data mining
Have the effects of shocks to oil price expectations changed?: Evidence from heteroskedastic proxy vector autoregressions
Studies of the crude oil market based on structural vector autoregressive (VAR) models typically assume a time-invariant model and transmission of shocks and possibly allow for heteroskedasticity by using robust inference procedures. We assume a heteroskedastic reduced-form VAR model with time-invariant slope coefficients and explicitly consider the possibility of time-varying shock transmission due to heteroskedasticity. We study a model for the global crude oil market that includes key world and U.S. macroeconomic variables and find evidence for changes in the transmission of shocks to oil price expectations during the last decades which can be attributed to heteroskedasticity
Testing for the cointegrating rank of a var process with an intercept
This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Testing the cointegrating rank of a vector autoregressive process with an intercept is considered. In addition to the likelihood ratio (LR) tests developed by Johansen and Juselius (1990, Oxford Bulletin of Economics and Statistics, 52, 169–210) and others we also consider an alternative class of tests that is based on estimating the trend parameters of the deterministic term in a different way. The asymptotic local power of these tests is derived and compared to that of the corresponding LR tests. The small sample properties are investigated by simulations. The new tests are seen to be substantially more powerful than conventional LR tests.Peer Reviewe
Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity
In this study, Bayesian inference is developed for structural vector
autoregressive models in which the structural parameters are identified via
Markov-switching heteroskedasticity. In such a model, restrictions that are
just-identifying in the homoskedastic case, become over-identifying and can be
tested. A set of parametric restrictions is derived under which the structural
matrix is globally or partially identified and a Savage-Dickey density ratio is
used to assess the validity of the identification conditions. The latter is
facilitated by analytical derivations that make the computations fast and
numerical standard errors small. As an empirical example, monetary models are
compared using heteroskedasticity as an additional device for identification.
The empirical results support models with money in the interest rate reaction
function.Comment: Keywords: Identification Through Heteroskedasticity, Bayesian
Hypotheses Assessment, Markov-switching Models, Mixture Models, Regime Chang
Heteroskedastic proxy vector autoregressions: An identification-robust test for time-varying impulse responses in the presence of multiple proxies
We propose a test for time-varying impulse responses in heteroskedastic structural vector autoregressions that can be used when the shocks are identified by external proxy variables as a group but not necessarily individually. The test is robust to the identification scheme for identifying the shocks individually and can be used even if the shocks are not identified individually. The asymptotic analysis is supported by small sample simulations which show good properties of the test. An investigation of the impact of productivity shocks in a small macroeconomic model for the U.S. illustrates the importance of the issue for empirical work
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