Bayesian analysis of the error correction model

Strachan, Rodney W.; Inder, Brett

doi:10.1016/j.jeconom.2003.12.004

Cited by 75 publications

(141 citation statements)

References 32 publications

Supporting

Mentioning

140

Contrasting

Order By: Relevance

“…This indeterminacy is commonly surmounted by imposing the so-called linear normalization where = [I r B 0 ] 0 . However, there are some serious drawbacks to this linear normalization (see Strachan andInder, 2004 andStrachan andvan Dijk, 2007). Researchers in this …eld (see Strachan, 2003, Strachan and Inder, 2004, Strachan and van Dijk, 2007 point out that it is only the cointegrating space that is identi…ed (not particular cointegrating vectors).…”

Section: The Time Varying Cointegration Modelmentioning

confidence: 99%

“…Accordingly, we introduce notation for the space spanned by ; p = sp ( ). In this paper, we follow Strachan and Inder (2004) by achieving identi…ca-tion by specifying to be semi-orthogonal (i.e. 0 = I).…”

Section: The Time Varying Cointegration Modelmentioning

confidence: 99%

“…Note that such an identifying restriction does not restrict the estimable cointegrating space, unlike the linear normalization. Another key result from Strachan and Inder (2004) is that a Uniform prior on will imply a Uniform prior on p.…”

Section: The Time Varying Cointegration Modelmentioning

confidence: 99%

“…To do so, we use some results from Strachan and Inder (2004), based on derivations in James (1954), on specifying priors on the cointegrating space. These were derived for the time-invariant VECM, but are useful here if we treat them as applying to a single point in time.…”

Section: Properties Of Proposed State Equationmentioning

confidence: 99%

See 3 more Smart Citations

Bayesian inference in a time varying cointegration model

Koop

Leon-Gonzalez

Strachan

2011

Journal of Econometrics

Self Cite

View full text Add to dashboard Cite

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved Vector autoregressions (VARs), ignoring cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop time varying parameter models which permit cointegration. Time-varying parameter VARs (TVP-VARs) typically use state space representations to model the evolution of parameters. In this paper, we show that it is not sensible to use straightforward extensions of TVP-VARs when allowing for cointegration. Instead we develop a speci…cation which allows for the cointegrating space to evolve over time in a manner comparable to the random walk variation used with TVP-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in an empirical investigation involving a permanent/transitory variance decomposition for in ‡ation.

show abstract

Section: The Time Varying Cointegration Modelmentioning

confidence: 99%

Section: The Time Varying Cointegration Modelmentioning

confidence: 99%

Section: The Time Varying Cointegration Modelmentioning

confidence: 99%

Section: Properties Of Proposed State Equationmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian inference in a time varying cointegration model

Koop

Leon-Gonzalez

Strachan

2011

Journal of Econometrics

Self Cite

View full text Add to dashboard Cite

show abstract

“…While the estimation of vector error correction models would allow for additional inferential possibilities, their Bayesian treatment introduces additional difficulties in terms of model specification like the appropriate choice of the number of cointegrating relationships and the specification of suitable priors on the cointegrating vectors (Strachan and Inder, 2004;Koop et al, 2009). …”

Section: Variables Data and Model Specificationmentioning

confidence: 99%

The Role of US Based FDI Flows for Global Output Dynamics

2017

View full text Add to dashboard Cite

Econometric Analysis with Vector Autoregressive Models

Lütkepohl

2009

Handbook of Computational Econometrics

View full text Add to dashboard Cite

∞ i=0 i = 0, and it is called I (d), d = 1, 2, . . . , if d y t is I (0). To simplify matters, all variables are assumed to be either I (0) or I (1) in the following, if not explicitly stated otherwise.Notice that a K-dimensional vector of time-series variables y t = (y 1t , . . . , y Kt ) is I (d), in short, y t ∼ I (d) if at least one of its components is I (d). In that case, d−1 y t will still have a stochastic trend while d y t does not. This definition does not exclude the possibility that some components of y t may be I (0) individually if y t ∼ I (1). Moreover, it is often convenient to define an I (0) process y t for t ∈ Z rather than t ∈ N in the same way as before, and I will repeatedly make use of this possibility in the following. In fact, I will assume that I (0) processes are defined for t ∈ Z in a stationary context, if not otherwise stated. On the other hand, if I (d) processes y t with d > 0 are involved, it is often easier to define them for t ∈ N, and this is therefore done here.More general definitions of integrated processes can be given. In particular, an I (0) process does not have to be a linear process, and I (d) processes for non-integer d can Kabaila P 1993 On bootstrap predictive inference for autoregressive processes. Journal of Time Series Analysis 14, 473-484. Kemp GCR 1999 The behavior of forecast errors from a nearly integrated AR(1) model as both sample size and forecast horizon become large. Econometric Theory 15, 238-256. Kilian L 1998a Confidence intervals for impulse responses under departures from normality. Econometric Reviews 17, 1-29. Kilian L 1998b Small-sample confidence intervals for impulse response functions. Review of Economics and Statistics 80, 218-230. Kilian L and Chang PL 2000 How accurate are confidence intervals for impulse responses in large VAR models?. Economics Letters 69, 299-307. Kim JH 1999 Asymptotic and bootstrap prediction regions for vector autoregression. International Journal of Forecasting 15, 393-403. King RG, Plosser CI, Stock JH and Watson MW 1991 Stochastic trends and economic fluctuations.

show abstract

Bayesian analysis of the error correction model

Cited by 75 publications

References 32 publications

Bayesian inference in a time varying cointegration model

Bayesian inference in a time varying cointegration model

The Role of US Based FDI Flows for Global Output Dynamics

Econometric Analysis with Vector Autoregressive Models

Contact Info

Product

Resources

About