Fractional Cointegration Rank Estimation
Bibliographic Data
| ID | 19417738 |
|---|---|
| Authors | Katarzyna Łasak (0000-0003-0395-9199, Vrije Universiteit Amsterdam), Carlos Velasco (0000-0001-5913-0107) |
| Year | 2015 |
| Volume | 33 |
| Issue | 2 |
| Pages | 241-254 |
| Publication date | 2015-04-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2014.945589 |
| OpenAlex | W3021927938 |
| Language | EN |
| Citations received | 1 |
| References cited | 34 |
This article considers cointegration rank estimation for a p-dimensional fractional vector error correction model. We propose a new two-step procedure that allows testing for further long-run equilibrium relations with possibly different persistence levels. The first step consists of estimating the parameters of the model under the null hypothesis of the cointegration rank r = 1, 2, ..., p − 1. This step provides consistent estimates of the order of fractional cointegration, the cointegration vectors, the speed of adjustment to the equilibrium parameters and the common trends. In the second step we carry out a sup-likelihood ratio test of no-cointegration on the estimated p − r common trends that are not cointegrated under the null. The order of fractional cointegration is reestimated in the second step to allow for new cointegration relationships with different memory. We augment the error correction model in the second step to adapt to the representation of the common trends estimated in the first step. The critical values of the proposed tests depend only on the number of common trends under the null, p − r, and on the interval of the orders of fractional cointegration b allowed in the estimation, but not on the order of fractional cointegration of already identified relationships. Hence, this reduces the set of simulations required to approximate the critical values, making this procedure convenient for practical purposes. In a Monte Carlo study we analyze the finite sample properties of our procedure and compare with alternative methods. We finally apply these methods to study the term structure of interest rates
Cointegration · Combinatorics · Econometrics · Error correction model · Monte Carlo method · Statistics · Computer Science · Financial Risk and Volatility Modeling · Global Financial Crisis and Policies · Mathematics · Monetary Policy and Economic Impact
Discretion versus policy rules in practice
Inflation forecast targeting
Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models
Inference When a Nuisance Parameter Is Not Identified Under the Null Hypothesis
Developments in the Study of Cointegrated Economic Variables
Gaussian Semiparametric Estimation of Long Range Dependence
Optimal Residual-Based Tests for Fractional Cointegration and Exchange Rate Dynamics
Statistical analysis of cointegration vectors
| Citations per year | 0.14 |
|---|---|
| Citation span | 2019 - 2019 (1) |
| Highly cited | No |