Direct cointegration testing in error correction models

1992

2010

In this paper, we consider a general class of vector error correction models which allow for asymmetric and non-linear error correction. We provide asymptotic results for (quasi-)maximum likelihood (QML) based estimators and tests. General hypothesis testing is considered, where testing for linearity is of particular interest as parameters of non-linear components vanish under the null. To solve the latter type of testing, we use the so-called sup tests, which here requires development of new (uniform) weak convergence results. These results are potentially useful in general for analysis of nonstationary non-linear time series models. Thus the paper provides a full asymptotic theory for estimators as well as standard and non-standard test statistics. The derived asymptotic results prove to be new compared to results found elsewhere in the literature due to the impact of the estimated cointegration relations. With respect to testing, this makes implementation of testing involved, and...

PLOS ONE, 2022

This paper evaluates the performance of eight tests with null hypothesis of cointegration on basis of probabilities of type I and II errors using Monte Carlo simulations. This study uses a variety of 132 different data generations covering three cases of deterministic part and four sample sizes. The three cases of deterministic part considered are: absence of both intercept and linear time trend, presence of only the intercept and presence of both the intercept and linear time trend. It is found that all of tests have either larger or smaller probabilities of type I error and concluded that tests face either problems of over rejection or under rejection, when asymptotic critical values are used. It is also concluded that use of simulated critical values leads to controlled probability of type I error. So, the use of asymptotic critical values may be avoided, and the use of simulated critical values is highly recommended. It is found and concluded that the simple LM test based on KPS...

Journal of Time Series Analysis, 1997

A residual-based test for cointegration is proposed where a parametric adjustment is made to account for the possible stationarity of the disturbance vector. Allowance is also made for the regressor variables to be cointegrated among themselves. The parametric adjustment turns out to be more robust and powerful than tests based on long-run variance estimators according to theoretical and simulation evidence.

2003

Part 1. Automated model selection Contents Chapter 2. Automatic identification of simultaneous equations models 2.1. Introduction 22. Results 2.3, Algorithm 2.4. An application 2.5. Conclusions 2;A. Proofs 2.B. Matlab program Chapter 3. Automatic identification and restriction of the cointegration space 3d. Introduction 3.2. The cointegrated VAR model 3.3. Identification and restriction of (3 3.4. Monte Carlo evidence 3.5. Use of the algorithm 3.6. Conclusions 3;A. Proofs Part 2. Small sample corrections Chapter 4. Bootstrapping and Bartlett corrections in the cointegrated VAR model 4.1. Introduction 4;2. Bartlett-corrected and bootstrap tests on cointegrating coefficients 4.3. Design of the Monte Carlo experiment 4.4. Results 4.5. Conclusions Chapter 5. A Bartlett correction in stationary autoregressive models Omtzigt, Pieter (2003), Essays on Cointegration Analysis

Economics Letters, 1998

Tests of cointegrating coefficients in vector autoregressive error correction models ignore the Cauchy-like behavior of the 2 estimator's finite-sample distribution. This causes excessive rejections of the null in standard x tests. We propose a 2 Cauchy-based x test, and show, via simulation, that it yields adequate rejection rates.

Review of Economics and Statistics, 1998

System tests for cointegration proposed by Stock and Watson (1988), Johansen (1988), and Bewley and Yang (1995) are compared using Monte Carlo experiments that include overspecification of the lag length and data-generating processes with moving average disturbances. Both AIC and SIC are used to select the lag length of the approximating vector autoregressions. The three tests considered are found to have very different characteristics, and each dominates in some portion of the parameter space.

2018

In this article ten cointegration tests based on residuals of cointegrating equation are compared on basis of stringency criterion: a robust technique for comparison of tests using Monte Carlo simulations. Two tests i.e. Phillips and Ouliaris'ˆu P and Choi Durbin-Hausman statistic are the leading performers and are recommended for any sample size. The remaining eight tests are recommended for only large sample sizes of 200 or greater. The use of all these ten tests is not recommended when presence of both intercept and linear time trend is assumed in cointegrating equation unless the sample size is very large i.e. greater than 200.

1997

This paper proposes a new test for cointegration In a single-equation framework where the regressors are weakly exogenous for the parameters of interest. The test is denoted as ECM test and is based upon the OLS coefficient of the lagged dependent variable in an autoregressive-distributed lag model augmented with leads of the regressors. The limit distributions of the standardised coefficient and t-ratio versions of the ECM tests are obtained and critical values are provided. These limit distributions do not depend upon nuisance parameters but they depend on the number of regressors. Finally, we compare their power properties with those of other cointegration tests available in the literature and find under which circumstances the ECM tests have a better performance.

Econometric Reviews, 2003

The aim of this paper is to compare the relative performance of several tests for the null hypothesis of cointegration, in terms of size and power in finite samples. This is carried out using Monte Carlo simulations for a range of plausible data-generating processes. We also analyze the impact on size and power of choosing different procedures to estimate the long run variance of the errors. We found that the parametrically adjusted test of is the most well-balanced test, displaying good power and relatively few size distortions.

2015

This study examines the relationships between the employment and foreign direct investment (FDI) in Malaysia. The Malaysian government continues to put efforts in attracting more FDI inflows as it seems that FDI plays a major role in the economic development of Malaysia. Besides, there is general perception that the FDI inflow contributes to increase the employment opportunity in the country. Hence, we apply an empirical analysis to study the effect of FDI on the employment in Malaysia. The data span from 1970 to 2007. Several econometric models are applied including the bounds testing (ARDL) approach, and ECM-ARDL model. The results show that there is no cointegration relationship between employment and the FDI in the long-run. However, there is a causal

Economics Letters, 1995

In this paper it is proposed to use a non-parametric bootstrap based Bartlett correction factor for the LR test for linear restrictions on the cointegrating vectors to reduce the finite sample size distortion problem of the test statistic.

Journal of Natural Sciences Research, 2015

Investment in the stock market is long term in nature. Any development that could affect the stability of the economy u s ually has serious impact on the stock market performance. This research work examines the impact of some macroeconomic variables (Inflation, Interest and Exchange rates as well as Real Gross Domestic Product) on Nigerian stock market index. The methodologies used are cointegration and vector error correction model using annually data collected from Nigeria stock exchange fact book and Central Bank of Nigeria statistical bulletin (2013). From the results obtained the Augmented Dickey-Fuller (ADF) test reveals that all other macroeconomic indicators were stationary at the first order of difference except for SMI and RGDP that were stationary at the second order of difference, I(2). The Johansen co-integration test shows there are at least three co-integrated variables out of the five economic series considered in this study at 5% level of significance. The vector e...