scholarly journals Linearity Test with Unit Root in TV-ESTAR Framework

2021 ◽  
Vol 13 ◽  
pp. 413-418
Author(s):  
Jinqi Song
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Limit Distribution ◽  
Unit Root ◽  
Structure Change ◽  
Finite Sample ◽  
Model Framework ◽  
Sample Characteristics ◽  
Linearity Test ◽  
F Statistics

Firstly, this paper proposes F statistic whose limit distribution and critical values are also provided to test nonlinearity and structure change with unit root in TV-ESTAR model framework. The results show that the distribution of F statistic is nonstandard. Then, this paper analyzes finite sample characteristics of F statistics through the Monte Carlo simulation and founds F statistics has better power than kss statistics in Kapetanios et al to test nonlinear unit root with structure change.

Finite Sample Distributions of t and F Statistics in an AR(1) Model with Anexogenous Variable

1987 ◽  
Vol 3 (3) ◽  
pp. 387-408 ◽  
Author(s):  
J.C. Nankervis ◽  
N.E. Savin
Keyword(s):  
Monte Carlo ◽  
Time Trend ◽  
Stable Process ◽  
Asymptotic Approximations ◽  
Test Statistics ◽  
Finite Sample ◽  
Sampling Behavior ◽  
Exogenous Process ◽  
Exogenous Time Series ◽  
F Statistics

The distributions of the test statistics are investigated in the context of an AR(1) model where the root is unity or near unity and where the exogenous process is a stable process, a random walk or a time trend. The finite sample distributions are estimated by Monte Carlo methods assuming normal disturbances. The sensitivity of the distributions to both the values of the parameters of the AR(1) model and the process generating the exogenous time series is examined. The Monte Carlo results motivate several theorems which describe the exact sampling behavior of the test statistics. The analytical and empirical results present a mixed picture with respect to the accuracy of the relevant asymptotic approximations.

Canonical Cointegrating Regression and Testing for Cointegration in the Presence of I(1) and I(2) Variables

1997 ◽  
Vol 13 (6) ◽  
pp. 850-876 ◽  
Author(s):  
In Choi ◽  
Joon Y. Park ◽  
Byungchul Yu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Asymptotic Theory ◽  
Finite Sample ◽  
Cointegration Tests ◽  
Simulation Monte Carlo ◽  
Simulation Results ◽  
Canonical Cointegrating Regression

This paper introduces tests for the null of cointegration in the presence of I(1) and I(2) variables. These tests use residuals from Park's (1992, Econometrica 60,119–143) canonical cointegrating regression (CCR) and the leads-and-lags regression of Saikkonen (1991, Econometric Theory 9,1–21) and Stock and Watson (1993, Econometrica 61, 783–820). Asymptotic theory for CCR in the presence of I(1) and I(2) variables is also introduced. The distributions of the cointegration tests are nonstandard, and hence their percentiles are tabulated by using simulation. Monte Carlo simulation results to study the finite sample performance of the CCR estimates and the cointegration tests are also reported.

scholarly journals Monte Carlo Simulation Of The Portfolio-Balance Model Of Exchange Rates: Finite Sample Properties Of The GMM Estimator

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Hong-Ghi Min
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Exchange Rates ◽  
Balance Model ◽  
Finite Sample ◽  
Gmm Estimator ◽  
Portfolio Balance ◽  
Finite Sample Properties ◽  
Chi Squared ◽  
Better Than

Using Monte Carlo simulation of the Portfolio-balance model of the exchange rates, we report finite sample properties of the GMM estimator for testing over-identifying restrictions in the simultaneous equations model. F-form of Sargans statistic performs better than its chi-squared form while Hansens GMM statistic has the smallest bias.

scholarly journals New Estimator for AR (1) Model with Missing Observations

2021 ◽  
Vol 23 (09) ◽  
pp. 147-159
Author(s):  
Mohamed Khalifa Ahmed Issa ◽  
Keyword(s):  
Time Series ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Least Squares ◽  
Unit Root ◽  
Constant Term ◽  
Ordinary Least Squares ◽  
Missing Observations ◽  
New Form ◽  
Near Unit Root

In this paper, new form of the parameters of AR(1) with constant term with missing observations has been derived by using Ordinary Least Squares (OLS) method, Also, the properties of OLS estimator are discussed, moreover, an extension of Youssef [18]has been suggested for AR(1) with constant with missing observations. A comparative study between (OLS), Yule-Walker (YW) and modification of the ordinary least squares (MOLS) is considered in the case of stationary and near unit root time series, using Monte Carlo simulation.

Finite Sample Performance of Likelihood Ratio Tests for Cointegrating Ranks in Vector Autoregressions

1995 ◽  
Vol 11 (5) ◽  
pp. 1015-1032 ◽  
Author(s):  
Hiro Y. Toda
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Likelihood Ratio ◽  
Test Performance ◽  
Likelihood Ratio Tests ◽  
Nuisance Parameters ◽  
Test Statistics ◽  
Finite Sample ◽  
Vector Autoregressions ◽  
Finite Sample Properties

This paper investigates through Monte Carlo simulation the finite sample properties of likelihood ratio tests for cointegrating ranks that were proposed by Johansen (1991, Econometrica 59, 1551–1580). We transform the model into a canonical form so that the experiment is well controlled without loss of generality and then conduct a comprehensive simulation study. As expected, the test performance is very sensitive to the value of the stationary root(s) of the process. We also find that the test performance depends crucially on the correlation between the innovations that drive the stationary and the nonstationary components of the process. We conclude that 100 observations are not sufficient to ensure reasonably good performance uniformly over the values of the nuisance parameters that affect the distributions of the test statistics.

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