No Country For Old Unit Root Tests: Bridge Estimators Differentiate between Nonstationary versus Stationary Models and Select Optimal Lag

نویسندگان

  • Mehmet Caner
  • Keith Knight
چکیده

This paper introduces a novel way of differentiating a unit root from a stationary alternative. We write up the model consisting of ”zero” and ”nonzero” parameters. If the lagged dependent variable has a coefficient of zero, we know that the variable has a unit root. We exploit this property and treat this as a model selection problem. We show that Bridge estimators can select the correct model. They estimate ”zero” parameter on the lagged dependent variable as zero (nonstationarity), if this is nonzero (stationary), estimate the coefficient with standard normal limit. In this sense, we extend the statistics literature as well, since that literature only deals with model selection among only stationary variables. The reason that our methodology can outperform the existing unit root tests with lag selection methods stems from the two-step nature of existing unit root tests. In our method, we select the optimal lag length and unit root simultaneously. We show that in simulations, this makes big difference in terms of size and power.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

An Alternative to Unit Root Tests: Bridge Estimators Differentiate between Nonstationary versus Stationary Models and Select Optimal Lag

This paper introduces a novel way of differentiating a unit root from stationary alternatives using so-called “Bridge” estimators; this estimation procedure can potentially generate exact zero estimates of parameters. We exploit this property and treat this as a model selection problem. We show that Bridge estimators can select the correct model with probability tending to 1. They estimate ”zer...

متن کامل

Structural Spurious Regressions and A Hausman-Wu-type Cointegration Test∗

Economic models often imply that certain variables are cointegrated. However, tests often fail to reject the null hypothesis of no cointegration for these variables. One possible explanation of these test results is that the error is unit root nonstationary due to a nonstationary measurement error in one variable. For example, currency held by domestic economic agents for legitimate transaction...

متن کامل

A Spurious Regression Approach to Estimating Structural Parameters∗

Economic models often imply that certain variables are cointegrated. However, tests often fail to reject the null hypothesis of no cointegration for these variables. One possible explanation of these test results is that the error is unit root nonstationary due to a nonstationary measurement error in one variable. For example, currency held by the domestic economic agents for legitimate transac...

متن کامل

Bootstrap Prediction Intervals for Autoregressive Models Based on Asymptotically Mean-Unbiased Parameter Estimators

The use of asymptotically mean-unbiased estimation is considered as a means of biascorrection, when bootstrap prediction interval is constructed for autoregressive (AR) models with unknown lag order. Its computational efficiency enables application of the endogenous lag order bootstrap algorithm to prediction intervals. Extensive Monte Carlo experiments are conducted using a number of stationar...

متن کامل

Optimal invariant tests for the autocorrelation coefficient in linear regressions with stationary or nonstationary AR( 1) errors

Inference on the autocorrelation coefficient p of a linear regression model with first-order autoregressive normal disturbances is studied. Both stationary and nonstationary processes are considered. Locally best and point-optimal invariant tests for any given value of p are derived. Special cases of these tests include tests for independence and tests for unit-root hypotheses. The powers of al...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2008