www.gusucode.com > econ 案例源码程序 matlab代码 > econ/AssessWhetherASeriesIsTrendStationaryAndARpExample.m

    %% Assess Whether a Series Is Trend Stationary and AR(p)
% Test the growth of the U.S. unemployment rate using the data in Schwert,
% 1987.
%%
% Load Schwert's macroeconomic data set.

% Copyright 2015 The MathWorks, Inc.

load Data_SchwertMacro
%%
% Focus on the unemployment rate growth over the dates condsidered in
% Leybourne and McCabe, 1999.
UN = DataTableMth.UN;
t1 = find(datesMth == datenum([1948 01 01]));
t2 = find(datesMth == datenum([1985 12 01]));
dUN = diff(UN(t1:t2)); % Unemployment rate growth
%%
% Assess the null hypothesis that the unemployment rate growth is a trend
% stationary, AR(1) process using the estimated variance from OLS 
% regression.
[h1,~,stat1,cValue] = lmctest(dUN,'lags',1,'test','var1')
%%
% The warning indicates that the pvalue is below 0.1. |h1| = 0 indicates
% that there is not enough evidence to reject that the unemployment rate 
% growth is a trend stationary, AR(1) process.
%%
% Assess the null hypothesis that the unemployment rate growth is a trend
% stationary, AR(1) process using the estimated variance from the maximum
% liklihood of the reduced-form regression model.
[h2,~,stat2,cValue] = lmctest(dUN,'lags',1,'test','var2')
%%
% |h2| = 1 indicates that the there is enough evidence to asuggest that the
% unemployment rate growth is nonstationary.
%% 
% Leybourne and McCabe, 1999 report that the original LMC statistic fails 
% to reject stationarity, while the modified LMC statistic does reject it.