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I am looking at a time series which has no obvious trend, but seems to have an intercept a little above zero. The results I get for ur.df function in R is the following:

logprice_df <- ur.df(test3, lags = 1, type= 'trend')
summary(logprice_df)

############################################### 
# Augmented Dickey-Fuller Test Unit Root Test # 
############################################### 

Test regression trend 


Call:
lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.50614 -0.04394  0.00134  0.03859  0.64408 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.841e-02  8.268e-03   2.226  0.02626 *  
z.lag.1     -1.573e-02  5.635e-03  -2.791  0.00537 ** 
tt           9.234e-06  1.080e-05   0.855  0.39272    
z.diff.lag   1.411e-01  3.364e-02   4.195 3.01e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 0.07512 on 865 degrees of freedom
Multiple R-squared: 0.02651,    Adjusted R-squared: 0.02314 
F-statistic: 7.852 on 3 and 865 DF,  p-value: 3.572e-05 


Value of test-statistic is: -2.791 2.6012 3.8997 

Critical values for test statistics: 
      1pct  5pct 10pct
tau3 -3.96 -3.41 -3.12
phi2  6.09  4.68  4.03
phi3  8.27  6.25  5.34

The problem is that I do not understand how one shall interpret these values. What is F-statistic? And what is the probabilities below Pr(>|t|) in the first table, and also Value of test-statistic?

I really appreciate any help.

COOLSerdash
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1 Answers1

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Maybe you can already find some answers to your questions regarding Pr(>|t|) and the F-statistic here in this answer: Multiple linear regression for hypothesis testing

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Maria
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