<p>The reg command always includes a constant.</p>
<p><span class="math-container">$labprod_t =\beta_0 +\beta_1 labprod_{t-1}+\varepsilon_t$</span>.</p>
<p>I know nothing about the paper you are citing, but be aware that OLS is biased when estimating AR(1) processes. You can see some discussion of that <a href="https://stats.stackexchange.com/questions/240383/why-is-ols-estimator-of-ar1-coefficient-biased">here</a>.</p>

CC BY-SA 4.0
2021-07-14T13:27:53.123Z
Thanks for your help! Do you have any idea how to solve this problem?
AVR
CC BY-SA 4.0
2021-07-14T17:27:22.240Z
It'd help if you provided more details. People don't want to sift through a paper to try to figure it out and interpret it in your context. 

If all you are trying to do is estimate the AR(1) process with OLS, yes, this is it. If you are trying to do something more complicated, can you describe it in your question rather than referencing us to a paper?
Michael Gmeiner
CC BY-SA 4.0
2021-07-15T07:23:00.800Z
I just want to know if there is an estimator which is not unbiased for AR(1) processes. For instance, I heard that Arellano-Bond could be an option to get this kind of result.
AVR
CC BY-SA 4.0
2021-07-15T12:23:10.577Z
Arellano-Bond is an IV estimator for fixed effects with a lagged dependent variable. It is an IV estimator. IV estimators are in general biased but consistent.
Michael Gmeiner
CC BY-SA 4.0
2021-07-14T12:36:35.950Z
2021-07-14T12:36:35.950Z
Michael Gmeiner
<p>I am trying to estimate some variables, but I would like to know if what I am doing is right.
I am using some data about labour productivity (in log) and, by using Stata, I filtered (Hodrick-Prescott) the data. Then, I fit a AR(1) process for labour productivity. Therefore, I created a lag variable for labour productivity and then run the following regression:</p>
<p>reg labprod lag(labprod)</p>
<p>Is it okay to write the results of this regression as:
<img src="https://latex.codecogs.com/gif.latex?labprod_%7Bt%7D=coeff*labprod_%7Bt-1%7D&plus;%5Cvarepsilon_%7Bt%7D" title="labprod_{t}=coeff*labprod_{t-1}+\varepsilon_{t}" /> ?</p>
<p>You can find <a href="https://www.nber.org/system/files/working_papers/w8905/w8905.pdf" rel="nofollow noreferrer">here</a> (pag.17) the paper with the results by Gali &amp; Monacelli. Did I correctly replicate the results? Do I need to undertake further steps?</p>
<p>Thank you in advance!</p>

<time-series>
Estimation of variables
AVR
# Estimation of variables

(Asked by: username_0 on 2021-07-13)

I am trying to estimate some variables, but I would like to know if what I am doing is right.
I am using some data about labour productivity (in log) and, by using Stata, I filtered (Hodrick-Prescott) the data. Then, I fit a AR(1) process for labour productivity. Therefore, I created a lag variable for labour productivity and then run the following regression:

reg labprod lag(labprod)

Is it okay to write the results of this regression as:
![](https://latex.codecogs.com/gif.latex?labprod_%7Bt%7D=coeff*labprod_%7Bt-1%7D&plus;%5Cvarepsilon_%7Bt%7D "labprod_{t}=coeff*labprod_{t-1}+\varepsilon_{t}") ?

You can find [here](https://www.nber.org/system/files/working_papers/w8905/w8905.pdf) (pag.17) the paper with the results by Gali & Monacelli. Did I correctly replicate the results? Do I need to undertake further steps?

Thank you in advance!

**Tags:** time-series


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## 1 answers

### Answer by username_1 on 2021-07-14. Score: 1

The reg command always includes a constant.

$labprod\_t =\beta\_0 +\beta\_1 labprod\_{t-1}+\varepsilon\_t$.

I know nothing about the paper you are citing, but be aware that OLS is biased when estimating AR(1) processes. You can see some discussion of that [here](https://stats.stackexchange.com/questions/240383/why-is-ols-estimator-of-ar1-coefficient-biased).

> **Comments:**
> **username_0**: Thanks for your help! Do you have any idea how to solve this problem?  
> **username_1**: It'd help if you provided more details. People don't want to sift through a paper to try to figure it out and interpret it in your context.   If all you are trying to do is estimate the AR(1) process with OLS, yes, this is it. If you are trying to do something more complicated, can you describe it in your question rather than referencing us to a paper?  
> **username_0**: I just want to know if there is an estimator which is not unbiased for AR(1) processes. For instance, I heard that Arellano-Bond could be an option to get this kind of result.  
> **username_1**: Arellano-Bond is an IV estimator for fixed effects with a lagged dependent variable. It is an IV estimator. IV estimators are in general biased but consistent.  

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