Well, I would start asking myself the question of whether it makes sense that a lag of the dependent variable might have an impact on the current value of that variable different from the one captured by the regressors included (which might include lags of those regressors, too).

Typically, in my field of research (Labour Economics), a typical example when looking at welfare dependence is that the receiving welfare benefits in t-1 has an effect on receiving welfare benefits it t. Also, it seems relevant to control for previous earnings in the case of the effect of training programs on earnings.

If you do not have clear reasons for thinking so, just use a static model.

You can also estimate a dynamic model as a robustness check and look whether the lag is significant. Nevertheless, this sort of procedure (e.g., Arellano-Bond, Blundell-Bond, etc.) means new assumptions on how to instrument some variables.

I hope it helps.

Best,

José-Ignacio

It helps a lot. thanks José-Ignacio Antón

It depends on your story telling.

For example, if the existing literature does imply that the lagged effects do exist, then you can use dynamic panel data model. If there is not enough theoretical support or you cannot tell a good story to persuade the readers that dynamic panel data is necessary but you use that, then maybe the effects are still significant, but you will receive some criticism about data framing, which is inconsistent with economics.

So usually we prefer to use static model rather than dynamic one.

I agree with kaizhao and José. Dynamic models are not systematic and have to be supported by theory. If not you have to try a dynamic model and check the signicance of the lagged dependent variable or use the Box-Jenkings procedure to identify the best specification.

Chiraz Karamti thanks for the suggestion. can you drop me the STATA codes for the Box-Jenkings procedure?

The Box_Jenkings procedure is a process in several steps. You have to begin with 1) the autocorrelogram 2) the unit-root tests 3) try several specifications of ARIMA(p,d,q) and 4) compare them according to several indicators i) the residuals of the appropriate model have to be a white noise so you have to use the Ljung-Box test on the residuals ii) check the parameters significance of the model iii) if the precedent steps are valid for several ARIMA specifications you have to choose the one with the minimal information critirion (BIC or AIC). There are several examples on line with STATA codes but I'll send you soon an example (I don't have my laptop right now).

First of all, the mathematical relationship between the independent variables and dependent variable must be determined correctly in econometric analysis . Today, the relationships between economic variables are largely influenced by past experiences and behaviors. Therefore, the effect of the aforementioned past experiences requires the lag of variables to be included in the model as an explanatory factor. Including these effects also increases the explanatory power of the model. Delayed values can be included in the model as the delayed value of the independent variable or dependent variable. While revealing the dynamic structure of the model, autoregressive models in which the lag of the dependent variable is included as an explanatory variable in the model due to the problems that arise in general and the area of use come to mind.

Therefore, the dynamic structure differs from static models by adding the lagged value of the dependent variable to the model as an explanatory variable. The decision as to whether the model includes a dynamic structure or not is entirely related to strong economic knowledge. After the decision about the dynamic structure is made, this situation becomes definite with the significance of the lagged variables.

Dear Gabriel,

All well said @Jose-Ignacio Anton and @ Tuvce Scar.

It all depends on the basis of your theory. You can use Static model and then dynamic mofel6as a robustness check.

Regards

Hi Gabriel,

here some codes

thank you Chiraz Karamti

well received

You can use the Arellano-Bond test for serial correlation and the Sargan-Hansen test for over-identification. If these tests indicate significant serial correlation or over-identification, respectively, dynamic panel data analysis may be more suitable due to potential time-series dependencies in the data.

To determine whether static or dynamic panel data analysis is appropriate:

1. **Endogeneity Test**: Conduct tests like the Hausman test to check for endogeneity. If significant, dynamic models are preferable.

2. **Time Dependence**: Assess if variables exhibit temporal dynamics or if past values influence current ones. If so, dynamic models are more suitable.

3. **Data Structure**: Consider panel data structure and specific research questions. Dynamic models are apt for studying changes over time, while static models suit cross-sectional analysis.

4. **Consult Literature**: Review relevant literature in your field to see what methods are commonly used for similar research questions and data types.

To determine whether to use static or dynamic panel data analysis:

1. **Assess Time Dynamics**: Check if variables exhibit time-dependent behavior or if past values influence current ones. Dynamic analysis is suitable if there's temporal dependency.

2. **Endogeneity Testing**: Conduct tests like the Hausman test to detect endogeneity. If significant, dynamic models are preferred.

3. **Consider Research Question**: Determine if your research aims to study changes over time (dynamic) or focuses on cross-sectional comparisons (static).

4. **Consult Literature**: Review existing studies in your field to see which approach is commonly used for similar data and research questions.