A colleague proposed the following solution, which I have not yet validated. It is possible that this information will be of assistance to you in any case.

1. Damodar N. Gujarati and Dawn C. Porter (2009). "Basic Econometrics" (5th edition), page 639

The key point is as follows: Gujarati suggests that the impact of non-stationarity is diminished in panel data with large N and small T, as the model captures more cross-sectional variation, which could render stationarity tests less critical.

2. Baltagi, Badi H. (2008). In "Econometric Analysis of Panel Data" (4th edition), pages 218-219, Baltagi states that in panels with large N and small T, the primary concerns often lie in the cross-sectional dependence rather than time-series stationarity.

3. Hsiao, Cheng (2003). "Analysis of Panel Data" (2nd Edition), p. 231.

The author's discussion supports the view that stationarity tests may be less crucial in large N, small T panels, as the model may be more robust to non-stationary time series properties.

Despite having more cross-sectional units than time periods, it's still advisable to perform stationarity tests to ensure that your panel data model provides reliable and valid results.

In panel data econometrics, the need to perform stationarity tests depends on the characteristics of the dataset and the model being estimated. When the cross-sectional dimension (number of countries in your case) is much larger than the time dimension (number of years), there is a line of reasoning that suggests the stationarity tests might be less critical, particularly if the focus is on the within-dimension (i.e., variations within countries over time).

However, a thorough justification would require careful consideration of the context and model used. While some econometricians argue that when the cross-sectional units dominate the time periods, the panel may behave more like a cross-sectional dataset where non-stationarity is less of a concern, this is not a universally accepted rule, and best practices often still recommend checking for stationarity to ensure the validity of the results.

Unfortunately, specific econometrics books like "Basic Econometrics" by Damodar N. Gujarati or other widely-used texts typically emphasize the importance of stationarity for time series data, and this importance often extends to panel data.

Reference to Professor Gujarati's Book:

In Gujarati's "Basic Econometrics" (5th edition, 2009), while discussing panel data models, the emphasis is often on ensuring that the underlying assumptions of the regression model are met. While the text does not explicitly state that stationarity tests can be ignored in cases where the cross-sectional units dominate, it does acknowledge the complexity of working with panel data and the importance of correct model specification.

To locate relevant content in Gujarati's book, you might refer to the section on panel data models:

Book: Basic Econometrics (5th Edition)

Author: Damodar N. Gujarati and Dawn C. Porter

Section: Chapter 16: Panel Data Regression Models

Pages: 620-622

Justification Based on Literature:

If you're looking for an academic justification, you could look at the following articles which discuss when stationarity might be less of a concern:

1) Baltagi, B. H. (2005). "Econometric Analysis of Panel Data." This text discusses the different approaches to panel data, including fixed effects, random effects, and dynamic models. While it doesn’t say stationarity can be ignored outright, it explains different scenarios where the model's focus shifts.

2) Pesaran, M. H. (2007). "A Simple Panel Unit Root Test in the Presence of Cross-Section Dependence." Journal of Applied Econometrics. This paper addresses unit root tests for panel data and acknowledges that in some cases, depending on the structure of the data and the model used, the need for stationarity testing may vary.

Suggested Phrase:

Considering the econometric properties discussed in Baltagi (2005) and Pesaran (2007), when the cross-sectional dimension (number of countries) substantially exceeds the time dimension (number of years), the panel data set may exhibit characteristics similar to cross-sectional data. Consequently, the necessity of performing stationarity tests might be reduced, particularly in the context of within-group estimations.

This phrase could be useful to justify your approach, but remember that it’s often recommended to still check for stationarity or to explicitly state the assumptions and limitations in your analysis.

The use of standard panel data analysis implies that the coefficients on your explanatory variables are constant across countries. If, for example, inflation is an explanatory variable its coefficient will be the same for all 186 countries. Perhaps you can, but I would be very doubtful.

Chapter 12 of the 6th edition of Baltagi (2021), Econometric Analysis of Panel Data, Springer, gives an account of the analysis of large N and T macro panels. Also relevant are

Your first consideration should be the nature of your data and the model you wish to estimate. Do your theory, confirmed by unit root tests, imply that your data are non-stationary? Is there cointegration? These are just two of the many questions that you should answer. The use of macro panel analysis is not easy and is more involved than set out in introductory econometric texts.

How do you intend to take account of cross-section correlation?

It is not sensible to think about testing for stationarity before knowing what one wants to analyse and before a careful specifiction of the model to estimate. I do not understan why one should need a stationarity test when one uases your data for, say, 20 of the 186 countries, but no test, if one does the estimates with the same time series for mor than 100 countries.

Skipping the stationarity test, simply because the number of countries exceeds the number of years, ignores the critical time dimension within the 3R Xi framework (space, time, and force). Time in this context represents the changing economic conditions over the 24-year period. By neglecting this, we undermine the logical treatment of temporal evolution and force (the external pressures such as economic policies, crises, etc.) that act on these countries.

This becomes especially problematic when considering the current political money system, which is not grounded in any tangible force, unlike under the gold standard. Since the removal of the gold standard, money has become an illogical construct—detached from the physical realities of the space and force dimensions in 3R Xi. The fiat money system allows for unlimited money creation, contributing to unstable, arbitrary forces within the economy. These illogical forces ripple through economic models and society, destabilizing both.

Econometric models, when they fail to rigorously test time-based assumptions like stationarity, further perpetuate this instability. They allow ungrounded financial policies to go unchecked, leading to unsustainable growth that contributes to societal destruction and ecological degradation. The disconnected money system drives this by promoting constant consumption and growth without respect to the finite space (resources) and time (long-term consequences) we live in. Without grounding in 3R's logical structure, these models can exacerbate the very problems they attempt to analyze.

whoops - distance-time-force. Space is a distance-distance-distance 3D simplification to 3R which is fine at a Newtonian or sensory neural network level of modelling.

In panel data econometrics, the need for stationarity tests is often debated when the cross-sectional units (countries in your case) significantly outnumber the time periods. According to Professor Damodar Gujarati's book, "Basic Econometrics" (5th edition), while time series data requires stationarity tests to avoid spurious results, panel data models, especially with large cross-sectional units and fewer time periods, may not always necessitate such tests. On page 637, he highlights that "panel data models are less prone to non-stationarity issues due to the presence of multiple cross-sectional units." Additionally, articles by Baltagi and others emphasize that with a large N (cross-sections) relative to T (time periods), panel models benefit from greater variation, reducing the risk of non-stationarity. However, it’s always advisable to consult specific econometric tests suited for panel data, like the Levin-Lin-Chu (LLC) test, if needed for robustness.

Before using a panal analysis, I would try time series estimates for several (maybe 5-15) different, assumedly almost uncorrelated countries. This would quickly show whether necessary conditions hold, for example if the coefficients of the explaining variables are the same for all countries (mentioned by John C. Frain), or at least sufficiently similar. It is also a first easy test for the specification of the model (equation).

Sharif Ahamed Please check your quotation from page 637 of Gujarati's Basic Econometrics (5th edition)? On page 607 of my copy, I read

As noted at the outset, the topic of panel data modeling is vast and complex. We have barely scratched the surface. The following are among the many topics we have not discussed.

1. Hypothesis testing with panel data.

2. Heteroscedasticity and autocorrelation in ECM.

3. Unbalanced panel data.

4. Dynamic panel data models in which the lagged value(s) of the regressand appears as an explanatory variable.

5. Simultaneous equations involving panel data.

6. Qualitative dependent variables and panel data.

7. Unit roots in panel data (on unit roots, see Chapter 21).

One or more of these topics can be found in the references cited in this chapter, and the reader is urged to consult them to learn more about this topic. These references also cite several empirical studies in various areas of business and economics that have used panel data regression models. The beginner is well-advised to read some of these applications to get a feel for how researchers have actually implemented such models.

Gujarati did not intend this book to be a foundation for the implementation of panel data models. You need to learn a lot more. In my earlier comment, I gave some references on the analysis of large N and T macro panels. Before you do your econometrics you need to have some sensible economic theory/model that you can test and estimate.

I am quoting from the international edition of the book which I presume is the same as the American version.