Modeling Inflation, Public Debt, and Exchange Rate Dynamics in Small Open Economies: A Systematic Review and Bibliometric Synthesis
This paper presents a systematic review and bibliometric synthesis of 120 studies on inflation, public debt, and exchange rate dynamics in small open economies, utilizing the novel Theory–Diagnostics–Method–Transparency (TDMT) framework to evaluate model selection, reporting transparency, and citation patterns while identifying ARDL/NARDL and ECM/VECM models as prime candidates for future meta-analytic pooling.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the complex world of economics, small open economies face a unique and persistent challenge. These are nations deeply connected to global trade, where the value of their currency, the price of goods in local shops, and the amount of money the government owes are not separate issues. Instead, they form a tightly linked trio. When the local currency loses value, imported goods become more expensive, and debts owed in foreign currencies grow heavier. When the government spends more than it earns, it often has to borrow or print money, which can push prices up and weaken the currency further. This cycle creates a constant balancing act for policymakers. If they try to fix one problem without considering the others, they risk making the whole situation worse. For decades, economists have tried to build mathematical models to understand how these three forces interact, hoping to find the right tools to predict crises and guide decisions. However, with so many different methods available, it has been difficult to see which tools work best for which specific situations, or whether the research itself is being reported clearly enough to be trusted.
A team of researchers set out to map this entire landscape of economic research, focusing specifically on small open economies like Ghana and its neighbors in Sub-Saharan Africa. They did not simply read a few papers to see what experts think; they conducted a massive, systematic review of 120 studies published between 1991 and 2026. To do this, they used computer programs to search through thousands of records, applying strict rules to select only the most relevant and accessible research. Their goal was to understand not just what these studies found, but how they were built. They examined which mathematical techniques were used most often, where the research came from, and whether the authors provided enough detail for others to verify their work. By organizing the literature into a clear structure, they aimed to create a guide that could help future researchers and central bank analysts choose the right method for their specific questions.
The researchers found that the field is dominated by a few specific types of models. The most common approach, used in nearly one-fifth of the studies, relies on a technique called autoregressive distributed lag, or ARDL. This method is popular because it works well even when the data is short or unstable, a common problem in developing economies. It is often followed by models that look at how errors in predictions correct themselves over time, and by complex simulations that try to mimic the entire economy from the ground up. Interestingly, the researchers discovered a significant gap between the theoretical tools available and the ones actually being used. While advanced models that can account for sudden changes in economic rules or policies exist, they are rarely applied to the specific, volatile histories of countries like Ghana. Instead, researchers often stick to simpler, older methods that assume the economy behaves the same way today as it did twenty years ago, even though these nations have undergone massive shifts in their financial systems.
Transparency was another major finding. The team developed a checklist to see how clearly authors reported their work, looking for details like the exact years of data used, the specific sources of their numbers, and whether they tested their results for errors. They found that reporting varied wildly depending on the method used. Studies using the popular ARDL method were relatively clear, often stating their data sources and timeframes. However, studies using the most complex, simulation-based models were the least transparent. In many of these cases, the authors did not specify which years of data they used or where they got their numbers, making it nearly impossible for other scientists to repeat the study or check the results. This lack of clarity suggests that while these advanced models are powerful, they are currently difficult to use for building a shared, reliable body of evidence.
The review also uncovered a bias in how the research is noticed and cited. Older, foundational papers that established the basic methods are cited far more often than newer studies that apply those methods to specific, real-world problems in Africa. This creates a situation where the most visible research is often the most theoretical, while the most relevant, policy-focused work gets less attention. The authors argue that this visibility gap is not a sign that the newer work is bad, but rather a reflection of how academic attention tends to stick to established names and methods. They suggest that this makes it harder for policymakers to find the most up-to-date, locally relevant evidence when they need it.
Ultimately, the researchers concluded that the field is ready for a new phase of synthesis, but only if the reporting standards improve. They identified that the studies using the ARDL and error-correction methods are the most numerous and the most transparent, making them the best candidates for a future effort to combine their results into a single, powerful analysis. They propose a new decision framework to help analysts choose the right tool: if the data is short and mixed, use the simpler lag methods; if the economy has changed its rules recently, use models that can detect those shifts; and if the goal is to understand volatility, use models designed for risk. By matching the method to the specific nature of the data and the policy question, rather than just following tradition, the researchers believe the field can move forward. Their work serves as a map, showing where the research has been, where the gaps are, and how to build a more reliable foundation for understanding the economic challenges facing small, open nations.
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