Econometric Analysis and Forecasts on Exports of Emerging Economies from Central and Eastern Europe

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper has clear practical significance in exploring the convergence and export-oriented growth model of the European economy after transformation. The research design is relatively systematic, combining descriptive statistics, inferential statistics, time series forecasting (ARIMA), and Granger causality test, and the methods are widely used. The paper has a complete structure and clear data sources, providing valuable empirical evidence on the long-term trends and country differences of export dynamics in Central and Eastern European countries. The paper covers the period from 1995 to 2024, which includes the key stages of the EU's eastward expansion, the financial crisis, the COVID-19 epidemic and the Ukraine crisis, enhancing the timeliness and policy reference value of the research. The following comments are for reference only.

 

1. Some research questions (such as RQ4) are slightly vague in expression, but can be more specific.
2. In the Granger causality test results, no significant causal relationship was found in some countries (such as Hungary and Poland), and the author only briefly attributed it to "lagged effects or non export-oriented investment". Further explanation can be attempted by combining the industrial structure or foreign investment characteristics of that country;
3. The selection basis of ARIMA model (such as AIC/SC) has little difference in some countries, so it can be added to explain why a certain model is selected instead of others.
4. The style of Figure 1 is inconsistent with the subsequent charts. It is recommended to unify the chart format and annotation method.
5. Some table numbers are incorrect (such as "Table 7" on page 20 and subsequent "Table 10", etc.), and the entire text needs to be checked and numbered uniformly.
6. At the end of the paper, it is mentioned that the use of aggregated data (as a percentage of GDP) failed to capture intra industry differences. This point can be further elaborated in the discussion and policy recommendations to illustrate its potential impact on the conclusions.
7. Consider briefly discussing the potential impact of external shocks such as geopolitical conflicts and energy crises on the forecast results after 2022 based on ARIMA predictions.

 

Author Response

We would like to thank you for the thorough and constructive feedback, which has greatly contributed to improving the quality and clarity of the manuscript. In the following lines, we will address your comments one by one, presenting the changes that we have introduced in the revised version of our manuscript. The revised text is marked with red in the manuscript.

Comments and Suggestions for Authors

This paper has clear practical significance in exploring the convergence and export-oriented growth model of the European economy after transformation. The research design is relatively systematic, combining descriptive statistics, inferential statistics, time series forecasting (ARIMA), and Granger causality test, and the methods are widely used. The paper has a complete structure and clear data sources, providing valuable empirical evidence on the long-term trends and country differences of export dynamics in Central and Eastern European countries. The paper covers the period from 1995 to 2024, which includes the key stages of the EU's eastward expansion, the financial crisis, the COVID-19 epidemic and the Ukraine crisis, enhancing the timeliness and policy reference value of the research.

Thank you for your valuable feedback

The following comments are for reference only.

1. Some research questions (such as RQ4) are slightly vague in expression, but can be more specific.

Thank you for this helpful comment. Following your suggestion, we have rephrased RQ4 to make it more specific and closely aligned with the empirical findings. The revised research question now explicitly addresses the links between trade openness, export dependence and volatility, as well as the mitigating role of domestic market size in buffering external shocks.

The updated RQ4 reads as follows (lines 135-137):

“To what extent does a higher degree of trade openness increase export dependence and volatility, and how does domestic market size mitigate exposure to external shocks in Central and Eastern European countries?”

This rephrasing clarifies the analytical focus of the question and ensures a clearer connection between the research question, the empirical results and the conclusions.

1. In the Granger causality test results, no significant causal relationship was found in some countries (such as Hungary and Poland), and the author only briefly attributed it to "lagged effects or non export-oriented investment". Further explanation can be attempted by combining the industrial structure or foreign investment characteristics of that country;

We appreciate this insightful comment. The absence of a statistically significant Granger causal relationship between FDI and exports (as a share of GDP) in countries such as Hungary and Poland may indeed reflect structural and compositional factors rather than a lack of economic linkage per se.

 We have inserted the following text in the section 4.2 Granger causality (lines 840-850)

In particular, a substantial share of FDI in these economies has been directed toward domestic market-oriented activities (e.g., services, retail, finance) or toward manufacturing segments integrated into global value chains where export decisions are driven by multinational production networks rather than host-country export performance. In such cases, exports may respond to global demand conditions and firm-level strategies with longer or heterogeneous adjustment lags that are not fully captured by standard Granger causality tests. Moreover, the relatively diversified industrial structure and larger domestic market, especially in the case of Poland, may weaken the short-run statistical linkage between FDI inflows and aggregate exports expressed as a percentage of GDP, as domestic demand absorbs a significant share of output. As a result, FDI may contribute more to productivity, technology transfer, or import substitution than to immediate export expansion.

1. The selection basis of ARIMA model (such as AIC/SC) has little difference in some countries, so it can be added to explain why a certain model is selected instead of others.

We thank the reviewer for this valuable remark.

We have inserted the following text at the beginning of section 4.3, ARIMA models, second paragraph (869-880)

For some countries the differences among the information criteria (AIC and SC) of the candidate ARIMA models are very small, indicating a similar level of in-sample fit. In such cases, the selection of the optimal model was not based exclusively on the minimization of AIC/SC, but rather placed greater emphasis on forecasting performance, as evaluated by error measures such as RMSE and MAPE. Given that one of the main objectives of the study is to generate short-term export forecasts, forecast accuracy was considered more relevant than marginal differences in information criteria. The selected models satisfy standard diagnostic requirements (stationarity, absence of residual autocorrelation), and their AIC/SC values are very close to the minimum, suggesting that there is no meaningful trade-off between parsimony and predictive ability. Therefore, model selection was guided by a balanced consideration of information criteria and out-of-sample predictive performance, with particular emphasis on the forecasting objective of the analysis.

1. The style of Figure 1 is inconsistent with the subsequent charts. It is recommended to unify the chart format and annotation method.

We have changed the style of Figure 1, using the 0-100 scale.

1. Some table numbers are incorrect (such as "Table 7" on page 20 and subsequent "Table 10", etc.), and the entire text needs to be checked and numbered uniformly.

We have checked the entire text and ensured all the tables are numbered and referenced correctly

1. At the end of the paper, it is mentioned that the use of aggregated data (as a percentage of GDP) failed to capture intra industry differences. This point can be further elaborated in the discussion and policy recommendations to illustrate its potential impact on the conclusions.

We thank the reviewer for this valuable suggestion. We have inserted this text at the end of paper, lines 1339-1355

A limitation of the present study is the use of aggregated export data expressed as a percentage of GDP, which does not capture intra-industry and sectoral heterogeneity. While this measure is suitable for cross-country comparability and long-term macroeconomic analysis, it may obscure substantial differences in export intensity, volatility, and shock sensitivity across industries.

In particular, export volatility at the aggregate level may be driven by a limited number of highly cyclical sectors, such as manufacturing industries deeply integrated into global value chains, while other sectors exhibit more stable export patterns. Similarly, the absence of Granger causality between FDI and exports in some countries may reflect the concentration of foreign investment in services or domestic-market-oriented activities, whose export effects are not fully captured in aggregate measures.

Consequently, the findings should be interpreted as reflecting overall export dynamics rather than sector-specific export behavior. From a policy perspective, this suggests that export promotion and FDI attraction strategies should be increasingly tailored to sectoral characteristics, with a focus on diversification, upgrading toward higher value-added activities, and reducing excessive dependence on a narrow set of export-oriented industries. Future research could extend the present analysis by employing disaggregated, sector-level data to better capture these intra-industry dynamics.

1. Consider briefly discussing the potential impact of external shocks such as geopolitical conflicts and energy crises on the forecast results after 2022 based on ARIMA predictions.

We thank the reviewer for this valuable suggestion. We have added a brief discussion highlighting that the ARIMA-based forecasts after 2022 should be interpreted as baseline projections, as the models do not explicitly account for major external shocks such as geopolitical conflicts and the energy crisis. The revised text emphasizes the potential for structural breaks and differential country exposure, and clarifies the conditional nature of the post-2022 forecast results.  We have inserted the following text at the end of paper, lines 1356-1369

The ARIMA-based forecasts for the post-2022 period should be interpreted with caution, as they are generated under the assumption that historical patterns and relationships persist over time. While the models capture long-run trends and cyclical dynamics in export performance, they do not explicitly account for major external shocks or structural breaks.

In this context, recent geopolitical tensions, particularly the war in Ukraine, as well as the energy crisis affecting European economies, may alter trade flows, production costs, and export competitiveness in ways that deviate from historical dynamics. Highly open economies with strong energy import dependence may experience greater volatility than projected by the baseline ARIMA forecasts, whereas countries with larger domestic markets may exhibit a stronger buffering effect.

Therefore, the forecast results should be viewed as baseline scenarios rather than unconditional predictions. Future research could incorporate structural break tests, regime-switching models, or multivariate forecasting frameworks to explicitly capture the impact of external shocks on export dynamics.

Reviewer 2 Report

Comments and Suggestions for Authors

I am attaching my review report in pdf file format.

Comments for author File: Comments.pdf

Author Response

We would like to thank you for the thorough and constructive feedback, which has greatly contributed to improving the quality and clarity of the manuscript. In the following lines, we will address your comments one by one, presenting the changes that we have introduced in the revised version of our manuscript. The revised text is marked with red in the manuscript

General Information:

This paper examines export performance in seven Central and Eastern European emerging economies (Croatia, Czech Republic, Hungary, Poland, Romania, Bulgaria, and Slovakia) over 1995-2024 using World Bank annual data, combining comparative descriptive/inferential statistics with ARIMA time-series modelling to generate short term export forecasts (2025-2027) and Granger causality tests to assess the dynamic links between exports and foreign direct investment (FDI) in the context of EU integration, trade openness, and regional economic convergence, while highlighting cross-country differences in export dependence, volatility, and structural drivers.

Major Comments:

The manuscript is likely to be accepted provided the authors fully and carefully address all the issues raised in my review:

1. Why do you model exports as % of GDP (rather than export volume/value), and how might this scaling affect cross-country comparability and the ARIMA forecasts?

We thank the reviewer for this important question. We inserted the following text in introduction (112-126)

Exports are modeled as a percentage of GDP in order to enhance cross-country comparability and to capture the degree of export dependence and trade openness rather than absolute export size. Given the substantial differences in economic scale among the countries analyzed, using export values or volumes would largely reflect country size effects, potentially obscuring structural differences in export orientation and integration into international markets.

Expressing exports relative to GDP also reduces the influence of inflation, exchange rate fluctuations, and price-level differences, thereby providing more stable time series for ARIMA modeling over a long historical period. This normalization is particularly relevant in the context of EU integration and convergence analysis, where export intensity is a key indicator of economic openness and external exposure.

We acknowledge that modeling exports as a share of GDP may combine movements in both exports and output and may mask sectoral heterogeneity. However, this measure is well suited to the study’s objective of analyzing aggregate export dynamics and generating short-term forecasts of export dependence. Accordingly, the ARIMA forecasts should be interpreted as projections of export intensity rather than absolute export growth.

2. Including this study-while clearly stating how it departs from prior work, will strengthen the literature review and make its unique contribution stand out more clearly:
   • 55. Ul-Durar, S., K. A. Dimitriadis, N. Arshed, M. De Sisto, and H. Harati. 2025. “Distributional and Tail-Dependent Perspec-tives in Economic Relationships: A Review of Quantile Regression Application.” Journal of Economic Surveys. https://doi.org/10.1111/joes.70057

We thank the reviewer for this valuable suggestion. In response, we have incorporated the study by Ul-Durar et al. (2025) into the literature review and explicitly clarified how our approach departs from and complements their work (346-365 and 1580-1582 in the reference list)

Recent advances in econometric methodology have emphasized the importance of distributional heterogeneity and tail-dependent relationships in economic analysis. In this context, Ul-Durar et al. (2025) provide a comprehensive survey of quantile regression applications, highlighting how economic relationships may differ across the conditional distribution of the dependent variable and how tail behavior can reveal asymmetric or non-linear effects that are obscured by mean-based approaches. Their framework is particularly relevant for analyzing inequality, risk transmission, and heterogeneous responses to economic shocks.

While such distribution-sensitive techniques offer valuable insights, the present study departs from this strand of literature by focusing on the temporal dynamics and aggregate evolution of exports rather than on conditional distributional effects. Specifically, this paper employs ARIMA models to capture persistence, trend behavior, and short-term predictability in export intensity, complemented by Granger causality tests to examine the dynamic interaction between foreign direct investment and exports. This time-series–oriented approach is well suited to the study’s objective of generating policy-relevant export forecasts and identifying medium-term transmission mechanisms in post-transition economies. Consequently, our contribution is complementary to quantile-based analyses: instead of modeling heterogeneity across the export distribution, we emphasize cross-country comparability, dynamic adjustment, and forecasting performance in the context of European integration and structural transformation.

3. Please elaborate on what the results imply and tighten the conclusion, so it is more succinct. Also, rewrite the abstract as one well-structured paragraph that clearly states the study’s aim, main findings, and primary contribution.

We thank the reviewer for this constructive suggestion. In response, we have expanded the discussion of the results’ implications by explicitly linking the empirical findings to economic policy, export resilience, and forecasting reliability in Central and Eastern European economies (lines 1193-1215). These additions clarify how the identified export dynamics, volatility patterns, and FDI–export causal relationships inform trade, investment, and industrial policy decisions.

The empirical results yield several important implications for economic policy and applied trade analysis in Central and Eastern Europe. First, the identification of persistent upward trends in export intensity across all countries confirms the long-term effectiveness of EU integration and trade liberalization as drivers of export-led growth. However, the marked heterogeneity in export dependence and volatility suggests that highly open economies—such as Slovakia, Hungary, and the Czech Republic—remain more exposed to external demand shocks, particularly in cyclical manufacturing sectors. This finding underscores the importance of export diversification and value-chain upgrading as mechanisms for enhancing resilience.

Second, the Granger causality results reveal that foreign direct investment contributes to export growth only in a subset of countries and often with significant time lags. This implies that FDI inflows alone are insufficient to generate immediate export expansion; instead, complementary factors such as absorptive capacity, infrastructure quality, and institutional effectiveness are critical for translating investment into export competitiveness. From a policy perspective, this highlights the need for targeted industrial and innovation policies that facilitate technology diffusion and firm-level integration into global value chains.

Finally, the robustness of the ARIMA-based forecasts indicates that, despite recent shocks—including the global financial crisis, the COVID-19 pandemic, and geopolitical disruptions—export dynamics in CEE economies exhibit a degree of structural stability. This supports the use of classical time-series models as reliable tools for short-term export forecasting and policy planning, particularly when the objective is to project aggregate export dependence rather than sector-specific performance

Regarding the conclusion, we consider that given the multi-country comparative scope, the integration of forecasting and causality analysis, and the paper’s emphasis on both empirical and policy contributions, a reduction in length would risk omitting essential interpretation and weakening the synthesis of results. We therefore respectfully decided to keep the conclusion section in its current form.

Regarding the abstract, we thank the reviewer for this helpful suggestion. In response, we have fully rewritten the abstract as a single, well-structured paragraph that clearly articulates the study’s aim, data and methodology, main empirical findings, and primary contribution (lines 14-35). The revised abstract improves clarity and coherence, strengthens the emphasis on the paper’s comparative and forecasting focus, and more explicitly highlights its contribution to the literature on export dynamics and econometric forecasting in Central and Eastern European economies.

This study examines the evolution, heterogeneity, and short-term prospects of export performance in seven Central and Eastern European (CEE) economies—Croatia, Czech Republic, Hungary, Poland, Romania, Bulgaria, and Slovakia—over the period 1995–2024. Using annual World Bank data, exports are modeled as a share of GDP to ensure cross-country comparability and to capture differences in trade dependence. The analysis combines descriptive and inferential statistics with Augmented Dickey–Fuller tests, non-parametric comparisons, Granger causality analysis, and country-specific ARIMA models to investigate export dynamics, the role of foreign direct investment (FDI), and future export trajectories. The results reveal a common long-term upward trend in export intensity across all countries, driven by European integration and structural transformation, but with pronounced cross-country differences in export dependence and volatility. Highly open economies such as Slovakia, Hungary, and the Czech Republic exhibit strong export performance alongside greater exposure to external shocks, while larger domestic markets such as Poland and Romania display lower export intensity and greater stabilization. Granger causality tests indicate that FDI contributes to export growth in several economies, often with multi-year lags, highlighting the importance of absorptive capacity and institutional quality in translating investment inflows into export competitiveness. ARIMA-based forecasts for 2025–2027 suggest continued export expansion and relative stabilization despite recent global disruptions. The study’s primary contribution lies in integrating comparative export analysis, causality testing, and short-term forecasting within a unified econometric framework, offering policy-relevant insights into export-led growth and economic convergence in post-transition European economies.

4. How were the ARIMA (p, d, q) orders selected for each country (e.g., AIC/BIC, ACF/PACF), and do the residual diagnostics (e.g., ADF stationarity after differencing, autocorrelation/ARCH checks) convincingly support forecast reliability for 2025-2027?

We thank the reviewer for raising this important methodological point. The ARIMA orders (p, d, q) for each country were selected using a combination of statistical criteria and time series diagnostics. Specifically:

Identification of differencing (d):

• We first examined the stationarity of the series using the Augmented Dickey-Fuller (ADF) test.
• Non-stationary series were differenced as necessary until stationarity was achieved, ensuring that the ARIMA models met the fundamental assumption of weak stationarity.

Identification of autoregressive and moving average orders (p and q):

• We used both the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) to suggest candidate AR and MA terms.
• For some countries the differences among the information criteria (AIC and SC) of the candidate ARIMA models are very small, indicating a similar level of in-sample fit. In such cases, the selection of the optimal model was not based exclusively on the minimization of AIC/SC, but rather placed greater emphasis on forecasting performance, as evaluated by error measures such as RMSE and MAPE. Given that one of the main objectives of the study is to generate short-term export forecasts, forecast accuracy was considered more relevant than marginal differences in information criteria. The selected models satisfy standard diagnostic requirements (stationarity, absence of residual autocorrelation), and their AIC/SC values are very close to the minimum, suggesting that there is no meaningful trade-off between parsimony and predictive ability. Therefore, model selection was guided by a balanced consideration of information criteria and out-of-sample predictive performance, with particular emphasis on the forecasting objective of the analysis.

We have inserted the previous text at the beginning of section 4.3, second paragraph (lines 869-880)

Model validation and residual diagnostics:

• Residuals of the selected ARIMA models were examined for autocorrelation using  Durbin-Watson and Breusch-Godfrey tests, confirming that serial correlation was not present.
• ARCH tests were conducted to assess conditional heteroscedasticity, ensuring that volatility clustering did not bias the forecasts.

Forecast reliability for 2025–2027:

• The combination of model selection criteria, stationarity verification, and comprehensive residual diagnostics provides strong support for the reliability of the short-term forecasts.
• Although ARIMA models rely on historical patterns and cannot fully anticipate unprecedented structural shocks (e.g., geopolitical events, energy crises), they are well-suited for projecting baseline trends in export performance, and the diagnostics indicate that the model assumptions are satisfactorily met for all countries analyzed.

In summary, the selection of ARIMA(p,d,q) models was guided by standard best practices, with careful attention to statistical criteria, stationarity and residual properties, thereby providing confidence in the robustness and reliability of the 2025–2027 export forecasts.

5. Given the Granger causality tests between FDI and exports, how do the authors justify the chosen lag structure and address the risk that Granger ‘causality’ reflects omitted variables (EU accession timing, global shocks) rather than a true causal mechanism?

We thank the reviewer for this important observation. The lag structure in the Granger causality tests was selected based on standard information criteria (AIC and SC) and robustness checks, ensuring that residual autocorrelation was minimized and that the chosen lags capture the short-term dynamic interactions between FDI and exports (Appendix A, Table A1-A14).

We acknowledge that Granger causality indicates predictive precedence rather than a strict causal mechanism, and that omitted variables such as EU accession timing or global shocks may influence the observed relationships. To address this, results are interpreted cautiously and in the context of country-specific economic structures and industrial composition. Even in the absence of strict causality, the tests provide valuable insights into the temporal linkage between FDI inflows and export dynamics, highlighting potential patterns of economic dependence and responsiveness that are relevant for policy analysis.

 

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The revised manuscript has been revised according to the comments and the overall quality has been improved. It is recommended to publish it.