Results#
The results of this research are presented in a sequential manner, reflecting the multi-stage approach to topic modeling and uncertainty analysis within the context of central bank policy in Cambodia’s highly dollarized economy. The findings are organized into distinct sections, each contributing to a comprehensive understanding of the underlying themes, nuances, and dynamics of uncertainty.
Topic Modeling without Prior#
The initial phase of topic modeling without prior was instrumental in uncovering the hidden thematic structure within a corpus of 39,637 Cambodian news articles. Utilizing the Latent Dirichlet Allocation (LDA) model, 20 distinct topics were identified, representing a broad spectrum of subjects ranging from politics and health to technology and international relations. The coherence scores and word cloud collages provided both quantitative validation and visual insights into the thematic richness of the corpus.
The topics identified were reflective of various societal, cultural, economic, and political dimensions. For instance, Topic 0 was found to focus on political dynamics, including elections and governance, while Topic 1 was centered on the global health crisis, specifically COVID-19. The distribution of topics within documents further enriched the understanding of the corpus’s thematic structure, offering potential applications in content categorization and recommendation systems.
Topic Modeling with Prior#
The second stage of the analysis introduced a topic modeling approach with prior information, specifically tailored to the study of central bank policy uncertainty. By incorporating prior knowledge related to general economic indicators and central banking aspects, the model was aligned with the research objectives, ensuring relevance and robustness.
Three distinct modifications characterized this methodology: the integration of prior knowledge targeting uncertainty-related themes, the strategic removal of specific stop words, and the fine-tuning of model parameters. These deliberate adjustments culminated in a bespoke approach adept at capturing and classifying uncertainty-related themes.
The Second Stage Uncertainty Model#
The second stage of uncertainty topic modeling further refined the analysis, focusing on a more nuanced understanding of uncertainty. This stage introduced a new set of priors and a reduction in the number of topics to five, aiming to capture the core aspects of uncertainty with greater precision.
The topics generated in this stage provided insights into different facets of uncertainty, including the emotional reactions, strategic responses, and economic implications. For example, Topic 0 captured the dynamics of risk and uncertainty in economic recovery, while Topic 1 focused on strategies to mitigate uncertainty.
Quantifying Central Bank Policy Uncertainty#
The research’s culmination was the application of the second stage uncertainty model to quantify Central Bank Policy Uncertainty. The model’s insights into different aspects and stages of uncertainty were particularly focused on topics 0, 1, and 2, encapsulating the complexity of central bank policy uncertainty.
Topic 0 represented the early stage of uncertainty, capturing the initial indicators of risk. Topic 1 signified the reactive measures employed to address unfolding uncertainties, while Topic 2 captured the specific actions of the central bank, such as interest rate cuts or hikes.
These three relevant topics provided a comprehensive view of central bank policy uncertainty, delineating the early stage of uncertainty, the reactive measures, and the central bank’s specific actions. This nuanced understanding enhanced the ability to quantify and analyze central bank policy uncertainty, offering valuable insights for policymakers, economists, and financial analysts.
Topic Trends and Dynamics#
In addition to the multi-stage topic modeling and uncertainty analysis, the research also delved into the analysis of topic trends, focusing on three key areas: Economic, Banking, and ASEAN-related topics. This exploration provided a nuanced understanding of the thematic dynamics over a span from 2014 to 2023.
The Economic topic exhibited a declining trend, possibly reflecting a shift in focus or changes in the global economic landscape. This consistent decrease might be indicative of a reduced emphasis on general economic indicators within the corpus. Conversely, the Banking topic demonstrated an upward trend, with a notable spike in 2020. This suggests an increasing interest in banking and financial services, possibly driven by regulatory changes, technological advancements, or specific economic events. The ASEAN topic’s fluctuation, particularly the significant increase in 2020, may be a reflection of regional developments, cooperation, or challenges within the ASEAN community.
The research further extended to an uncertainty analysis, specifically examining economic activity. By targeting instances where the Economic topic weight was greater than 0.5, the analysis aimed to understand the trends and patterns related to uncertainty within the context of economic themes. The data was meticulously selected, aggregated, and plotted to provide a visual representation of how uncertainty has evolved in conjunction with economic activity.
The trends revealed in the uncertainty plot were indicative of specific patterns and fluctuations that might correspond to various economic events and conditions. Peaks in the uncertainty graph could align with significant economic events, policy changes, or market volatility. The focus on documents with a high Economic topic weight allowed for insights into how uncertainty is linked to specific economic indicators, policies, or sectors. Moreover, the time-series analysis enabled the examination of seasonal patterns, cyclical trends, or responses to one-off events, providing a temporal perspective on the underlying drivers of uncertainty within the economic context.
The uncertainty analysis, in conjunction with the topic trend exploration, enriched the research’s overall findings. It offered a multifaceted view of how uncertainty is expressed and evolves within the realm of economic activity, banking, and regional cooperation. These insights are valuable for various stakeholders, including economists, policymakers, investors, and business leaders, as they navigate the complex landscape of uncertainty in the economic domain.
The results of this additional analysis not only complement the primary findings of the research but also open avenues for further exploration. Future research could delve into correlations with external economic indicators, sentiment analysis, or predictive modeling, thereby providing a more comprehensive and actionable understanding of economic uncertainty. This integration of topic trends and uncertainty analysis underscores the research’s depth and breadth, contributing to a robust and nuanced understanding of the thematic structures and dynamics within the corpus.
Evaluation#
The evaluation of the topic-based measures of policy uncertainty within this research is a multifaceted process that encompasses both a narrative approach and a comparison to alternative, established measures of uncertainty, ensuring a comprehensive assessment of the model’s effectiveness.
The narrative approach is characterized by a manual examination of articles that are associated with the highest uncertainty scores according to the model. This meticulous content analysis aims to discern the primary sources of uncertainty, exploring whether the model’s identification aligns with the direct examination of the articles. For instance, the analysis delves into the nature of uncertainty, whether it pertains to exchange rate policy, currency stabilization policy, de-dollarization policy, or international monetary policy, and further investigates the specific drivers of that uncertainty within these broad categories. This process not only validates the model’s accuracy in identifying the nature of uncertainty but also offers insights into the multifaceted factors contributing to policy uncertainty.
Complementing the narrative approach is the comparison to alternative uncertainty measures, such as the Economic Policy Uncertainty Index and the Policy Uncertainty Index. This comparison serves as a benchmark, providing a context to assess the topic-based uncertainty measures. The evaluation includes an examination of the correlation between the topic-based measures and these alternative indices over time, expecting a reasonably strong positive correlation if the measures are effectively capturing true policy uncertainty. Additionally, the relative magnitudes of the measures are scrutinized, where large divergences might indicate potential issues with the topic-based measures. The cross-examination of the sources of policy uncertainty between the alternative measures and the topic-based model further adds to the validation process, ensuring consistency and alignment.
The article retrieval process, a vital step in conducting the uncertainty analysis, is carefully orchestrated to gather the most relevant and insightful materials. By selecting the topic, date range, and number of articles with precision, the analysis bridges the gap between quantitative topic modeling and qualitative content analysis. This integration leverages the strengths of both methodologies, providing a nuanced and context-specific understanding of uncertainty.
Together, these evaluation methods form a robust framework that not only assesses the effectiveness of the topic-based measures but also enriches the understanding of policy uncertainty. The combination of narrative analysis, comparative assessment, and article retrieval ensures that the model’s measures are not only validated but also contextualized within the broader landscape of policy uncertainty. This comprehensive evaluation contributes to the model’s potential as a valuable tool in understanding and predicting economic outcomes, reflecting the complexity and multifaceted nature of policy uncertainty in the economic landscape.