You're analyzing economic indicators with conflicting data. How do you decide which ones to prioritize?

The first step in deciding on the use of specific economic indicators is to remove the "noise" from the data series. Descriptive statistics, curve fitting, and forecasting are important segments of time series analysis with many applications in economics and other disciplines. Time series data have four components: trend, seasonal, cyclical, and random components. Research on economic volatility uses a statistical filtering (e.g., HP filter) approach to isolate the cyclical and trend components of changes in the series. Moreover, the moving average is a mathematical formula used to find trends and smooth out the data series by filtering out noise from random fluctuations.

When I first encountered conflicting economic data in my analyses, I felt overwhelmed. To tackle this, I started by focusing on indicators most relevant to my project's goals. For example, if I was assessing consumer behavior, I prioritized consumer confidence and retail sales data over industrial production. I also emphasized data from credible sources like central bank reports. Gradually, by applying economic theories and occasionally consulting with seasoned economists, I developed a more structured approach to prioritize and interpret these indicators effectively.

When analyzing conflicting economic indicators, it's important to focus on the most relevant ones for the current situation. Key indicators like GDP growth, inflation, and unemployment are often the most telling because they directly affect the economy. It's also useful to look at specific areas like consumer confidence and business investments to get a detailed picture. By balancing these with global economic changes and policy updates, you can get a better understanding of the overall economic health. The goal is to consider both short term changes and long-term trends to make well informed decisions.

When analyzing a given economy, be aware of the fact that data is noisy. Economists should look at indicators with less volatility, over longer periods, distinguish real from nominal data, and combine various perspectives. In the case of conflicting data, I recommend the following: a) Extract the signal from the noise: Variation in headline economic data can be dominated by noisy components that tell a different story from what the component with more predictive value suggests. b) Combine multiple perspectives: Looking at a single indicator in a vacuum might not provide an accurate diagnosis, especially over a short period of time. Interpretation of such an indicator can be enhanced by other indicators that might paint a different picture.

I often used whatever information was easiest to find, not necessarily the most current. I soon learned that this approach could lead to misguided conclusions, especially in a fast-changing economy. I shifted my strategy to prioritize the latest data available. This adjustment proved invaluable during a period of rapid policy changes, as it allowed me to provide timely, relevant advice. Now, I always ensure that the economic indicators I use are as up-to-date as possible to accurately reflect the current economic conditions.

I once based an entire economic forecast on data that, it turned out, came from a less reputable source. The indicators were skewed, leading to some embarrassing projections. This experience taught me a crucial lesson about the importance of data quality. Now, I meticulously assess the source of every piece of data I use, favoring information from government agencies and reputable financial institutions. This careful scrutiny ensures that my analyses are based on reliable information, significantly enhancing the accuracy of my economic assessments.

While analyzing economic data, both quality and timeliness are important. However, if one has to prioritize, it should always be quality over timeliness. A good economic analysis should strike the right balance between the two.

I would always prioritise accuracy and decide whether the indicator is the best indicator that fits the country's context. For e.g., when do we use GDP and GNP? GNP is the value of all products and services produced by the citizens of a country both domestically, and internationally minus income earned by foreign residents. For small and open economies like SG, GNP might arguably be more reflective. Sometimes there might be edge cases, or further inspection is needed. For e.g., Acemoglu and Robinson predicted western liberal democracies can grow well economically. What about China? Probably there is a need to unpack further details to see if the way of governance is affecting growth in any way (for e.g. regulation affecting innovation?)

In my experience, contextual awareness is vital in assessing economic indicators. A single indicator's positivity in a generally struggling economy could be deceptive, just as a negative indicator in a thriving economy might not be as alarming. It's crucial to analyze indicators in the context of the overall economic situation to ensure a balanced understanding.

The starting point is to recognize you may tend to selectively search for information that supports your existing beliefs or hypotheses. Being aware of this can help you avoid falling into the trap of confirmation bias. Actively seeking out information that challenges your initial hypothesis will allow you to gain a more comprehensive understanding and prevent you from becoming too focused on confirming your preconceived ideas. Remaining open-minded and willing to revise your beliefs based on new evidence, as well as establishing clear and objective criteria for evaluating data, is crucial. Finally, seeking input and feedback from others is essential for obtaining a well-rounded understanding of the issue.

I would like to highlight the importance of adaptability in today's dynamic economic environment. The ability to quickly adjust to changes, whether technological advancements, market shifts, or global events, is crucial for success. Embracing a mindset of continuous learning and flexibility can not only help professionals thrive in unpredictable circumstances but also seize opportunities that arise from them.