Blog by Lizi Gogoladze, Economics Student at ISET & European University Viadrina
Have you ever been impressed by the stream of numbers you often see in the news? Countless indicators, predictive forecasts and scenarios, ranging from dire to super positive can be overwhelming to comprehend sometimes, making it tempting to scroll past them.
However, contrary to popular belief, these numbers are not conjured up by economists peering into the magic crystal ball. They are a result of sophisticated forecasting methods that shape economic policy, guide investment decisions, and impact the daily lives of every one of us.
Understanding GDP: The Foundation of Economy
Gross Domestic Product is the total monetary value of all goods and services produced within a country over a specified period. It’s an all-encompassing aggregate measure that summarizes a country’s economic state, influences monetary and fiscal policy decisions, investment and operating strategies, and the quality of people’s lives. To put it simply, it’s the backbone of the overall economy. So, by forecasting the GDP policymakers learn what the future holds.
Utilizing quantitative modeling methods, historical data and professional judgment, analysts aim to predict and forecast future economic conditions. Some of the indicators that field experts base their predictions on are the consumer price index (CPI), expenditure, unemployment rates, interest rates, industrial production, share prices, business investment and many more.
However, the forecasting process isn’t as straightforward as loading the data into a software program and watching it work its magic. If not correctly interpreted, none of the indicators mentioned above will be very useful. Analysts have to run extensive lines of code across time to identify trends, cycles, and seasonal patterns, all while considering economic theories. Only then can they create a structural regression model that measures relationships between numerous variables and generates the much-desired GDP forecast.
Evaluating Forecasts: How Accurate Are They?
It’s not uncommon to question the validity of such forecasts - whether you’re someone with no economic background or an experienced field professional. The complex and uncertain foundation involved in economic forecasting can make even the most meticulously prepared projections fail to meet the target due to unpredictable shocks that the Eastern European and Central Asian region has already grown accustomed to.
Although forecasts are crucial to providing decision-makers with relevant information, they become fallible when certain shocks occur in the economy, such as political disruptions, natural disasters, and, obviously, global pandemics.
Nonetheless, predictive forecasting is still an irreplaceable tool for economists who constantly strive towards improving the accuracy of their models and predictions.
The Future of Economic Forecasting
Did you really think we would wrap up this blog post without mentioning big data, AI and machine learning? On the topic of refining models, those three buzzwords above have revolutionized the field as they not only emerge exactly at the line where traditional forecasting comes short, but also provide unparalleled and accurate insights.
ML algorithms are powerful at processing and analyzing large datasets. These structural mechanisms are adept at identifying substantial time-series patterns and relationships within the data that previous econometric models, which depend on predetermined assumptions about variable connections, might overlook.
However, it should also be noted that machine learning algorithms are not perfect and are continuously refined. Although they are an invaluable addition to macroeconomic forecasting models, there is still a long way to go in enhancing their completeness, accuracy, and reliability.
As we move forward, leveraging these advanced, in-depth predictive algorithms will continue to shape a clearer future of economic forecasting, enabling economists and policymakers to navigate the complexities of the global economy with greater precision and confidence.
So, How Efficient Is Machine Learning Really?
A recent working paper, Adaptive Trees: a New Approach to Economic Forecasting, published by the Economics Department of the OECD in 2020 explored Adaptive Trees, a new machine learning algorithm based on regression trees that addresses non-linearities and structural change in macroeconomic data, which are key issues that often lead to inaccurate forecasts.
The performance of the algorithm in forecasting GDP growth 3- to 12-months ahead is assessed through simulations in pseudo-real-time for six major economies (USA, UK, Germany, France, Japan, Italy). Notably, after relevant assessment, the Adaptive Trees algorithm performed better than both traditional forecasting methodologies, AR(1) model, as well as other machine learning models, Random Forest and Gradient Boosted Trees, in this context.
“The flexibility of machine learning and richness of non-linear and time-varying processes that can be modeled make it potentially well-suited to capture complex economic relationships.”
All in all, machine learning algorithms can handle high dimensional data and have the potential to improve predictive forecasting performance with larger datasets featuring non-linearities and other complexities that traditional models may be limited in addressing.
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