Unmasking Economic Mysteries: Using AI to Detect Impact of Stimulus End on Spending.

In the last post of our three-part series at #Porandu, we went deeper into the anomalies detected in the Consumer Price Index All Urban Consumers (CPIAUCSL) and the Advance Real Retail and Food Services Sales (RRSFS) using deep learning and machine learning techniques. By uncovering these anomalies and providing a more detailed explanation, we aim to provide a comprehensive understanding of economic behavior and potential financial crimes.

The main idea of the previous 3 posts was to be able to understand Economic Anomalies. Economic anomalies are deviations from expected economic behavior. To do this I used Deep Learning (AI) models as a statistical analysis tool.

These anomalies can be caused by various factors, including natural disasters, policy changes, technological advancements, and even financial crimes. Identifying these anomalies is crucial for economists, financial analysts, and financial crimes researchers to understand market movements, make informed decisions, and prevent fraudulent activities.

The post showed that certain anomalies occurred in the Advance Real Retail and Food Services Sales (RRSFS). A particularly relevant one is the one identified in June 2022. In June 2022 a slight decrease in RRSFS (0.45) could potentially indicate a slowdown in advance retail and food services sales. The end of money saved from government stimulus? Possibly.

The US government provided various forms of financial assistance and relief programs in response to the COVID-19 pandemic. These included direct payments, expanded unemployment benefits, tax credits, support for businesses, and increased funding for healthcare. Specifically the Pandemic Unemployment Assistance (PUA) program. It was created under the CARES Act in March 2020 to provide temporary unemployment benefits to workers who were not eligible for regular state unemployment benefits.

Although the sending of checks had ended a few months earlier, the amounts saved began to run out (which is why the anomaly appears on this date in the analysis of the previous post, and not before).

"There is no more money" (dixit President of Argentina, 2023)

What causality does this effect have? or is it a coincidence?

In this post I use an AI/machine learning model to conduct an analysis of the Advance Real Retail and Food Services Sales (RRSFS) over the past three years. It begins by creating a data frame with monthly dates and corresponding sales values. Next, it visualizes these data using a line graph to discern trends in sales over time.

Subsequently, it identifies the start date of June 2022 to split the data into two periods: pre and post this date. This segmentation allows for an examination of how sales change before and after a specific point in time.

Finally, it fits a CausalImpact model to assess the causal impact of any event or intervention on RRSFS sales. This analysis aims to understand how external factors may have influenced sales and contribute to economic and financial decision-making.

Empirical Analysis

Based on the analysis conducted using the CausalImpact model, we can draw several conclusions regarding the impact of the event or intervention on Advance Real Retail and Food Services Sales (RRSFS).

1. Actual vs. Prediction: The actual sales observed post-intervention were lower than what was predicted. On average, there was a decrease of 0.34 units in sales, leading to a cumulative reduction of 7.51 units.
2. Prediction Accuracy: Before the intervention, the model predicted an average sales increase of 10 units, with a standard deviation of 3.6 units. This resulted in a cumulative predicted increase of 231 units, with a standard deviation of 78.2 units. The 95% confidence interval for the predicted cumulative increase ranges from 69.7 to 382 units.
3. Effect of the Intervention: The absolute effect of the intervention on sales was a decrease of 11 units on average, with a standard deviation of 3.6 units. This translates to a cumulative reduction of 238 units, with a standard deviation of 78.2 units. The 95% confidence interval for the absolute effect ranges from a decrease of 18 to 3.5 units.
4. Relative Effect: The relative effect of the intervention on sales was calculated to be -103%, indicating a decrease relative to the predicted sales. This suggests that the intervention led to a reduction in sales beyond what was expected by the model. The 95% confidence interval for the relative effect ranges from -109% to -102%.
5. Statistical Significance: The posterior tail-area probability (p-value) is calculated to be 0.004, indicating that there is a statistically significant difference between the actual and predicted sales. Additionally, the posterior probability of a causal effect is 99.6%, implying a high likelihood that the observed decrease in sales is indeed caused by the intervention.

Findings and Conclusions

The specific finding regarding the potential impact of the end of government stimulus programs on the observed decrease in sales is crucial for economic analysis.

Firstly, if the observed decrease in sales coincides with the termination of government stimulus programs, it suggests that these programs were contributing significantly to supporting consumer spending and overall economic activity. The cessation of such programs may have led to a reduction in disposable income and consumer confidence, resulting in decreased spending on retail and food services.

This finding underscores the importance of government intervention in sustaining economic recovery during periods of economic downturn. It highlights the need for policymakers to carefully consider the timing and duration of stimulus measures to ensure a smooth transition to sustainable economic growth.

Additionally, the observed decrease in sales following the end of government stimulus programs may have broader implications for the economy. It could signal a potential slowdown in overall economic recovery, as reduced consumer spending in retail and food services sectors may ripple through other sectors of the economy, affecting employment, investment, and economic output.

Furthermore, the specific timing of the decrease in sales relative to the end of stimulus programs provides valuable insights for policymakers. It can inform future decisions regarding the implementation of targeted economic policies or interventions to support sectors that are particularly vulnerable to fluctuations in consumer spending.

In conclusion, the finding that the end of government stimulus programs may have played a role in the observed decrease in sales highlights the intricate relationship between government policy, consumer behavior, and economic outcomes. It underscores the importance of proactive and targeted policy measures to support economic recovery and ensure sustainable growth.

?Disclaimer: This primary analysis demonstrates how AI can be applied to economic issues, particularly in assessing the impact of the end of government stimulus programs on retail and food services sales. However, it's essential to recognize that this analysis is based on statistical modeling and inference techniques. While the findings offer valuable insights, they should be interpreted within the context of the analysis's limitations and assumptions. Economic dynamics are complex, and other factors beyond the scope of this analysis may influence the outcomes. Therefore, further research and validation are advised to confirm these findings and provide a more nuanced understanding of the relationship between government stimulus policies and economic outcomes.

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