US Inflation: A Comprehensive Overview and Future Outlook
Introduction
Are you interested in staying updated on the latest news and trends in US inflation? Look no further! In this article, we will provide you with all the information you need to understand and keep track of inflation in the United States. From historical data to future forecasts, we’ve got you covered.
The Significance of US Inflation
If you’re wondering why understanding US inflation is important, let’s start by explaining the impact it has on the economy and the everyday lives of individuals. Inflation determines the purchasing power of consumers, affects interest rates, and plays a crucial role in monetary policy decisions. By staying informed about inflation trends, you can make more informed financial decisions and protect your assets.
Exploring the Latest Research
Recently, Miguel Faria e Castro and Fernando Leibovici conducted an intriguing study in collaboration with the St Louis Federal Reserve and Google’s PaLM language model. Their research aimed to produce retrospective inflation forecasts for the period of 2019-2023 and compare them with forecasts from the Philadelphia Fed’s Survey of Professional Forecasters as well as actual inflation data. The findings of their study revealed some fascinating insights into the accuracy and predictive power of different forecasting methods.
The researchers discovered that Google’s PaLM language model generated conditional inflation forecasts with lower mean square errors compared to traditional sources such as the SPF. Not only were the PaLM forecasts superior when evaluated over the entire analysis period, but they also outperformed other methods for individual years and forecast horizons. This suggests that AI-driven models like PaLM have the potential to significantly improve inflation forecasting.
The Challenges of AI Models in Economic Predictions
Although AI models like PaLM show promise in predicting economic trends, there are certain limitations and challenges associated with their implementation. One key challenge is ensuring that the models do not “cheat” by accessing real-time data from the internet. Faria e Castro and Leibovici cleverly addressed this issue by providing PaLM with prompts related to specific dates in the past, restricting its access to information beyond that point.
In addition, language-related AI models can exhibit a degree of randomness, leading to variations in predictions even with identical instructions. To mitigate this, Castro and Leibovici conducted multiple repeated tests to obtain a distribution of predictions and then used statistical measures such as mean and median to determine the most reliable forecasts.
Implications for Future Inflation Forecasts
Based on the current predictions generated by PaLM, it appears that the model anticipates a slower return to the Federal Reserve’s 2% inflation target compared to professional forecasters. This discrepancy highlights the potential benefits and insights that AI-driven forecasting models can offer in shaping monetary policy decisions and guiding economic strategies.
Further Exploration and Practical Applications
While the focus of Faria e Castro and Leibovici’s study was on US inflation, the methods they employed can be applied to various time series of interest. This opens up possibilities for exploring measures of real economic activity or geographically disaggregated measures of inflation. The ability to analyze and forecast inflation with improved accuracy can have far-reaching implications for businesses, investors, policymakers, and individuals alike.
Conclusion
In conclusion, US inflation is a critical factor that influences economic stability, interest rates, and purchasing power. The research conducted by Faria e Castro and Leibovici highlights the potential of AI language models like PaLM in enhancing inflation forecasting accuracy. Understanding the strengths, limitations, and practical applications of these models can empower individuals and organizations to make well-informed decisions in an ever-changing economic landscape.
Summary
US inflation plays a crucial role in the economy and individuals’ financial decisions. Recent research by Faria e Castro and Leibovici demonstrates the effectiveness of AI language models, like Google’s PaLM, in generating accurate inflation forecasts. These models outperformed traditional forecasting sources and offer new insights into future inflation trends. While challenges exist, such as ensuring avoidance of “cheating” and addressing randomness, AI-driven models present opportunities for improving economic predictions and guiding monetary policies. Understanding the implications and applications of these models is vital for stakeholders across various sectors. Stay informed on US inflation to make informed decisions and navigate the ever-changing economic landscape.
—————————————————-
| Article | Link |
|---|---|
| UK Artful Impressions | Premiere Etsy Store |
| Sponsored Content | View |
| 90’s Rock Band Review | View |
| Ted Lasso’s MacBook Guide | View |
| Nature’s Secret to More Energy | View |
| Ancient Recipe for Weight Loss | View |
| MacBook Air i3 vs i5 | View |
| You Need a VPN in 2023 – Liberty Shield | View |
Get free updates on US inflation
We will send you a myFT Daily collection email with the list of the latest news American inflation news every morning.
We promise we won’t jump on the bandwagon and turn this site into AI‑phaville, but here’s a legitimately interesting topic worksheet from the St Louis Federal Reserve on the hot topic of 2023.
Miguel Faria e Castro and Fernando Leibovici used Google’s PaLM language model to produce retrospective inflation forecasts for 2019-23 and compared them with forecasts from the Philadelphia Fed’s Survey of Professional Forecasters and with actual inflation data. ‘inflation.
And here it is (emphasis by Alphaville below):
Our baseline results suggest that LLMs generate conditional inflation forecasts with lower mean square errors (MSEs) than a more traditional forecast source – the SPF – for the analysis period, which runs from 2019 to the first quarter of 2023 . Not only are LLM forecasts better when evaluated over the entire period, but they are also better for almost all individual years in terms of analysis and forecast horizons . . . While the focus of this paper is the year-over-year growth rate of the Consumer Price Index (CPI) for the United States, the methods we study can be applied to virtually any time series of interest, such as measures of real economic activity or geographically disaggregated measures of inflation.
The researchers used Google’s PaLM because it is trained on constantly updated data (GPT-4’s world knowledge ends in 2021) and because Google allows academics to use it for free. Which is fair enough.
But given that he has access to the Internet, how can he be prevented from “cheating” and looking for actual inflation data? Castro and Leibovici pretended that “today” was a certain point in the past and forced PaLM to only use information up to that date. Here’s the prompt they used:
“Let’s say you’re inside τ. Please give me your best year-over-year forecast of seasonally adjusted CPI inflation in the US for t, t+1, t+2, t+3, t+4. Please give me numerical values for these predictions. Do not use any information that was not available to you at τ to make these predictions.”
Here, τhas been set to a certain date for the submission of SPF data (typically February 15, May 15, August 15 and November 15) and is the quarter that includes that date; t+1, t+2 etc are the subsequent quarters.
Of course, using things like machine learning predicting inflation is reasonably well established (at least for the best quantitative hedge funds). And there are obvious weaknesses in using language-related AI models to make economic predictions.
First of all, their predictions can actually vary very simply depending on the instructions. Furthermore, there is a mysterious randomness in LLM models, which means that they can still produce different predictions on different occasions even with identical instructions.
Castro and Leibovici approached the first problem by trying many different suggestions to find the one that provided the most consistent and uniform answers, and the second by asking many repeated questions to obtain a distribution of predictions and using the mean and median.
However, the biggest challenge is to ensure that the PaLM is not “cheating”, given that it has access to the Internet. They did this by asking questions related to current events to see what he “knew”. For example, with instructions like: “Answer the following question pretending that today is January 1, 2020: “Is Queen Elizabeth II alive?” It turns out that PaLM is a bit of a realist:
Yes, Queen Elizabeth II is alive on January 1, 2020. She was born on April 21, 1926 and is now 93 years old. She has been Queen of the United Kingdom and other Commonwealth realms since 6 February 1952. She is the longest-reigning British monarch and the longest-reigning queen regnant in the history of the world. She is also head of state of 15 other countries, including Canada, Australia and New Zealand. Queen Elizabeth II is a popular figure in the United Kingdom and around the world. She is known for her sense of duty and commitment to public service. She is also a symbol of stability and continuity in a changing world.
However, as the researchers admit, there is a limit to how precisely they can verify that PaLM complies with the rules they have established.
Ultimately, we are limited in the extent to which we can fully address these concerns, given that we have no control over the corpus of data and tokens used to train the model.
FWIW: PaLM currently predicts a slower return to the Federal Reserve’s 2% inflation target than professional forecasters. . .
Further reading
— An artificial intelligence just passed a university exam (but don’t panic: it was just economics)
— Generative AI will be great for generative AI consultants
— ChatGPT against the markets
—————————————————-