Search arXivSearch

arXiv · 1206.0450

Why price inflation in developed countries is systematically underestimated

Abstract

There is an extensive historical dataset on real GDP per capita prepared by Angus Maddison. This dataset covers the period since 1870 with continuous annual estimates in developed countries. All time series for individual economies have a clear structural break between 1940 and 1950. The behavior before 1940 and after 1950 can be accurately (R2 from 0.7 to 0.99) approximated by linear time trends. The corresponding slopes of regressions lines before and after the break differ by a factor of 4 (Switzerland) to 19 (Spain). We have extrapolated the early trends into the second interval and obtained much lower estimates of real GDP per capita in 2011: from 2.4 (Switzerland) to 5.0 (Japan) times smaller than the current levels. When the current linear trends are extrapolated into the past, they intercept the zero line between 1908 (Switzerland) and 1944 (Japan). There is likely an internal conflict between the estimating procedures before 1940 and after 1950. A reasonable explanation of the discrepancy is that the GDP deflator in developed countries has been highly underestimated since 1950. In the USA, the GDP deflator is underestimated by a factor of 1.4. This is exactly the ratio of the interest rate controlled by the Federal Reserve and the rate of inflation. Hence, the Federal Reserve actually retains its interest rate at the level of true price inflation when corrected for the bias in the GDP deflator.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ivan Kitov. 2012-06-03. Why price inflation in developed countries is systematically underestimated. https://arxiv.org/abs/1206.0450

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Prediction Markets Beat the Weather Forecast on Tomorrow's High Temperature

The sooner we receive information, and the more accurate it is, the better planning decisions we can make. Every day, prediction markets let anyone bet on tomorrow's high temperature in cities around the world, creating a market-implied forecast built on dispersed information. We use the past five years of market data from the Kalshi exchange for seven American cities to extract, hour by hour, the market-implied forecast. We use this forecast as a measuring instrument to see how much information about the temperature the market makes public before the public forecasting system does. We race it against the leading American and European weather forecasts. In six of the seven cities we study, the market beats the most accurate single public forecast, the National Blend of Models (NBM). Aggregating every city-day, at the end of the market's first hour of trading it beats the best single public product by about 10 percent in root-mean-square error, and holds its lead through the day, overnight, and into the target day. Looking at how the forecasts move over time, we find the National Blend travels four times further toward the market between its postings than the market travels toward the NBM. The market does not react to new weather forecast updates; instead, the forecast slowly publishes information that the market had already shared publicly.

q-fin.GN

Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?

Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient boosted trees, random forest, and neural network yields a value-weighted annualized Sharpe ratio of 2.08 for classification and 1.39 for regression. This outperformance strengthens with class granularity and persists across subsamples and after transaction costs. Spanning tests show that classification retains economically large alphas after we control for regression, whereas regression alphas shrink substantially once we control for classification. These results indicate that classification extracts more return information than matched regression. Our diagnostics trace classification's advantage to more precise separation of return deciles.

q-fin.GN

AI for AI: Optimizing Additional Infrastructure Build-out to Power Artificial Intelligence Data Centers

The twenty-first century's transformative technology, artificial intelligence, is increasingly constrained by the twentieth century's transformative technology, the electricity grid. Rapid growth in electricity demand from data centers is leading to higher electricity prices, without a compensating supply-side response. We develop a framework linking data-center load growth, available generation capacity, and market-clearing prices to understand this phenomenon. We first analyze a deterministic model to show how differing estimates of demand and supply growth rates affect prices. We then model the expansion of new data centers and their associated electricity demand, together with build-outs of new electricity supply, as stochastic processes,resulting in probabilistic distributions of supply, demand, and prices rather than a single forecast. Finally, we formulate generation expansion as a stochastic control problem in which a revenue-maximizing investor dynamically chooses the intensity of supply-side investments. The analysis highlights a central challenge of the data-center build-out: even when rapid demand growth increases the need for new generation, the uncertainties related to load forecasts, development execution risks, and value cannibalization from overbuilding capacity may weaken incentives to invest at the pace required to keep electricity prices stable.

q-fin.GN