Board of Governors of the Federal Reserve System

Abstract:

Most statistical agencies construct sectoral real GDP using double deflation and base period prices. When the base period price used for intermediate inputs is not equal to their marginal revenue product, such as when firms apply a markup, real GDP fluctuations become mechanically linked to variations in intermediate inputs. This is because these inputs generate profits that are incorporated into real value added. Taking this channel into account, we demonstrate that real GDP reported in national accounts substantially diverges from a theory-consistent "physical" value added. This, in turn, has implications for the measurement of productivity. Between 1999 and 2021, "physical" productivity cumulative growth in the Finance sector was 15pp lower compared to the Solow Residual, while it was 15pp higher in the Manufacturing sector.

Keywords: Economic Measurement, National Accounts, Markups, Productivity

DOI: https://doi.org/10.17016/IFDP.2023.1375

Abstract:

We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm rely mostly on expert judgment to determine crises, often with a lag. Consequently, crisis durations and the buildup phases of vulnerabilities are usually determined only with the benefit of hindsight. Although we can identify and forecast a portion of crises worldwide to various degrees with traditional econometric techniques and using readily available market data, we find that textual data helps in reducing false positives and false negatives in out-of-sample testing of such models, especially when the crises are considered more severe. Building a framework that is consistent across countries and in real time can benefit policymakers around the world, especially when international coordination is required across different government policies.

Keywords: Financial Crises, Machine Learning, Natural Language Processing

DOI: https://doi.org/10.17016/IFDP.2023.1374

Abstract:

We uncover the major drivers of macro aggregates and the real exchange rate at business cycle frequencies in Group of Seven countries. The estimated main drivers of key macro variables resemble each other and account for a modest fraction of the real exchange rate variances. Dominant drivers of the real exchange rate are orthogonal to main drivers of business cycles, generate a significant deviation of the uncovered interest parity condition, and lead to small movements in net exports. We use these facts to evaluate international business cycle models accounting for the dynamics of both macro aggregates and the real exchange rate.

Keywords: international business cycles, real exchange rate, uncovered interest parity

DOI: https://doi.org/10.17016/IFDP.2023.1373

Abstract:

Motivated by cognitive theories verifying that investors have limited capacity to process information, we study the effects of information overload on stock market dynamics. We construct an information overload index using textual analysis tools on daily data from The New York Times since 1885. We structure our empirical analysis around a discrete-time learning model, which links information overload with asset prices and trading volume when investors are attention constrained. We find that our index is associated with lower trading volume and predicts higher market returns for up to 18 months, even after controlling for standard predictors and other news-based measures. Information overload also affects the cross-section of stock returns: Investors require higher risk premia to hold small, high beta, high volatile, and unprofitable stocks. Such findings are consistent with theories emphasizing that information overload increases information and estimation risk and deteriorates investors' decision accuracy amid their limited attention.

Keywords: Limited attention, dispersion, sentiment, predicting returns, behavioral biases

DOI: https://doi.org/10.17016/IFDP.2023.1372

Abstract:

We provide evidence for a causal link between the US economy and the global financial cycle. Using intraday data, we show that US macroeconomic news releases have large and significant effects on global risky asset prices. Stock price indexes of 27 countries, the VIX, and commodity prices all jump instantaneously upon news releases. The responses of stock indexes co-move across countries and are large - often comparable in size to the response of the S&P 500. Further, US macroeconomic news explains on average 23 percent of the quarterly variation in foreign stock markets. The joint behavior of stock prices, bond yields, and risk premia suggests that systematic US monetary policy reactions to news do not drive the estimated effects. Instead, the evidence points to a direct effect on investor’ risk-taking capacity. Our findings show that a byproduct of the United States' central position in the global financial system is that news about its business cycle has large effects on global financial conditions.

Keywords: Global Financial Cycle, High-frequency event study, International spillovers, Macroeconomic announcements, Monetary policy, Stock returns, VIX

DOI: https://doi.org/10.17016/IFDP.2023.1371

Abstract:

We assess the quantitative relevance of expectations-driven sovereign debt crises, focusing on the Southern European crisis of the early 2010’s and the Argentine default of 2001. The source of multiplicity is the one in Calvo (1988). Key for multiplicity is an output process featuring long periods of either high growth or stagnation that we estimate using data for those countries. We find that expectations-driven debt crises are quantitatively relevant but state dependent, as they only occur during stagnations. Expectations are a major driver explaining default rates and credit spread differences between Spain and Argentina.

Keywords: Self-fulfilling debt crises, sovereign default, multiplicity, stagnations.

DOI: https://doi.org/10.17016/IFDP.2023.1370

Abstract:

The COVID-19 pandemic has led to an unprecedented shift of consumption from services to goods. We study this demand reallocation in a multi-sector model featuring sticky prices, input-output linkages, and labor reallocation costs. Reallocation costs hamper the increase in the supply of goods, causing inflationary pressures. These pressures are amplified by the fact that goods prices are more flexible than services prices. We estimate the model allowing for demand reallocation, sectoral productivity, and aggregate labor supply shocks. The demand reallocation shock explains a large portion of the rise in U.S. inflation in the aftermath of the pandemic.

Keywords: Sectoral Reallocation, Inflation, Input-Output Models, Moment-matching exercise

DOI: https://doi.org/10.17016/IFDP.2023.1369

Abstract:

We review the "climate action plans" of Global Systemically Important Banks (GSIBs) and the progress they are making toward achieving them. G-SIBs have identified the drivers of climate risk and their transmission channels to credit and other risks. Additionally, some have started to measure and model these risks. While most GSIBs have committed to fully offsetting their emissions by mid-century, they are only beginning to measure financed emissions resulting from their loans and investments, which comprise the vast majority of their emissions. G-SIBs have also committed to increase green finance and have started to do so. All told, despite some progress by large global banks to address climate change considerations, much work lies ahead to properly measure and disclose climate-related risks, and to better align financing activities with their net-zero targets.

Keywords: Climate change, banks, climate finance, environmental reporting

DOI: https://doi.org/10.17016/IFDP.2023.1368