Logan Kelly, Ph.D.

Accurate inflation forecasting is crucial for effective monetary policy, particularly during turning points that demand policy realignment. This study examines the efficacy of dedicating ensembles of shallow recurrent neural network models to different forecasting horizons for predicting U.S. inflation turning points more precisely than traditional methods, including the Survey of Professional Forecasters (SPF). We employ monthly data from January 1970 to May 2024, training these ensemble models on information through December 2022 and testing on out-of-sample observations from January 2023 to May 2024. The models generate forecasts at horizons of up to 16 months (one ensemble per horizon), accounting for both short- and medium-term dynamics. The results indicate that such ensembles of recurrent neural networks consistently outperform conventional approaches using key performance metrics, notably detecting inflation turning points earlier and projecting a return to target levels by May 2024—several months ahead of the Survey of Professional Forecasters' average forecast. These findings underscore the value of such ensembles in capturing complex nonlinear relationships within macroeconomic data, offering a more robust alternative to standard econometric methods. By delivering timely and accurate forecasts, dedicated ensembles of shallow recurrent neural networks hold great promise for informing proactive policy measures and guiding decisions under uncertain economic conditions.

Background: The COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most "at a glance" comparisons may be misleading because the curve that should explain the evolution of COVID-19 is different across countries, as a result of the underlying geopolitical or socio-economic characteristics. Therefore, this paper contributes to the scientific endeavour by creating a new evaluation framework to help stakeholders adequately monitor and assess the evolution of COVID-19 in countries, considering the occurrence of spikes, "secondary waves" and structural breaks in the time series. Methods: Generalized Additive Models were used to model cumulative and daily curves for confirmed cases and deaths. The Root Relative Squared Error and the Percentage Deviance Explained measured how well the models fit the data. A local min-max function was used to identify all local maxima in the fitted values. The pure Markov-Switching and the family of Markov-Switching GARCH models were used to identify structural breaks in the COVID-19 time series. Finally, a quadrants system to identify countries that are more/less efficient in the short/long term in controlling the spread of the virus and the number of deaths was developed. Such methods were applied in the time series of 189 countries, collected from the Centre for Systems Science and Engineering at Johns Hopkins University. Results: Our methodology proves more effective in explaining the evolution of COVID-19 than growth functions worldwide, in addition to standardizing the entire estimation process in a single type of function. Besides, it highlights several inflection points and regime-switching moments, as a consequence of people's diminished commitment to fighting the pandemic. Although Europe is the most developed continent in the world, it is home to most countries with an upward trend and considered inefficient, for confirmed cases and deaths. Conclusions: The new outcomes presented in this research will allow key stakeholders to check whether or not public policies and interventions in the fight against COVID-19 are having an effect, easily identifying examples of best practices and promote such policies more widely around the world.

The recent experience during the debt and banking crises in the European Monetary Union (EMU) has demonstrated how important it is to consider liquidity (or rather the lack thereof) in macroeconomics. Similar to the Fed's policy during the US real estate crisis, the ECB took huge efforts to insert liquidity into the banking sector to prevent further financial turmoil, only to find that the transmission mechanism was severely hampered. Strong heterogeneity during the crises accentuated the difficulties of a common monetary policy. The main contribution of this paper is to show that properly measured liquidity contains substantial information on macroeconomic dynamics. Liquidity overcomes two problems of using interest rates (and interest rate spreads) as the main indicator of the monetary and financial side of the economy. First, contrary to the policy rate, they include information on the different impacts of monetary shocks between countries, thereby accounting for heterogeneity in the transmission mechanism and the different states of the banking sector. Second, (growth rates of) liquidity indicators are not subject to the zero lower bound problem and are thus particularly useful when considering samples, such as the recent crisis. We propose a range of liquidity indicators, based on Theil-Törnqvist index number, that are designed to account for measurement problems during times of financial turmoil, when liquidity preference – and thus the price of liquidity – can change quickly. We then study the information content of those variables.

Deteriorating economic conditions in late 2008 led the Federal Reserve to lower the target federal funds rate to near zero, inject liquidity through novel facilities, and engage in large-scale asset purchases. The combination of conventional and unconventional policy measures prevents using the effective federal funds rate to assess the effects of monetary policy beyond 2008. We employ a broad monetary aggregate to elicit the effects of monetary policy shocks both before and after 2008. Our estimates align well with major changes in the Fed's asset purchase programs and yield responses that are free from price, output, and liquidity puzzles that plague other approaches.

We extend the scope of monetary aggregation beyond capital certain assets that make up central bank data sets and identify groups of assets that form monetary aggregates composed of both capital certain and risky, capital uncertain, assets. We construct monetary aggregates for the US and UK using a superlative index and relax a key assumption of the Consumption Capital Asset Pricing Model (CCAPM), a one year planning horizon, by using forecasted returns on risky assets. Our new risky monetary aggregates perform well in VAR tests. We recommended exploring risky assets as providers of liquidity services in future research on this topic.

Starting as early as the 1950s, regulatory and technological innovations have played a co-causal role in the measurement and control of air pollution. "Technology-forcing" regulations, particularly early regulation in California, pushed the automobile industry to develop technology to mitigate carbon dioxide emissions, but as technology to measure carbon dioxide emissions was developed, more and better regulation was adopted. While the role of regulation in the development of new technology remains a topic of continued political debate, our analysis strongly supports the proposition that regulatory innovation played a significant role in the curtailment of carbon dioxide emissions since 1960.

In this study, we use 2010-15 daily data stock market from the Nairobi Securities Exchange (NSE) to investigate herding behavior among NSE market participants. We examine the impact of rising and falling markets, as well as exogenous factors such as political and regulatory instability, on herding behavior. Our findings are twofold. First, herding behavior differs by sector, moreover, each sector responds differently to rising and falling markets. Thus, failing to consider each sector separately may mask herding behavior. Second, herding behavior is most pronounced from 2013 through 2014, which was a time of both political and regulatory instability for Kenya.

We develop an empirical framework to show the importance of money during the Great Moderation, while accounting for the fact that monetary policy was exclusively conducted through interest rates. We estimate the impulse response functions and forecast error variance decomposition derived from a structural VAR with a least absolute shrinkage and selection operator–based lag selection. The variance decomposition suggests that a substantial component of macroeconomic variation has been driven by shocks to the money market, which were not only unintended by the Federal Reserve, but worse passed unnoticed allowing those shocks to accumulate over time.