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on Macroeconomics |
| By: | Michael D. Bordo (Rutgers University, NBER, Hoover Institution, Stanford University); John V. Duca (Oberlin College, Federal Reserve Bank of Dallas) |
| Abstract: | The rise of inflation in 2021 and 2022 surprised many macroeconomists who ignored the earlier surge in money growth because of past instability in the demand for simple-sum monetary aggregates. We find that the demand for more theoretically-based Divisia aggregates can be modeled and that these aggregates provide useful information about nominal GDP. Unlike M2 and Divisia-M2, whose velocities do not internalize shifts in liabilities across commercial and shadow banks, the velocities of broader Divisia monetary aggregates are stable and can be empirically modeled through the Covid-19 pandemic. In the long run these velocities depend on regulation and mutual fund costs that affect the substitutability of money for other financial assets. In the short run, we control for swings in mortgage activity and use vaccination rates and the stringency of government pandemic restrictions to control for the unusual pandemic effects. The velocity of broad Divisia money declines during crises like the Great and COVID Recessions, but later rebounds. In these recessions, monetary policy lowered short-term interest rates to zero and engaged in quantitative easing of about $4 trillion. Nevertheless, broad money growth was more robust in the COVID Recession, likely reflecting a less impaired banking system that could promote rather than hinder deposit creation. Our framework implies that nominal GDP growth and inflations rebounded more quickly from the COVID Recession versus the Great Recession. Our different scenarios for future Divisa money growth and the unwinding of the pandemic have different implications for medium-term nominal GDP growth and inflationary pressures. |
| Keywords: | Velocity, Monetary Services Index, Divisia, Liquidity, Money, Shadow Banks, Mutual Funds |
| JEL: | E51 E41 E52 E58 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:pri:cepsud:319&r=mac |
| By: | Richard Schnorrenberger; Aishameriane Schmidt; Guilherme Valle Moura |
| Abstract: | We investigate the predictive ability of machine learning methods to produce weekly inflation nowcasts using high-frequency macro-financial indicators and a survey of professional forecasters. Within an unrestricted mixed-frequency ML framework, we provide clear guidelines to improve inflation nowcasts upon forecasts made by specialists. First, we find that variable selection performed via the LASSO is fundamental for crafting an effective ML model for inflation nowcasting. Second, we underscore the relevance of timely data on price indicators and SPF expectations to better discipline our model-based nowcasts, especially during the inflationary surge following the COVID-19 crisis. Third, we show that predictive accuracy substantially increases when the model specification is free of ragged edges and guided by the real-time data release of price indicators. Finally, incorporating the most recent high-frequency signal is already sufficient for real-time updates of the nowcast, eliminating the need to account for lagged high-frequency information. |
| Keywords: | inflation nowcasting; machine learning; mixed-frequency data; survey of professional forecasters; |
| JEL: | E31 E37 C53 C55 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:806&r=mac |
| By: | Conrad, Christian; Lahiri, Kajal |
| Abstract: | Macroeconomic expectations of various economic agents are characterized by substantial cross-sectional heterogeneity. In this paper, we focus on expectations heterogeneity among professional forecasters. We first present stylized facts and discuss theoretical explanations for heterogeneous expectations. We then provide an overview of the empirical evidence supporting the different theories and point to directions for future research. Our literature review is complemented by empirical evidence based on the ZEW Financial Market Survey, covering the behavior of expectations heterogeneity during the recent surge in inflation in 2021 and 2022. A central finding is that differences in perceptions about the workings of the economy and heterogeneity in perceptions of the precision of new signals drive disagreement among professional forecasters. While the level of disagreement varies over the business cycle, differences in beliefs persist over time. |
| Keywords: | disagreement, expectations, forecasts, rationality, survey data |
| JEL: | C53 D83 D84 E17 E37 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:283583&r=mac |
| By: | Alexandru Petrea (Alexandru cel Bun Military Academy, Chisinau, Republic of Moldova) |
| Abstract: | The European Union has set ambitious targets for renewable energy, aiming to increase the percentage share of renewable energy in gross final energy consumption and promote its use in transportation and heating sectors. Romania, having a significant potential in renewable energy, especially wind and solar energy, can play an important role in achieving these goals. The exploitation of abundant natural resources and the development of production capacities in wind, solar and hydropower can contribute to the transition to a cleaner and more sustainable energy system, bringing economic and environmental benefits to the country and contributing to significant reductions in greenhouse gas emissions. |
| Keywords: | Renewable Energy Source, Energy Transition, Energy Policies, Energy Efficiency, Climate Goals, Sustainable Energy Technologies and Assessments |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:smo:raiswp:0286&r=mac |
| By: | Alexander Cuntz; Carsten Fink; Hansueli Stamm |
| Abstract: | The emergence of Artificial Intelligence (AI) has profound implications for intellectual property (IP) frameworks. While much of the discussion so far has focused on the legal implications, we focus on the economic dimension. We dissect AI's role as both a facilitator and disruptor of innovation and creativity. Recalling economic principles and reviewing relevant literature, we explore the evolving landscape of AI innovation incentives and the challenges it poses to existing IP frameworks. From patentability dilemmas to copyright conundrums, we find that there is a delicate balance between fostering innovation and safeguarding societal interests amidst rapid technological progress. We also point to areas where future economic research could offer valuable insights to policymakers. |
| Keywords: | Artificial Intelligence, Intellectual Property, Patents, Copyright |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:wip:wpaper:77&r=mac |
| By: | Baikie, Victoria (Monash University) |
| Abstract: | This paper analyses how income inequality changes through the clean energy transition. Gini Coefficients are used to present overall changes in inequality over the chosen time period. Influences of rooftop solar and electrification are considered in this report as the literature suggests there is unequal access to these technologies. Key findings suggest the energy transition contributes to an overall decline in inequality from 2023 to 2050 and energy prices become cheaper. Larger proportion of households with solar, reduces the burden of high energy prices. However, the fall in inequality is shown to not be equal across all income brackets with the lowest two brackets declining the least. In the data, the gap between the highest and lowest income brackets remains prevalent at the point of Net Zero. |
| Keywords: | Energy Transition ; Income Inequality ; Solar ; Energy Prices JEL classifications: P28 ; Q43 ; Q58 ; O15 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:wrkesp:71&r=mac |
| By: | Anton Kolotilin; Alexander Wolitzky |
| Abstract: | We offer a simple analysis of the problem of choosing a statistical experiment to optimize the induced distribution of posterior medians, or more generally $q$-quantiles for any $q \in (0, 1)$. We show that all implementable distributions of the posterior $q$-quantile are implemented by a single experiment, the $q$-quantile matching experiment, which pools pairs of states across the $q$-quantile of the prior in a positively assortative manner, with weight $q$ on the lower state in each pair. A dense subset of implementable distributions of posterior $q$-quantiles can be uniquely implemented by perturbing the $q$-quantile matching experiment. A linear functional is optimized over distributions of posterior $q$-quantiles by taking the optimal selection from each set of $q$-quantiles induced by the $q$-quantile matching experiment. The $q$-quantile matching experiment is the only experiment that simultaneously implements all implementable distributions of the posterior $q$-quantile. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.17142&r=mac |
| By: | Pengfei Zhao; Haoren Zhu; Wilfred Siu Hung NG; Dik Lun Lee |
| Abstract: | Volatility, as a measure of uncertainty, plays a crucial role in numerous financial activities such as risk management. The Econometrics and Machine Learning communities have developed two distinct approaches for financial volatility forecasting: the stochastic approach and the neural network (NN) approach. Despite their individual strengths, these methodologies have conventionally evolved in separate research trajectories with little interaction between them. This study endeavors to bridge this gap by establishing an equivalence relationship between models of the GARCH family and their corresponding NN counterparts. With the equivalence relationship established, we introduce an innovative approach, named GARCH-NN, for constructing NN-based volatility models. It obtains the NN counterparts of GARCH models and integrates them as components into an established NN architecture, thereby seamlessly infusing volatility stylized facts (SFs) inherent in the GARCH models into the neural network. We develop the GARCH-LSTM model to showcase the power of the GARCH-NN approach. Experiment results validate that amalgamating the NN counterparts of the GARCH family models into established NN models leads to enhanced outcomes compared to employing the stochastic and NN models in isolation. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.06642&r=mac |
| By: | Renan P. de Oliveira; Alessandro V. M. Oliveira |
| Abstract: | This study discusses the literature on the convergence of business models of airlines in Brazilian air transport, focusing on the formation of flight networks. Initially, it analyzes the determinants of the network formation patterns of the "fundamental" business models (archetypes) of airlines in the first years after the sector's deregulation. Then, it discusses how the business models of Brazilian companies resemble these patterns. The literature highlights convergences between the network formation strategies of full-service companies in relation to older low-cost companies, in addition to business model redirections after mergers and acquisitions. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.11371&r=mac |
| By: | Shaheen, Susan PhD; Martin , Elliot PhD; Cohen, Adam |
| Abstract: | This brief explores how shared micromobility (bikesharing and scooter sharing) has evolved since the pandemic. Primary data for this report were collected through four surveys: An Operator Survey (n=25) and an Agency Survey (n=52) distributed between January 2022 and May 2022 to all known shared micromobility operators and agencies and included questions about the attributes of shared micromobility systems1 operating within those agency jurisdictions and operator markets; and a similar Operator Survey (n=29) and an Agency Survey (n=52) distributed between January 2023 and June 2023 to all known shared micromobility operators and agencies. |
| Keywords: | Engineering |
| Date: | 2024–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsrrp:qt4h04w8m1&r=mac |
| By: | Frankovic, Ivan; Etzel, Tobias; Falter, Alexander; Gross, Christian; Ohls, Jana; Strobel, Lena; Wilke, Hannes |
| Abstract: | This paper presents the methodology applied in the Deutsche Bundesbank's climate transition stress test for the German financial system, see Deutsche Bundesbank (2023). It discusses the construction of the transition scenarios underlying the analysis, including a long-run orderly scenario and a more disruptive short-term carbon price shock. Furthermore, the document shows the methodology for translating scenario impacts onto the asset level, which includes the consideration of firm-level carbon emission data where available. Finally, the impacts on the balance sheets of German banks, funds and insurers are discussed. |
| Keywords: | climate risks, stress testing, climate scenarios, financial stability |
| JEL: | G2 H23 Q5 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubtps:283347&r=mac |
| By: | Harstad, Bard (Stanford U) |
| Abstract: | International trade and natural resource exploitation interact in multiple ways. This paper first presents a dynamic game in which the South (S) exploits (e.g., deforests) in order to export (e.g., lumber or agricultural products). Because of negative externalities, the North might lose from trade, unless the resource has already been depleted. Anticipating this, S exploits more. All negative results are reversed if renegotiation-proof tariffs can be contingent on the size of the remaining resource stock. Larger gains from trade, and more attractive terms of trade, can be used to slow exploitation. Combined with export subsidies, the outcome is first best. |
| JEL: | F13 F18 F55 Q37 Q56 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:stabus:4127&r=mac |
| By: | Tasso Adamopoulos; Fernando Leibovici |
| Abstract: | We study the role of international trade risk for food security, the patterns of production and trade across sectors, and its implications for policy. We document that food import dependence across countries is associated with higher food insecurity, particularly in low-income countries. We provide causal evidence on the role of trade risk for food security by exploiting the exogeneity of the Ukraine-Russia war as a major trade disruption limiting access to imports of critical food products. Using micro-level data from Ethiopia, we empirically show that districts relatively more exposed to food imports from the conflict countries experienced a significant increase in food insecurity by consuming fewer varieties of foods. Motivated by this evidence, we develop a multi-country multi-sector model of trade and structural change with stochastic trade costs to study the impact and policy implications of trade risk. In the model, importers operate subject to limited liability and trade off the production cost advantage against the risk of higher trade costs when sourcing goods internationally. We find that trade risk can threaten food security, with substantial quantitative effects on trade flows and the sectoral composition of economic activity. We study the desirability of trade policy and production subsidies in partially mitigating exposure to trade risk and diversifying domestic economic activity. |
| Keywords: | food security; trade; risk; structural change; productivity |
| JEL: | E10 F10 F60 I30 O11 O41 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:97907&r=mac |