Business Cycle Research Articles

EXTRACTING PRICE SIGNALS FROM THE TERM STRUCTURE OF COMMODITY AND CREDIT MARKETS USING AUSTRIAN BUSINESS CYCLE THEORY

Austrian School economists first explained the significance of interest rates for the structure of production in time. Furthermore, they showed how manipulating the level of interest and the credit supply causes the business cycle (v.Böhm-Bawerk, 1891; v.Hayek, 1938; v.Hayek, 1941 and v.Mises, 1949). More recently, Austrian School economists have elaborated the relationship between the term structure of interest rates and the business cycle (Barnett and Block, 2009; Bagus and Howden, 2010; Bagus et al., 2018; Potuzak and Nemec, 2020; Alonso-Neira and Sanchez-Bayon, 2023). At the same time, market practitioners understand the potency of yield curve inversions as a predictor of future recessions as a result of experience and observation unrelated to Austrian Business Cycle Theory (ABCT). Reference textbooks (Stigum and Crescensi, 2007) explain that capital allocators are pricing in future cuts to the central bank discount rate, thus inverting the yield curve. This ignores the role of free-market price signals in credit, which result from supply and demand fundamentals, and are the link connecting ABCT to term structures.The term structure of commodities markets is also affected by actual or anticipated shortfalls in production over different time horizons. This is widely understood, perhaps because settlement by physical delivery is commonplace and tangible (Hull, 2022). Even though credit and derivatives may at first glance seem more abstract, the same underlying market principles are at work there. The theoretical foundations of and recent work on ABCT provide the necessary tools to analyse both markets through the same lens.

Divisia monetary aggregates, monetary policy and business cycle analysis

Abstract The paper builds a parsimonious US business cycle SVARMA model, establishing identification conditions for independent monetary shocks. The SVARMA model, utilizing Divisia M3 and Divisia M4, is compared to the simple sum M2. The monetary rule with Divisia M3 yields theoretically consistent results marked by the absence of the usual price and liquidity puzzles. As the Federal Reserve (Fed) took a more hawkish approach to curb inflation, significant increases in US interest rates and declines in monetary aggregates were largely influenced by the Fed’s reaction function, which incorporates the Divisia M3 monetary rule. Findings emphasize the monetary impact on the business cycle, highlighting the significance of Divisia monetary aggregates. Historical and variance decompositions reveal diverse, dynamic effects of monetary shocks on macroeconomic variables. The SVARMA model with Divisia M3 and M4 demonstrates superior performance over simple sum M2 in capturing the time path of monetary shocks.

Analyzing Business Cycles in Azerbaijan: Application of Various Filters and Spectral Analysis

This study investigates the cyclical behavior of Azerbaijan's economy and its non-oil-gas sector through the application of various filtering techniques, including the Hodrick-Prescott, Baxter-King, and Christiano-Fitzgerald filters. Utilizing quarterly GDP data from Q1 2001 to Q4 2023, we analyze the volatility, cycle duration, and significant periods of economic growth and contractions. Our analysis reveals that the Hodrick-Prescott filter shows more pronounced and volatile cyclical components, while the Baxter-King and Christiano-Fitzgerald filters provide smoother and more moderated views. Spectral analysis, employing periodograms smoothed with Bartlett, Hamming, Hann, and Parzen kernel windows, identifies cycle durations ranging from 4.1 to 19.4 quarters for total GDP and 4.3 to 19.4 quarters for non-oil-gas GDP. We find that Azerbaijan's economy exhibits higher volatility compared to advanced economies, likely due to its dependence on oil exports and susceptibility to external shocks. Key periods such as the 2008 financial crisis and the COVID-19 pandemic significantly impacted economic performance, with notable declines identified across all filtering methods. The HP filter indicates a sharp and more prolonged downturn during these crises, whereas the BK and CF filters capture less prolonged and less severe impacts. This research provides a comprehensive understanding of Azerbaijan's business cycles, highlighting the importance of robust methodologies in economic analysis. Our findings contribute to the limited literature on Azerbaijan’s economy and offer valuable insights for policymakers aiming to enhance economic stability and growth.

The Estimating Parameter and Number of Knots for Nonparametric Regression Methods in Modelling Time Series Data

This research aims to explore and compare several nonparametric regression techniques, including smoothing splines, natural cubic splines, B-splines, and penalized spline methods. The focus is on estimating parameters and determining the optimal number of knots to forecast cyclic and nonlinear patterns, applying these methods to simulated and real-world datasets, such as Thailand’s coal import data. Cross-validation techniques are used to control and specify the number of knots, ensuring the curve fits the data points accurately. The study applies nonparametric regression to forecast time series data with cyclic patterns and nonlinear forms in the dependent variable, treating the independent variable as sequential data. Simulated data featuring cyclical patterns resembling economic cycles and nonlinear data with complex equations to capture variable interactions are used for experimentation. These simulations include variations in standard deviations and sample sizes. The evaluation criterion for the simulated data is the minimum average mean square error (MSE), which indicates the most efficient parameter estimation. For the real data, monthly coal import data from Thailand is used to estimate the parameters of the nonparametric regression model, with the MSE as the evaluation metric. The performance of these techniques is also assessed in forecasting future values, where the mean absolute percentage error (MAPE) is calculated. Among the methods, the natural cubic spline consistently yields the lowest average mean square error across all standard deviations and sample sizes in the simulated data. While the natural cubic spline excels in parameter estimation, B-splines show strong performance in forecasting future values.

Entrepreneurial firm creation and economic uncertainty: an explainable artificial intelligence approach

ABSTRACT Through this paper, we examined the relationship between macroeconomic factors and new entrepreneurial firms in France during the 2000–2020 period; this relationship included several uncertainties and shocks (the 9/11, the 2008 global financial crisis, the Brexit, COVID-19 Pandemic, and so forth). To this aim, we applied various advanced machine learning (ML) techniques, including the eXtreme Gradient Boosting (XGBoost) algorithm, Light Gradient-Boosting Machine (LightGBM) algorithm, Neural Network (NN), Random Forest (RF), and Linear Regression (LR). We also investigated SHapley Additive exPlanation (SHAP) framework for interpretation and analysis purposes. Furthermore, we compared the performance and forecasting accuracy of the five models. The study revealed three major results. First, the XGBoost algorithm provides the most accurate prediction of a firm’s creation. Second, a heterogeneous relationship exists between new business creation and the business cycle. Third, higher GDP and unemployment levels are associated with greater procyclicality and countercyclicality in entrepreneurship, respectively. Finally, the results of the study suggest the importance of developing effective financial assistance during economic downturns to encourage faster recovery in the formation of new businesses.

Granular origins of the Chinese business cycle

ABSTRACT Recent events in China, like Foxconn’s factory downsizing and Evergrande’s bankruptcy, have significantly impacted regional economies, highlighting how idiosyncratic shocks from large firms can generate aggregate effects. Using the ‘granular hypothesis’, which posits a significant impact of large firms’ actions on the macroeconomy, this study investigates how idiosyncratic shocks from large Chinese firms affect regional productivity growth and markups. Our findings demonstrate that idiosyncratic shocks to the 100 largest firms per province significantly impact regional labour productivity and total factor productivity growth but not gross regional domestic product growth. Subsequently, we examine how idiosyncratic shocks impact product market distortions, revealing that idiosyncratic shocks to large firms increase markups in regional markets, suggesting amplified market power.

Labour market skills, endogenous productivity and business cycles

This paper analyses how labour market heterogeneity affects unemployment, productivity and business cycle dynamics. To this aim, we set up a model with asymmetric search and matching frictions across skilled and unskilled workers, and endogenous productivity through R&D investment and intangible capital accumulation. Skill mismatch and skill-specific labour market institutions have three main effects on business cycles and growth dynamics. First, the relative scarcity of skilled workers increases the natural rate of unemployment and reduces total factor productivity with long-run effects on the growth rate of output. Second, skill heterogeneity in the labour market generates asymmetric outcomes and amplifies measures of employment, wages and consumption inequality. Finally, the model provides important insights for the Phillips and Beveridge curves. Incorporating skill heterogeneity leads to a flattening of the Phillips curve as wages and unemployment respond unevenly across skill types. Also, the model generates sideward shifts of the Beveridge curve following business cycle shocks, with the extent of these shifts depending on the degree of skill heterogeneity.

Devastation of the cerrado of mato grosso do sul and the advance of arenization in the pardo river watershed

Despite the significant biodiversity in the Cerrado, the process of occupation, subsequent economic cycles, and intensification of agriculture in a predatory manner have caused profound alterations in this biome. This study analyzed changes in land use and land cover in the Cerrado of Mato Grosso do Sul between 1985 and 2022 by mapping and also investigating the formation and dynamics of sand dunes in the Bauru sedimentary basin, focusing on the Pardo River watershed. MapBiomas Network data, collection 8, were used for land use and land cover analysis. Arenization foci cartography was conducted using brightness index 2 based on Sentinel-2 satellite images in Sentinel Application Platform software. The historical analysis of land use and cover in the Cerrado of Mato Grosso do Sul revealed a rapid transformation of the landscape, with a drastic reduction of native vegetation and an intensification of large-scale monocultures. Between 1985 and 2022, the Cerrado of Mato Grosso Sul lost 4.6 million hectares of native vegetation (forest formations, savannahs, grasslands, and wetlands), representing 24.54% of this territory. Recent changes in land use include pasture-soybean and pasture-eucalyptus. In the Pardo River watershed, arenization foci cover an area of 17,834.34 hectares, with varying dimensions ranging from less than 1 hectare to 376.41 hectares. The arenization process occurs in different slope compartments, such as the base, slope, and interfluves. The physical and chemical characteristics of Quartzipsamments, such as low organic carbon content in soil, are associated with high rates of surface exposure and weakened load-dependent structures, leading to degradation. Arenization occurs where natural vegetation has been suppressed and soil has been depleted by poorly managed production processes.

The Integration of Machine Learning in Central Banks: Implications and Innovations

The effectiveness of using artificial intelligence (AI) techniques to lessen the negative effects of economic cycles is examined in this study article. Economic cycles, which are defined by variations in the level of economic activity, present important difficulties for decision-makers in government, business, and society at large. This research investigates many approaches to reducing economic cycles, such as forecasting, policy creation, and adaptive decision-making, by utilizing AI, namely machine learning algorithms. The first section of the paper reviews the body of research on economic cycles and how artificial intelligence might be used to address them. After that, it explores certain AI methods including neural networks, time series analysis, and natural language processing, explaining how they might be used for risk management and economic forecasting. Additionally, the study investigates AI-driven policy interventions, examining how machine learning algorithms might be used to optimize monetary and fiscal policies in order to sustain growth and prevent economic downturns. The study also addresses the difficulties and constraints that come with using AI technologies in economic management, such as problems with algorithmic biases, data quality, and legal considerations. It highlights how crucial interdisciplinary cooperation and openness are to creating AI-driven solutions that are reliable, moral, and socially conscious. This research paper offers insights into the possible advantages and disadvantages of employing AI tools for damping economic cycles through empirical analysis and case studies. In order to fully realize AI's promise in fostering resilience and stability in the economy, it ends by presenting future research areas and policy suggestions. Keywords: artificial intelligence, trend, economic cycles, resilience

Business Cycle Dynamics in the Romanian Economy: Phases and Developments

Romania's economy is not yet sufficient / complete and competitive connected to the developed European economy and the global one. In recent economic and general political history, the political, institutional and economic changes that have taken place since 1990 in Central and Eastern Europe, including Romania, are already recognized. The article deals with the evolutionary situational stages towards the New Economy of Romania. It also describes the post-transformation horizon for achieving quasi-constant inverted levels between “tangible-intangible” for the transition to a new business cycle in the New Knowledge-Based Economy and Risk. It follows that if GDP reflects the sum of the market value of all goods and services intended for final consumption, not intermediate ones, produced in all branches of the economy within Romania within a year, by way of investigative proximity, business and cycles of manifestation of these were closely followed by dimensional evolutionary trends of similarity and aggregation with GDP. In fact, the New Knowledge-Based Economy has radically and positively changed Romania's advance towards the digitalized society, based more and more on the operationalization of intangible assets. The problem of vision, strategy and development tactics through new business cycles is found in the search for the optimal structural ratio between tangible and intangible assets (knowledge) insurers for competitiveness and sustainability. Keywords: business cycle, Romanian economy, business, entrepreneurship, investment, new knowledge-based economy, tangible assets, intangible assets

Forecasting U.S. stock returns conditional on geopolitical risk and business cycles

Using standard predictors in the forecasting literature, we forecast the U.S. stock market returns conditional on geopolitical risk and business cycles over the 1927–2021 period. We find that out-of-sample forecasting performance is significantly better in times of high geopolitical risk versus low geopolitical risk. Consistent with previous research, we find further evidence of improved return predictability in recessions. However, we find little difference in forecasting performance in recessions versus expansions once the level of geopolitical risk is controlled for. We find similar results when using stock market cycles and periods of positive/negative industrial production growth in place of recessions/expansions. Our study contributes to the forecasting literature by documenting that geopolitical risk by itself and in combination with business cycle indicators impacts the forecasting ability of standard forecasting variables in the literature. We also contribute to the literature on the adaptive markets hypothesis with evidence of time-varying return predictability. We find inconclusive evidence as to whether our results are based on time-varying predictability or time-varying risk.

Overeducation under different macroeconomic conditions: How Spanish university graduates fit in the labor market?

This paper analyzes a relevant issue for education and labor policymakers, overeducation in the early careers of university graduates. Using Spanish data, we investigate the role played by the business cycle and field of study and their interaction in shaping both the incidence and persistence of overeducation. We also analyze the relevance of specific types of knowledge and skills as driving factors in reducing overeducation risk. Our data come from the Survey on the Labor Insertion of University Graduates (EILU) conducted by the Spanish National Statistics Institute in 2014 and 2019. The survey collects rich information on cohorts that graduated in 2019 and 2014, during the Great Recession and the subsequent economic recovery, respectively. Our results show, first, the relevance of the economic scenario when graduates enter the labor market. Graduation during a recession increases overeducation risk and persistence. Second, a clear heterogeneous pattern is observed across fields of study, with health sciences graduates displaying better performance in terms of both overeducation incidence and persistence and less impact of the business cycle. Third, we find evidence that some transversal skills can help to reduce overeducation risk in the absence of specific knowledge required for the job, thus indicating some kind of compensatory role. Our findings have important policy implications. Overeducation, and more importantly overeducation persistence, imply a non-neglectable misallocation of resources, with serious consequences not only for the affected individuals but for the society as a whole. Therefore, policymakers need to address this issue in the design of education and labor market policies, seeking to ensure that the competencies acquired at university provide graduates with a better fit in the labor market.

Is the business cycle getting hit by climate policy uncertainty in China?

Is the business cycle getting hit by climate policy uncertainty in China?

Re-examining international spillovers: An Asian perspective

This study investigates the transmission and the business cycle implications of Chinese export shocks to other Asian economies. Based on a panel vector autoregression (PVAR) model, we provide evidence that a positive export shock originating from China simultaneously stimulates aggregate demands for both China and other Asian economies. The PVAR model takes into account three main sources of potential interactions among ten Asian economies. Simulations from a two-country DSGE model featuring endogenous trade links suggest that positive trade spillover is essential to explain the macroeconomic comovement pattern. Our findings further indicate the importance of the Chinese economy to business cycle synchronization.

Vulnerable options with regime switching and stochastic liquidity

Investigating default risk in pricing options holds significant practical importance, as nearly all market participants and institutions face credit risk. Additionally, economic cycles and asset liquidity are crucial factors that should be incorporated. This paper considers these factors and derives an analytical pricing formula. Specifically, we model the economic cycles through switching volatility driven by a continuous-time Markov chain, while we adopt a discounting factor based on market liquidity levels to model the asset liquidity. We establish a risk-neutral measure after embracing a regime-switching Esscher transform, and formulate a price representation to value vulnerable options analytically despite the complexity of the developed model. We conduct several numerical experiments to validate the model’s efficacy and flexibility.

Project-oriented strategic management of UK construction firms – towards corporate diversification: critical literature review

PurposeThe paper analyses the strategic management strategies of UK Construction Contracting Firms (CCFs) and their impact on the industry, highlighting their fragmentation, high-risk, low-profit nature and low profit returns. It suggests proactive strategies for sustainable growth and explores the potential of corporate diversification.Design/methodology/approachUtilising a thematic critical literature review, specific inclusion/exclusion criteria are used to select relevant literature together with a thematic qualitative synthesis data analysis approach to identify trends and challenges.FindingsUK CCFs primarily use reactive and project-based strategic management, which may not align with long-term success due to market volatility, reactive supply, fragmentation, high competition and lack of differentiation. The short-term business cycle makes medium- to long-term strategy development difficult. It is recommended that CCFs adopt proactive strategic management and consider corporate diversification for enhanced competitiveness, stability and performance. Although there are conflicting findings on the impact of diversification on firm performance, this research suggests that it is a viable strategy for achieving enhanced firm performance and competitive advantage.Research limitations/implicationsThe importance of proactive corporate strategies for CCFs is emphasised to overcome industry challenges, promote sustainable growth and enhance competitiveness. Corporate diversification, cultural transformation, management qualifications promotion and talent development collaboration are advocated, providing valuable insights for practitioners, policymakers and researchers.Practical implicationsThe practical implications of this research involve fostering a shift towards proactive and dynamic strategic management in the UK construction industry, addressing the historical neglect of broader strategic perspectives and empowering practitioners and academics to drive positive change and innovation within the sector.Social implicationsThe social implications of this research encompass the potential to enhance the overall efficiency, sustainability and collaborative dynamics within the UK construction industry, which can ultimately contribute to improved infrastructure development and the well-being of communities.Originality/valueProject-driven strategic management in the UK construction industry is explored, questioning the reactive approach used by CCFs. It provides insights, best practices and improvement areas, emphasising diversification, proactive corporate strategies, cultural shifts and industry improvement, spanning theory, practice and theory.

Automation, Bargaining Power, and Labor Market Fluctuations

We argue that the threat of automation weakens workers’ bargaining power in wage negotiations, dampening wage adjustments and amplifying unemployment fluctuations. We make this argument based on a business cycle model with labor market search frictions, generalized to incorporate automation decisions. In the model, procyclical automation threats create endogenous real wage rigidity that amplifies labor market fluctuations. The automation mechanism is consistent with empirical evidence. It is also quantitatively important for explaining the large volatilities of unemployment and vacancies relative to that of real wages, a puzzling observation through the lens of standard business cycle models. (JEL E13, E24, E32, J31, J63, J64)

A Simple Explanation of Countercyclical Uncertainty

This paper documents that labor search and matching frictions generate countercyclical uncertainty because the inherent nonlinearity in the flow of new matches makes employment uncertainty increasing in the number of people searching for work. Quantitatively, this mechanism is strong enough to explain uncertainty and real activity dynamics, including their correlation. Through this lens, uncertainty fluctuations are endogenous responses to changes in real activity that neither affect the severity of business cycles nor warrant policy intervention, in contrast with leading theories of the interaction between uncertainty and real activity dynamics. (JEL D81, E23, E24, E32, J41, J63, J64)

International monetary fund conditionality and structural reforms: Evidence from developing countries

AbstractThe global economy, dominated by the consequences of a disastrous health crisis and international tensions, needs policy support to regain its growth dynamic. To regain an inclusive and sustainable growth dynamic, structural policies of governments are needed to allow a reallocation of resources and to stimulate productivity. International cooperation seems to be necessary and the IMF's contribution could play an important role in promoting reforms. Indeed, in IMF‐supported programmes, the conditions for developing countries' transition to liberalization and open markets are often common. We address the question of whether periods of International Monetary Fund (IMF) conditionality contribute to the promotion of structural reforms in developing countries. Through the entropy balancing method and alternative identification strategies, we show that IMF conditionality programmes promote structural reforms in developing countries. We show that the effect of IMF conditionality can vary depending on the type of conditionality, the type of reform, the time frame and the initial level of structural reforms and can depend on some structural factors including the business cycle, the quality of fiscal and monetary policy, the level of development and the quality of institutions. Furthermore, we show that IMF conditionality can have spillover effects on trading partners and that IMF conditionality programmes that are met tend to have a greater impact on structural reforms. Finally, the effects of adopting reforms under IMF conditionality depend on domestic partisan politics.

Decomposing Uncertainty: How Foreign Policy Risks Shape Chinese Stock Market Dynamics

This study examines the transmission mechanism of foreign economic policy uncertainty (FEPU) on the Chinese stock market from 1997 to 2022, using a vector autoregressive adaptation of the dividend discount model. By decomposing excess returns into cash flow (CF), excess return (ER), and risk-free rate (RF) components, we disentangle the effects of FEPU shocks on these distinct factors.Our findings indicate that FEPU primarily leads to a significant but temporary overestimation of impacts on cash flows and excess returns, with the market typically correcting itself after an initial overreaction. This pattern suggests a complex interplay between investor sentiment and market dynamics. Notably, trade stability amplifies the impact on CF, while closer monetary policy coordination enhances the transmission to RF and ER expectations. Additionally, economic cycles play a moderating role: downturns exacerbate the negative impacts on CF and RF, while bullish conditions foster resilience. Investor sentiment emerges as a crucial factor, with negative sentiment intensifying FEPU’s adverse effects during economic contractions.This research contributes a detailed perspective on how international relations, policy harmonization, and market psychology collectively influence FEPU transmission and affect China’s financial stability amidst global uncertainties. Our decomposition method illuminates the dynamic forces that drive market reactions over time, offering critical insights for refining risk management strategies and crafting policy measures that buffer the financial system against external economic shocks.