This paper examines Cybersyn—Chile’s cybernetic coordination system under Salvador Allende—during the October 1972 national truckers’ strike, which cut aggregate industrial output by roughly 9
Since the early 1980s, economic volatility has decreased sharply, and growth rates have declined marginally, especially in more recent years. The two phenomena are related, and a positive relationship between volatility and growth may be due to the cleansing effect of recessions. Should this be the case, stabilization policies have downsides because, if successful, they could lead to diminished growth. Using an autoregressive exponential GARCH-in-mean (ARMA-EGARCH-M) framework from 1959 to 2019, this paper highlights a positive relationship between economic volatility and growth. Using United States data, this framework shows that fiscal and monetary interventions are stabilizing, but over the long run they are detrimental to growth and prosperity.
With the adoption of inflation targeting in South Africa in 2000, the Bureau for Economic Research (BER) began to collect inflation expectations survey data on behalf of the South African Reserve Bank. This respected survey is rich by international standards and has contributed valuable insights to policy, academic and private sector analyses. International trends towards greater reliance on microdata within macroeconomics are, however, placing slightly different demands on the survey, and access to complementary datasets has offered new opportunities to enhance it. In this pilot study, we link the inflation expectations survey data of firms to a spatial tax panel dataset. We investigate whether the survey sample adequately represents the structure of the South African economy and offer a series of survey weights to be added to the micro dataset. The results show that the BER has maintained an adequate level of representativity over the life of the survey, but we recommend that sample weights be estimated periodically to ensure that representativity is ensured institutionally. The sample weights can also support targeted recruitment in future. Finally, through careful documentation we hope to enable other researchers to pursue questions that benefit from linking the inflation expectations data with other datasets.
Estimating output gaps is challenging for emerging Asian economies due to limited data availability and the potential effects of outliers. I apply the Beveridge-Nelson (BN) filter, comparing it with commonly used filters—Hodrick-Prescott (HP), Christiano-Fitzgerald (CF), and Hamilton—across four emerging Asian economies and argue that it provides more reliable and informative estimates than alternative methods. When benchmarked against narrower indicators of slack, BN filter output gap estimates provide a more informative indicator than capacity utilization and unemployment, given longer data coverage and controlling for long-run structural changes. I also document two systematic results for these economies. First, cyclical consumption is more volatile than the output gap. Second, decomposing GDP growth volatility shows that less than one-third of growth fluctuations is accounted for by movements in trend growth, with most variation attributed to the cyclical component. Taken together, these findings contrast with the interpretation in Aguiar and Gopinath (2007) that shocks to the trend are the primary driver of fluctuations in emerging economies and departs from their view that the “cycle is the trend.” Crucially, the BN filter estimates are also subject to smaller and less frequent revisions when faced with large changes in economic conditions, which benefits real-time policy decision-making.
In this paper we introduce a novel measure of uncertainty defined at the micro-level as firms’ inability to forecast the evolution of own revenues in the current or subsequent year. In order to do so, we exploit newly-available survey data collected by the Chamber of Commerce of Bolzano–Bozen for the period 2014–2023. Uncertainty is higher among firms of smaller size, active in construction and manufacturing, and with the legal form of the sole proprietorship. Looking at the evolution of uncertainty over time, we find a significant increase in the share of uncertain firms in 2021, which persisted (although of smaller magnitude) through the most recent available year, 2023. The overall increase was highest among firms of smaller size and among those with the legal form of a sole proprietorship. We finally analyze the link between firm-level uncertainty and investment behavior: the probability that a firm increases its investments is 3.6 percentage points lower among uncertain firms than among others (the average share is just above 20 percent).
A robust GDP nowcasting model must dynamically adapt to time-varying circumstances to avoid becoming obsolete. We propose a state-based weighted estimation approach, which leverages prior data observed in similar economic conditions to better capture current economic dynamics. Our method uses a diverse set of state variables to compare economic states over time, including media news-based indicators and macro-financial variables. Empirical results show that state-based weighting significantly improves GDP growth nowcasting compared to traditional unweighted approaches.
There is a long tradition of using sentiment in economic analysis. Sentiment variables such as consumer confidence, inflation expectations and investor sentiment are often used to gauge the attitudes of consumers and firms about the state of the economy. These measures are typically collected through quarterly surveys, which limits their usefulness for more frequent economic monitoring. The increasing publication of online content has made large text collections easily accessible to computer algorithms for sentiment analysis. This article combines the recent accessibility of large language models with topic modelling and lexicon-based sentiment analysis to construct topic augmented sentiment indices (TaMSI) for the South African economy. The information value of TaMSI is also evaluated in an application where I forecast traditional survey-based confidence indicators using a Mixed-Frequency Bayesian VAR. The results find a reduction in forecasting errors with the inclusion of the sentiment indices for longer term forecast. The model framework has been developed and is currently in implementation at the Bureau for Economic Research, Stellenbosch, South Africa.
As a small, open economy, Uruguay is highly exposed to international and regional shocks that affect its domestic uncertainty. To account for this phenomenon, we construct geometric indices of economic uncertainty, based on quarterly data from the qualitative industrial survey on their expectations regarding the economy and the export market, between 1998 and 2022. Based on the estimated linear ARDL models that showed negative but weak relationships between the uncertainty indices and the GDP cycle, we test for the existence of structural breaks in these relationships. Wald tests carried out on the re-specified models (considering non-linearities around the break) confirmed the significance of one structural break in the third quarter of 2003 in the model of the export market uncertainty. After this date, the negative influence of uncertainty on the macroeconomic cycle fades. The evidence of a differential impact before and after this date remains when controlling for the variability of domestic prices of non-tradable goods. Two main conclusions emerge from these results. First, the results highlight the evidence that the Uruguayan economy processed relevant transformations that made it less vulnerable after 2003. Second, the importance of business expectations about the future of export markets has to be stressed, particularly in small, open and emergent economies.
This paper uses the real Gross Domestic Product (RGDP) of Pakistan and dates business cycles in Pakistan for use in econometric/non-econometric analysis on Pakistan’s economy. It follows National Bureau of Economic Research’s methodology to date business cycles in Pakistan. From fiscal year (FY) 1951-52 to FY-2015, absence of quarterly RGDP data pushes us to use Tahir et al., (2018), who estimated the quarterly RGDP from quarter 1 (Q1) of FY1978, this paper used their latest numbers in dating business cycle over the period Q1-FY1978 to Q4-FY2015. From Q1-FY2016, Pakistan Bureau of Statistics (PBS) started publishing quarterly RGDP, thus this paper uses the official statistics to date business cycle in Pakistan using the actual official data from FY2016 onwards. It is found that Pakistan’s economy underwent a recession during Q3-Q4 of FY-2020 and FY-2023, respectively. The recessionary periods are identified using graphs and supported by plotting numerous demand-side, supply-side, and general textbook macroeconomic indicators. The aim of this work is to help students, researchers, academics, and policymakers by providing updated business cycle dating platform containing dedicated economy dashboard of macroeconomic indicators, regularly with a delay of two quarters after the actual quarter in focus, as the supporting high frequency data becomes available.
The ifo Business Climate Index is one of the most important leading indicators for the German economy. It is based on a monthly survey of approximately 9000 firms and reflects responses to two core questions: the assessment of the firms’ current business situation and their expectations for the next 6 months. These questions are deliberately formulated without precise definitions, allowing each respondent to draw on its own relevant factors. This paper investigates which factors firms actually consider, whether they differ across sectors and firm types, and how their importance has changed over time. To this end, we conducted a dedicated meta-survey in 2019 and repeated it as part of the regular ifo Business Survey in 2025. Our results show that internal factors—such as profit situation, demand, and turnover—are the primary drivers of firms’ assessments. However, external influences, particularly the economic policy framework and general economic sentiment, have gained importance in recent years. These findings enhance the interpretability of the index and contribute to understanding its strong forecasting performance. The identified factors may also prove valuable for applied business cycle analysis.
This paper reassesses the “resource curse” hypothesis in the Russian economy over 1990–2021 using coherent wavelet analysis. We study the time-varying and horizon-specific relationships among natural resource rents, GDP per capita, and the Human Development Index (HDI). Contrary to a universal curse narrative, we find no persistent negative association between resource rents and development. At long horizons, rents are positively associated with HDI, suggesting scope for resource wealth to finance social progress. Short-run comovements are episodic and regime-dependent: during periods of macroeconomic stability, rents align with higher income and human development, whereas crises and institutional uncertainty weaken or temporarily reverse these links. The results imply that policy and institutional quality—rather than resource abundance per se—determine development outcomes.
This study evaluates whether feature selection improves machine learning forecasts of German business cycles. Using a high-dimensional dataset with 73 indicators, primarily from the OECD Main Economic Indicator Database, covering a period from 1973 to 2023, Sequential Floating Forward Selection (SFFS) is applied to build compact, explainable, and performant models. The focus is on regularized regression models (LASSO, Ridge, Elastic Net) and tree-based classification models (Random Forest, Gradient Boosting and AdaBoost). SFFS yields models with up to eleven indicators that outperform a standard term-spread probit model—especially during Quantitative Easing. Regularized regressions provide the most accurate recession signals. Feature selection increased the forecasting power of tree-based models, while marginally reducing the performance of regression models. The findings contribute to the ongoing discussion on the use of machine learning in economic forecasting, especially in the context of limited and imbalanced data.
This study presents an examination of the predictive power of narrative reports from German economic institutes beyond traditional quantitative forecasts in anticipating economic recessions and directional changes in the business cycle. I transform qualitative narratives into quantitative sentiment scores using four different dictionaries and methods and use fixed-effect logistic regression to analyse their impact. To evaluate model performance, I use the Area under the Receiver Operating Characteristic Curve (AUROC) to compare models with versus without sentiment scores. Additionally, I employ DeLong’s test and bootstrapping to test the significance of AUROC improvements. Furthermore, I explore the potential of combining multiple sentiment scores to enhance forecasting accuracy. The results show that sentiment scores significantly enhance forecasting accuracy. This suggests that narrative information provides valuable insights beyond quantitative forecasts alone.
Unequally spaced data poses a dilemma on how to aggregate high-frequency variables to model a low-frequency variable. To tackle this quandary, this work proposes to apply MI(xed) DA(ta) S(ampling) (MIDAS), which allows the independent and dependent variables to be sampled at various and different frequencies, to forecast the real GDP growth in Brazil using macroeconomic data. The results show that the restricted polynomial MIDAS specification can outperform the AR(1) and the unrestricted Midas for out of the sample recursively estimated nowcasts. Furthermore, this paper showcases the impact of different pooling schemas to enrich forecast combinations using the Midas framework: only the inverse MSE weighted forecast combinations beat the benchmark under the Augmented Diebold–Mariano test. Finally, the MSE cumulative ratio emerged as a compelling framework to uncover unforeseen swerves. Fortuitously, the cumulative MSE ratio revealed that between 2014Q3 until the end of 2015, the quotient for the monetary base MIDAS model continuously declined. While this behavior might not be related to the “fiscal pedaling”, its trend contributes to the economic policy narrative during those years.
This paper examines the cyclical nature of green investment in research and development (R D) within the context of the green economy, focusing on the simultaneous eco-innovation activities of multiple firms. A stochastic expanding-variety endogenous growth model is employed to explore the pro-cyclicality of green R D, demonstrating how firms’ collective green innovation efforts during economic upswings lead to higher-quality, more impactful green technologies. These innovations, spurred by environmental incentives and market demands for sustainability, significantly expand the knowledge base, pushing the green economy forward. The simulations presented over varying periods—10, 20, 30, and 40 years—reveal the long-term effects of economic cycles on green technology levels, production output, and patent valuations, indicating a potential for sustained growth in green technologies and industries driven by ongoing innovation. By integrating green innovation into the economic model, this paper highlights the role of policy incentives and market demand in fostering sustainable technology development. It suggests that well-calibrated economic policies and incentives can significantly influence the trajectory of the green economy, especially when they enhance the attractiveness and yields of green investments.
To establish economic climate indicators, most countries organize business surveys repeated at regular time intervals. Business surveys are thus repeated surveys. These countries often use seasonal adjustment procedures to obtain the estimator of the business cycle. The main question in repeated surveys is how to summarize the results, and different approaches are suggested in the literature. In this study, we present an analysis of the data from business surveys for each province of the Netherlands and a subset of questions completed by a simulation-based study. We consider the seasonal adjustment with Tramo-Seats and the simplest repeated-survey approach suggested by the literature, a weighted average between the direct estimate and an ARIMA forecast. The results show that the simplest repeated survey methods for business surveys provide better results than the existing estimators. We conclude that the business surveys for deriving economic climate indicators should benefit from a repeated-survey approach (not only the most straightforward method we use here, but also the more advanced methods) instead of seasonal adjustment recommended by the European Commission, or perhaps a combination of the two. The availability of seasonally unadjusted results from the business surveys for the different countries is needed to be able to confirm the conclusion.
This study investigates the impact of trading partners’ economic policy uncertainty (EPU) on Bangladesh’s exports. Using monthly bilateral data for 17 major partners from 2001 to 2020 and a newspaper-based EPU index, we estimate linear and nonlinear panel ARDL models with common correlated effects. Results indicate a positive long-run effect of foreign EPU on exports, reflecting Bangladesh’s competitiveness in low-cost consumer goods. The relationship is asymmetric in the long run but insignificant in the short run. Causality tests confirm bidirectional linkages between exports and EPU. Findings underscore the importance of monitoring global policy uncertainty to sustain export resilience and growth.
This research examined whether indicators derived from Principal components analysis (PCA) should be modeled as reflective or formative composite constructs in forecasting economic cycles within an Economic early warning model (EWM). The study employed Partial least squares structural equation modeling (PLS-SEM), incorporating Confirmatory tetrad analysis in partial least squares (CTA-PLS), Confirmatory composite analysis (CCA), and was further validated using out-of-sample forecasting performance. Quarterly Thai economic data were collected, in which the in-sample period spanned 2012Q3 to 2022Q4, aligning leading indicators and forecast targets for model development, with out-of-sample evaluation for the final endogenous construct from 2023Q1 to 2024Q3. Results indicate that PCA-derived indicators are better specified as reflective constructs. CTA-PLS did not reject the null hypothesis of reflectiveness, while formative misspecification led to poor performance in the CCA process, resulting in incorrect indicator deletion and reduced forecasting accuracy. These findings underscore the importance of proper model specification for statistical validity and practical forecasting effectiveness, contributing to economic cycle forecasting methodology by integrating composite indicator structure evaluation and bootstrap-enhanced inference within a structural forecasting framework.
