Economic Forecasting Research Articles

Stochastic debt sustainability analysis using time-varying fiscal reaction functions - an agnostic approach to fiscal forecasting

ABSTRACT This paper presents a model-based approach for stochastic primary balance and public debt simulations to assess fiscal sustainability in selected OECD countries. Fiscal behaviour is modelled by means of a fiscal reaction function with time-varying coefficients, which is then, together with a time-varying coefficient vector autoregression, embedded in a stochastic debt sustainability analysis framework. In a pseudo-out-of-sample forecasting exercise using vintage datasets, the model is evaluated against its frequently used fixed coefficient pendant and the European Commission’s Economic Forecasts at different horizons. The results indicate that stochastic debt sustainability analyses based on time-varying fiscal reaction functions and vector autoregressions perform competitively in terms of mean squared error and forecast bias at different horizons, especially with respect to public debt as well as short-term primary balance forecasts. Thus, models of this sort should be considered for complementary use at policy institutions, using them jointly with more ‘discretionary’ approaches to fiscal sustainability analysis.

When Does External Knowledge Benefit Team Creativity? The Role of Internal Team Network Structure and Task Complexity

Creativity in teams is spurred by members’ access to diverse knowledge, often from interactions with external sources. However, not all teams that have the capabilities to acquire such external knowledge are equally creative. Integrating theories of absorptive capacity and creative synthesis in teams, we propose that teams’ external knowledge acquisition capabilities in securing a wide variety of knowledge resources need to be complemented by internal knowledge integration capabilities that facilitate balanced/equal participation of all team members in the creative problem-solving process. In turn, this combination enables effective information elaboration processes underlying the generation of truly creative team outcomes. We test these ideas in two field studies. First, in a sample of 81 research and development teams in three organizations in science and technology fields, we find that teams’ connections with a wide range of external parties—indicating their external knowledge acquisition capability—benefit their creativity, but only when the team’s internal team member problem-solving network structure—an indicator of the team’s knowledge integration capability—is less centralized (i.e., not controlled by one or few members). We further demonstrate that these effects are more salient when the team’s task is more complex. Replicating these findings in a second sample of 57 project teams in an energy manufacturing and services firm, we show that these effects are mediated by the team’s information elaboration processes. Theoretical and practical implications are discussed. Funding: This research was supported by grants received by C. Tang and her colleagues at UCAS, from the National Natural Science Foundation of China [Projects 71974178, 71932009], MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS, and the Fundamental Research Funds for the Central Universities [Project E2E40806X2]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.1661 .

Analysis of economic forecasting in the post-epidemic era: evidence from China

This paper presents a predictive analysis of the Chinese economy in the post-epidemic era. Five major public health emergencies historically similar to the COVID-19 epidemic are used as the control group, and a fuzzy mathematical model is applied to forecast and analyze China’s economy after the COVID-19 epidemic. The forecast results show that China’s overall economy will have recovered to the pre-epidemic level in about 1 year, with the fastest recovery in individual economic indicators, followed by government final consumption and imports, then CPI, fiscal revenue, exports and money supply, and the slowest recovery in employment. Finally, a combination of all the parties makes policies and recommendations for China’s economic and social development in the post-epidemic era.

COVID-19 PANDEMIC AND NIGERIA’S ECONOMIC GROWTH: A COMPARATIVE ANALYSIS OF PRE AND P0ST 2020

The outbreak of covid-19 pandemic affected the global economy, Nigeria inclusive. As a result, economic forecasts predicted a global recession for the year 2020 and possibly beyond. Hence, this study aims to examine the extent to which covid-19 pandemic has affected the growth of the Nigerian economy by doing a comparative analysis of pre and post 2020 covid-19. Monthly time series data was employed from the period January 2018 through October 2022. The model specified used real gross domestic product as the dependent variable while the regressors were agricultural output, industrial output and services sector output. Covid-19 was used as an economic growth interruption variable rather than a real quantitative variable and its effect in the model was evaluated using the Chow break-point test. The stated hypotheses were tested using the t-test, F-test and coefficient of multiple determination (R2). Other tests conducted were stationarity test and the ARDL Bounds test for co-integration. The Chow test revealed that structural break existed at the specified break point period of May 2020. Hence, we estimated the model parameters before and after the break-point period. The result revealed that the agricultural sector increased real GDP significantly before and after the month of May 2020. The industrial sector’s output was positive but not significant before the pandemic, it became significant after the pandemic. The services sector did not significantly increase real GDP before the pandemic, but afterwards, the sector’s contribution to GDP slipped to negative. The study concluded that the Nigerian economy has obviously been largely affected by the covid-19 pandemic with the services sector being highly affected. It was recommended that government should make deliberate favourable policies that will further enhance the agricultural and industrial sectors output and that the government should serve as encouragers in the services sector as this will make young entrepreneurs to invest more in myriads of services available in the services sector in this period of post covid-19 recovery.

Rooftop extraction method for high spatial resolution remote sensing image based on sparse representation

ABSTRACT In existing rooftop extraction methods, either too few or too many features in high spatial resolution remote-sensing image (HSRRSI) are used, reducing the rooftop extraction accuracy. Accordingly, a rooftop extraction method for HSRRSI based on sparse representation (SR) is proposed in this work. The optimal segmentation parameters are first determined by the ratio of mean difference to neighbours to standard deviation index method and maximum area method. Thereafter, the optimal feature subset of HSRRSI is constructed on the basis of the L 1 regularization SR model to remove redundant features. Finally, a random forest classifier is used to extract rooftops based on the optimal feature subset. Results show that the overall accuracy of the two study areas in Zhanggong District are 0.91776 and 0.88313, respectively. This study can help in effectively extracting rooftops from HSRRSI, which is of great significance in urban planning, population statistics and economic forecasting.

Macroeconomic forecasting in Poland: lessons from the external shocks

The aim of this paper is to analyse the forecast errors of Polish professional forecasters under theexternal shock of the COVID-19 crisis in 2020-based on the Parkiet competition. This analysis showsthat after the initial disruption related to the imposed lockdown in March and April, commercial economists were able to lower their forecasts errors of the industrial production and retail sales. On the other hand, a far worse performance has been seen in the case of the market variable; either the size of errors or the disagreement were elevated throughout the whole of 2020. Furthermore, long-term forecasts that were produced during the first year of the pandemic have been characterized with visible inconsistencies, i.e. forecasts of economic growth were similar when forecasters either assumed a strong increase in unemployment or when they did not. Economists made the biggest error in case of labour market forecasting. This phenomenon is likely related to the scarcity of information in the public statistics. Such problems are likely to repeat in the case of other external shocks, i.e. the forthcoming energy crisis.

Retracted: Neural network economic forecast method based on genetic algorithm

AbstractRetraction: [Zhong Li, Neural network economic forecast method based on genetic algorithm, IET Software 2023 (https://doi.org/10.1049/sfw2.12090)].The above article from IET Software, published online on 12 January 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

SME viability in the COVID-19 recovery.

Using survey data from a representative sample of Irish Small and Medium Enterprises (SMEs), we study how firms are likely to perform under macroeconomic forecasts of the pandemic recovery. The rate of financial distress among firms is expected to fall under baseline forecasts from a peak of 12% in 2020 to 7% by 2024. We find that those firms that struggle to recover by the end of our scenario window were mostly unprofitable or distressed prior to the pandemic. Beyond our baseline case, we further model three alternative recovery scenarios to study the effect of fiscal support tapering, a partial recovery due to structural change in sectoral demand, and a financing gap driven by credit risk retrenchment by lenders. Our findings highlight the continued importance of "bridging" liquidity finance provision to ensure the long-term solvency of viable firms. What proportion of SMEs are financially unviable in the post-pandemic economy? We study data from a representation sample of Irish SMEs and consider how they will perform under forecasts of the pandemic recovery. In our baseline scenario, we estimate that 7% of firms will remain distressed by 2024 and we find that most of these firms were unprofitable or already distressed prior to the pandemic. We look at a number of alternative macroeconomic scenarios, including where government supports are withdrawn, firms in some sectors do not fully recover, and where lenders lower the amount of money they are willing to extend to loan applicants. The impact of government support tapering alone is expected to be modest, and a partial recovery for some firms is not expected to raise aggregate distress by a sizeable amount. However, a sharp contraction in lending to otherwise viable firms leads to a significantly heightened distress rate. Policy measures that seek to support liquidity finance provision to viable firms will continue to have a role in the pandemic recovery.

Macroeconomic forecasting in times of crises

SummaryWe propose a parsimonious semiparametric method for macroeconomic forecasting. Based on ideas of clustering and similarity, we partition the series into blocks, search for the closest blocks to the latest block of observations, and forecast with the matched blocks. In a real‐time forecasting exercise, we show that our approach does especially well for labor market and other key macro variables. Our method outperforms parametric linear, nonlinear, time‐varying, and combination forecasts for the period 1999–2015 and particularly in the Great Recession. When adding financial spreads, our method delivers further improvements for labor market variables and capacity utilization.

The financial market's ability to forecast economic growth: information from sectoral movements

PurposeFinancial market holds superior information that can give insights into the future trajectory of economic growth. Further, identifying sectors that hold the key to future economic growth is important for all economies, but particularly relevant to emerging markets. However, unlike existing studies, the paper provides new insights into the forward-oriented nexus between financial markets and economic growth.Design/methodology/approachThis paper takes a forward-looking approach of using financial market information to predict future economic growth. The authors use ARDL modeling approach to predict economic growth using the information from stock market sectoral returns.FindingsThe authors find that sectoral stock returns significantly improve economic growth forecasts. However, the forecasting superiority is not uniform across sectors and horizons. Auto, consumers' spending, materials and realty sectors provide the most forecasting gains. In contrast, banking, capital goods and industrial sectors provide superior forecasts, but only at horizons beyond one year. The authors also find that the forecast superiority of sectors at longer horizons is inversely related to volatility.Research limitations/implicationsResearch highlights the need for sector-focused policy actions in driving economic growth. Further, the findings of the paper identify sectors that drive short-, medium- and long-term economic growth.Practical implicationsThere is a significant heterogeneity among different sectors and across horizons in predicting economic growth. Results suggest that targeted policy actions in sectors like materials, metals, oil and gas, and realty are key in driving economic growth. Further, policies geared toward the grassroots industries are at least as beneficial as the large-scale industries. Evidence also suggests the need for an active fiscal policy to address infrastructural bottlenecks in primary industries like basic materials and energy. Evidence nevertheless does not undermine the role of monetary policy actions.Originality/valueUnlike any paper till date, the innovation of the paper is that it takes an ARDL modeling approach to measure stock market sectoral returns' ability to forecast economic growth several months ahead in the future. Though the paper considers the Indian case, the innovation and contribution extents to the entire field of economic studies.

Retracted: Application of Improved LSTM Algorithm in Macroeconomic Forecasting.

[This retracts the article DOI: 10.1155/2021/4471044.].

Real-Time Uncertainty in Estimating Bias in Macroeconomic Forecasts

Real-Time Uncertainty in Estimating Bias in Macroeconomic Forecasts

Macroeconomic Forecast and Investment Decisions for China in 2023

Macroeconomic Forecast and Investment Decisions for China in 2023

Macroeconomic Forecasting in Uzbekistan: A Comprehensive Outlook

Uzbekistan, a landlocked Central Asian country, has undergone significant economic reforms and transformation since gaining independence in 1991. As the largest producer of gold in the region and the world's seventh-largest producer of cotton, Uzbekistan's economy relies heavily on its natural resources. This article delves into the current state of macroeconomic forecasting in Uzbekistan, using the latest statistics, models, and expert opinions to offer a comprehensive outlook on the country's future economic growth.

Modeling the AI-Driven Age of Abundance: The Essential Role of Human to AI Leverage Ratio (HAILR) in Macroeconomic Forecasting

Modeling the AI-Driven Age of Abundance: The Essential Role of Human to AI Leverage Ratio (HAILR) in Macroeconomic Forecasting

Embracing the Challenges and Opportunities of Financial Management in an AI-Dominated Business Environment

In this work, we describe an adaptive financial management strategy, tailor-made to meet the demands of, and capitalize on, an economy ruled by AI. The suggested solution combines three essential algorithms: LSTM-based machine learning for economic forecasting; SHAP-based explainable AI for openness in decision-making; and blockchain technology with proof-of-work (PoW) security. This LSTM-based method handles the sequential data often seen in time series analysis, which is crucial for effective financial forecasting. It is particularly effective at identifying complex interrelationships in financial time series data, providing a solid basis for reliable forecasting. By giving each feature in a prediction model an equal amount of weight, the SHAP algorithm improves the openness of decisions. The experimental results confirm the superiority of the suggested technique over the conventional methods. It uses dynamic Machine Learning models, in particular LSTM networks, to provide more precise economic forecasts than static models based on averages. Using SHAP, explainable AI solves the problem of interpretability that plagues conventional techniques, allowing for more open deliberation. The combination of Blockchain with PoW gives better security, overcoming the risks of centralized systems employed in previous approaches. The suggested adaptive strategy provides a comprehensive and robust framework for managing finances in a world controlled by artificial intelligence.

Economic Forecasts Using Many Noises

Economic Forecasts Using Many Noises

Variable Selection in Macroeconomic Forecasting with Many Predictors

In the data-rich environment, using many economic predictors to forecast a few key variables has become a new trend in econometrics. The commonly used approach is factor augment (FA) approach. This paper pursues another direction, variable selection (VS) approach, to handle high-dimensional predictors. VS is an active topic in statistics and computer science. However, it does not receive as much attention as FA in economics. This paper introduces several cutting-edge VS methods to economic forecasting, which includes: (1) classical greedy procedures; (2) l1 regularization; (3) false-discovery-rate control methods, (4) gradient descent with sparsification and (5) meta-heuristic algorithms. Comprehensive simulation studies are conducted to compare their variable selection accuracy and prediction performance under different scenarios. Among the reviewed methods, a meta-heuristic algorithm called sequential Monte Carlo algorithm performs the best. Surprisingly the classical forward selection is comparable to it and better than other more sophisticated algorithms. In addition, these VS methods are applied on economic forecasting and compared with the popular FA approach. It turns out for employment rate and CPI inflation, some VS methods can achieve considerable improvement over FA, and the selected predictors can be well explained by economic theories.

Analysis of the use of vector autoregressions in economic forecasting

Using vector autoregressions is a promising direction in short-term economic forecasting. They do not simply model the relationship between different factors, but also model the time-distributed relationship of these factors. Vector autoregressions are suitable for modeling complex dynamic economic multifactor processes. The complexity of the problem of estimating coefficients, which increases with the dimensionality of vectors, prevents the widespread use of autoregressions in practice. Vector autoregressions in complex-valued form with the same dimensionality as the modeled vector contain a much smaller number of coefficients. This facilitates the estimation of the coefficients of vector autoregressions. Some problems requiring further investigation arise when using vector autoregressions in complex form. Among them is the problem of selecting the best model. The information criteria used for this purpose limit the variety of vector autoregressions, reducing them to elementary models. The study was supported by the Russian Science Foundation grant No. 23-28-01213, https://rscf.ru/project/23-28-01213.

Econometrics of Machine Learning Methods in Economic Forecasting

Econometrics of Machine Learning Methods in Economic Forecasting