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Innovations to consumer confidence convey incremental information about economic activity far into the future. Does this reflect a causal effect of animal spirits on economic activity, or news about exogenous future productivity received by consumers? Using indirect inference, we study the impulse responses to confidence innovations in conjunction with an appropriately augmented New Keynesian model. While news, animal spirits, and pure noise all contribute to confidence innovations, the relationship between confidence and subsequent activity is almost entirely reflective of the news component. Confidence innovations are well characterized as noisy measures of changes in expected productivity growth over a relatively long horizon.

Despite the widespread attention given to surveys of consumer confidence, the mechanisms by which household attitudes influence the real economy are less well understood. This paper begins with an overview of how consumer confidence is measured and reported. It then evaluates the relationship between consumer attitudes and the real economy. The evidence suggests that popular survey measures contain some information about future aggregate consumer expenditure growth. However, much of that information is found in other economic and financial indicators, and the independent information provided by consumer confidence predicts a relatively modest amount of additional variation in future consumer spending.

This paper employs the mixed-frequency Granger causality test, reverse unconstrained mixed-frequency data sampling models, and Chinese data from January 2006 to June 2024 to test the nexus between consumer confidence and the macroeconomy. The results show that changes in the real estate market, GDP, and urban unemployment rate are Granger causes of consumer confidence. In reverse, consumer confidence is a Granger cause of the CPI. Second, GDP and the real estate market (CPI and urban unemployment rate) have a significant positive (negative) impact on consumer confidence, while the conditions of industrial production, interest rate, and stock market do not. Third, the “animal spirits” extracted from consumer confidence cannot lead to noticeable fluctuations in China’s macroeconomy. This suggests that the “animal spirits” will not dominate economic growth, even though they affect the macroeconomy slightly and inevitably. The results are robust after replacing the dependent variable and considering the influence of the global financial crisis and the COVID-19 pandemic.

This study aims to introduce a novel approach for predicting China’s consumer confidence index (CCI), a key economic indicator that reflects consumers’ confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.

We explore the time-series relationship between investor sentiment and the small-stock premium using consumer confidence as a measure of investor optimism. We estimate the components of consumer confidence related to economic fundamentals and investor sentiment. After controlling for the time variation of beta, we study the time-series variation of the pricing error with sentiment. Over the last 25 years, investor sentiment measured using consumer confidence forecasts the returns of small stocks and stocks with low institutional ownership in a manner consistent with the predictions of models based on noise-trader sentiment. Sentiment does not appear to forecast time-series variation in the value and momentum premiums.

Exogenous uncertainty shocks may have different effects on domestic and foreign consumption under different consumer confidence regimes. In this paper, we specify a threshold vector autoregressive (TVAR) model with different consumer confidence regimes to study the response of endogenous macroeconomic variables to exogenous shocks. The evidence shows that in China, compared to high consumer confidence, low consumer confidence dampens consumption both at home and abroad. However, low consumer confidence benefits exports, the source of foreign consumption. A forecast error variance decomposition analysis further confirms the difference in the effects under different consumer confidence regimes. A comparative analysis shows that consumer confidence is much more influential in the US than in China. Our findings differ from those of earlier works, as we introduce stochastic uncertainty to both the mean and heteroscedasticity and apply counterfactual analysis to show the hazard of ignoring stochastic uncertainty in the traditional threshold vector autoregression. Finally, from the ex ante and ex post perspectives, we provide managerial implications for the authorities to tackle economic issues based on different consumer confidence regimes.

The global financial crisis wreaked havoc on the European economy and dented consumer confidence. This paper exploits a panel dataset of 18 EU countries over the period 2001–2014 to examine whether this decrease in consumer confidence affected household saving behaviors and if so, which specific sub-indicators of consumer sentiment played the most significant role. To tackle the issue of potential endogeneity between household saving rates and consumer confidence, we use an instrumental variable approach. Our results suggest that confidence in household financial situations has a substantially larger effect on household saving than confidence in the general economic situation. Moreover, we find that the impact of consumer confidence on household saving has increased after the crisis, potentially due to a threshold effect.

European Consumer Confidence Indicator (CCI) is conceptualized as a measure of prevailing consumer sentiment and a coincident indicator of private consumption. At the beginning of 2019, the European Commission (EC) changed the CCI’s methodology after more than 18 years of use, opening room for an evaluation. We construct an extensive set of more than 86 million alternatives to European CCI and provide detailed performance analysis focusing on correlations, residuals’ descriptive diagnostics, and success in tracking consumption’s direction of change. We propose inspecting the contribution of all survey questions (both 12 monthly and 3 quarterly), alternative monthly to quarterly transformations, alternative approaches to standardization, and allowing survey balances to enter the computation formula both with a positive and a negative sign. Almost all results imply that EC’s methodological change to new official CCI relies on stronger theoretical foundations and enhances the predictive power of CCI. The correlations with private consumption are higher than before while the success rate in tracking the direction of change is similar as before. However, the new CCI can be even more precise in capturing the direction of change up to 20–30%. Correlation values imply a possible improvement of up to 9% from the new CCI and suggest guidelines for further improvements. Expected spending on durable goods is the most relevant survey question for constructing CCI.

The COVID-19 pandemic and induced economic and social constraints have significantly impacted the confidence of both consumers and businesses. Despite that, comprehensive studies of the impact of the COVID-19 pandemic on the consumer and business sentiment are still lacking. Thus, in our research we aim to identify consumer and business confidence indicators' reaction to the spread of the COVID-19 pandemic in the Eurozone, the United States, and China. For this purpose, we used the method of correlation-regression analysis. We chose the consumer-confidence index, manufacturing purchasing manager's index, and services purchasing manager's index as dependent variables; and the number of confirmed cases of COVID-19, the number of deaths caused by COVID-19, and the mortality rate of COVID-19 infections as independent variables. The results showed a relatively rapid and robust effect of COVID-19 in the short period, but longer-term results depended on the region and were not so unambiguous: in the case of the Eurozone, the spread of COVID-19 pandemic did not affect the consumer-confidence index (CCI) or, in the cases of the United States and China, affected this index negatively; the purchasing managers' index (PMI) in the services sector was significantly negatively affected by the mortality risk of COVID-19 infection; and the impact on the purchasing managers' index (PMI) in the manufacturing industry appeared to be mixed.

Most single-factor and multifactor asset pricing models constitute special cases of the consumption-based asset pricing theory, in which investors' marginal utility is the key determinant of asset prices. However, in recent years, production-based asset pricing models have been extraordinarily successful in correctly pricing a wide range of anomaly portfolios that are typically mispriced in previous research. In parallel, research on conditioning information has contributed to significantly improve the performance of classic consumption-based asset pricing models. On this basis, in this paper we conduct an in-depth research on the performance of consumption and production-based asset pricing models on the Tokyo Stock Exchange, for the period from 1992 to 2018, in order to test to what extent consumer confidence helps consumption models to correctly capture shifts in the investment opportunity set of investors. To overcome the constraints imposed by the periodicity of macroeconomic data, we use a factor-mimicking portfolio approach that allows us to test the performance of the models into consideration at different frequencies. Our results suggest that the consumer confidence index for Japan helps consumption-based asset pricing models outperform production-based models for different anomaly portfolios. Conversely, in those cases where consumption models perform worse, the production models also perform poorly. These results help to partially reconcile the results provided by the consumption and production models, and constitute a step forward for the purpose of identifying the fundamental risk factors that drive asset prices.