Measuring Macroeconomic Uncertainty Using Internet Search Data: The Case of Poland

After 2008, the barriers to cross-border flows of production factors such as commodities, capital, and labor have become increasingly apparent. The new round of liberalism has quietly risen, triggering a wave of counter-globalization characterized by anti-free trade and anti-integration”. Some developed countries have attempted to go beyond the constraints of the structural adjustment of the global economy and embark on the so-called priority of interest and self-development”. This, to a certain extent, weakened the synchronization of the world economic cycle and objectively exacerbated the synthetic fallacy” in the global economic development. In the absence of effective international economic rules and regulations, this synthetic fallacy” may cause frictions in trade, finance, and other areas to increase, thereby accelerating current account imbalances.Based on the above facts, this paper regards current account imbalances as the result of the internal and external factors, and combines the changes in the economic globalization process with macroeconomic uncertainty to explore whether the synchronization between China and the world economic cycle has changed. Is there any uncertainty in China’s macro-economy, and how does it affect current account imbalances, accelerating or smoothing? If the synchronization between China and the world economic cycle is reduced and macroeconomic uncertainty is raised, will it accelerate current account imbalances? What is the impact? Firstly, the method of real-time measurement of macroeconomic uncertainty using large-scale mixed macroeconomic data sets is proposed, and the macroeconomic uncertainty index is constructed. It avoids the possibility of directly using the substitution variables such as financial market volatility in the past literature to reflect the possible deviation of macroeconomic uncertainty. Then we use the Markov framework to establish a macroeconomic cycle synchronization index. Finally, the vector autoregressive model with Bayesian variable selection （VAR-BVS） weakens the endogeneity problem caused by missing or mistaken variables, and analyzes the dynamic relationship between the three before and after the global financial crisis. The study examines the possible links between the three.The study finds that: The relationship between the two is similar to a U-shape curve with the synchronization of the world economic cycle as the horizontal axis and current account imbalances growth rate as the vertical axis. Whether before or after the crisis, macroeconomic uncertainty plays an accelerator” role in current account imbalances. In the long term, the increase in macroeconomic uncertainty has an accelerating effect on current account imbalances. In the short term, in addition to the impact of the domestic economic environment, the decline in the synchronization of the world economic cycle will play an important role in accelerating current account imbalances. The reason for the synchronization between China and the world economic cycle, and macroeconomic uncertainty affecting current account imbalances may be due to the international division of labor, factor mobility, and preventive savings motivation. There is structural heterogeneity in the relationship between domestic and foreign economic environment and current account imbalances, which is different from the relationship between trade in goods and trade in services. The findings show that a stable domestic economic environment and a cooperative international economic environment are important conditions to improve current account imbalances.

This paper develops a macroeconomic uncertainty index based on the multistage procedure that combines maximum likelihood and Bayesian estimation methods proposed by Jurado et al. (Am Econ Rev 105(3):1177–1216, 2015). Our approach streamlines the computation of the macroeconomic uncertainty index by specifying a state-space model estimated by maximum likelihood that allows us to obtain in one step the parameters of the model, the dynamic factors, and the forecast errors of the macroeconomic variables used to construct the index. Moreover, we estimate stochastic volatility models on the forecast errors also by maximum likelihood using a density filter that proves to be faster than a Bayesian estimation. After showing that our methodology produces reasonable results for the USA, we apply it to compute a macroeconomic uncertainty index for Ecuador, becoming the first index of this kind for a small developing or middle-income country. The results show that the Ecuadorian economy is more volatile and less predictable during recessions. We also provide evidence that macroeconomic uncertainty is detrimental to economic activity, finding that the responses of non-oil output, employment in the formal sector, and consumer prices to macroeconomic uncertainty shocks are sizable and persistent.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00181-021-02069-5.

Macroeconomic uncertainty shocks and households’ consumption choice

A stock market volatility index is a widely used proxy of uncertainty in the macroeconomy, where a rise in the index dampens real economic activity. By contrast, the macroeconomic uncertainty index proposed by Jurado et al. (2015) measures the predictability of a wide range of macroeconomic indicators and thus, is a comprehensive indicator of macroeconomy-wide uncertainty. This study empirically investigates the nonlinear link between financial volatility and real economic activity based on the level of the macroeconomic uncertainty index. Employing data from the United States and Japan, the empirical analysis suggests that an increase in financial volatility lowers industrial production and business-fixed investment more persistently when macroeconomic uncertainty is higher.

On economic uncertainty, stock market predictability and nonlinear spillover effects

The uncertainty in the financial market, whether the US—China trade war will slow down the global economy or not, Federal Reserve Board (FRB) policy to increase the interest rates, or other similar macroeconomic events can have a crucial impact on the purchase or sale of financial assets. In this study, we aim to build a model for measuring the macroeconomic uncertainty based on the news text. Further, we proposed an extended topic model that uses not only news text data but also numeric data as a supervised signal for each news article. Subsequently, we used our proposed model to construct macroeconomic uncertainty indices. All these indices were similar to those observed in the historical macroeconomic events. The correlation was higher between the volatility of the market and uncertainty indices with larger expected supervised signal compared to uncertainty indices with the smaller expected supervised signal. We also applied the impulse response function to analyze the impact of the uncertainty indices on financial markets.

The uncertainty in the financial market, whether US-China trade war will slow down global economy or not, Federal Reserve Board (FRB) policy to increase the interest rates, or other similar macroeconomic events can have a crucial impact on the purchase or sale of financial assets. In this study, we aim to build a model for measuring the macroeconomic uncertainty based on the news text. Further, we proposed an extended topic model which uses not only news text data but also numeric data as a supervised signal for each news article. Subsequently, we used our proposed model to construct four macroeconomic uncertainty indices. All these indices were similar to those observed in the historical macroeconomic events, and the correlation was higher with the volatility of the market index with respect to the uncertainty index. We also applied the impulse response function to analyze the impact of the uncertainty indices on financial markets.

ABSTRACTIn this article, we evaluate the causal relationship between macroeconomic uncertainty indices, inflation and growth rate for 17 Eurozone countries on a county-level examination. In performing a series of linear and nonlinear causality tests, we find little evidence of a causal relationship between uncertainty and macroeconomic variables. Thus, macroeconomic analysis based on uncertainty indices should be treated with caution.

In this paper, we evaluate the causal relationship between macroeconomic uncertainty indices, inflation and growth rate for 17 Eurozone countries on a county level examination. In performing a series of linear and non-linear causality tests we find little evidence of a causal relationship between uncertainty and macroeconomic variables. Thus, macroeconomic analysis based on uncertainty indices should be treated with caution.

The purpose of this study is to estimates the size of the shadow economy for 80 countries from nine regions spanning the period 1975-2012 based on Tanzi-type currency demand approach (CDA). This study contributes to the literature in three distinct ways. First, we augment CDA regression with a macroeconomic uncertainty index (MUI). Second, the construction of the uncertainty index is based on the dynamic factor model (DFM). Third, the pooled mean group (PMG) estimator allows in capturing the heterogeneity across countries in the short-run dynamics but imposing restrictions in the long-run parameters. The results confirm the existence of the longrun equilibrium relationship among the variables examined. All coefficients show expected signs along with statistical significance. More importantly, the macroeconomic uncertainty index variable show positive relationship, suggesting that public tend to hold more currency in an uncertain macroeconomic environment. In addition, we observe that developing regions (ranging from 19.9% to 37.3%) exhibit relatively large size of the shadow economy. On the contrary, developed regions have a considerable smaller estimate (ranging from 13.7% to 19.0%) of the size of shadow economy. On average, the world estimate of the shadow economy as a percentage of GDP is about 23.1%. Keywords: Shadow Economy; Currency Demand; Macroeconomic Uncertainty; Pooled Mean Group.

The uncertainty in the financial market, whether US—China trade war will slow down global economy or not, Federal Reserve Board (FRB) policy to increase the interest rates, or other similar macroeconomic events can have a crucial impact on the purchase or sale of financial assets.In this study, we aim to build a model for measuring the macroeconomic uncertainty based on the news text. Further, we proposed an extended topic model which uses not only news text data but also numeric data as a supervised signal for each news article. Subsequently, we used our proposed model to construct four macroeconomic uncertainty indices. All these indices were similar to those observed in the historical macroeconomic events, and the correlation was higher with the volatility of the market index with respect to the uncertainty index.KeywordsUncertaintyEconomic policyTopic modelText mining

This study uses the Diebold-Yilmaz (2012) and Baruník-Křehlík (2018) frameworks to examine time-varying volatility spillovers among five key rare earth minerals, cryptocurrencies, and macroeconomic uncertainty indexes. Our results reveal considerable cross-market spillovers (31.75% of total variance), which are short-term (29.97%, 1–4 days) in nature and over 50% during the COVID-19 pandemic. Ethereum (70.99%) and bitcoin (66.58%) emerge as predominant short-term transmitters, whereas dysprosium (31.45%) has a more long-lasting, cross-horizon effect. Macroeconomic uncertainty indices act as net recipients. This increased short-run spillover requires forward-looking macroeconomic policy and integrated risk management directed at cryptocurrency and strategic rare earths for financial stability.

A fundamental component of transit planning is understanding passenger travel patterns. However, traditional data sources used to study transit travel have some noteworthy drawbacks. For example, manual collection of travel surveys can be expensive, and data sets from automated fare collection systems often include only one transit system and do not capture multimodal trips (e.g., access and egress mode). New data sources from smartphone applications offer the opportunity to study transit travel patterns across multiple metropolitan regions and transit operators at little to no cost. Moreover, some smartphone applications integrate other shared mobility services, such as bikesharing, carsharing, and ride-hailing, which can provide a multimodal perspective not easily captured in traditional data sets. The objective of this research was to take a first look at an emerging data source: back-end data from user interactions with a smartphone application. The specific data set used in this paper was from a widely used smartphone application called Transit that provides real-time information about public transit and shared mobility services. Visualizations of individuals’ interactions with the Transit app were created to demonstrate three unique aspects of this data set: the ability to capture multicity transit travel, the ability to capture multiagency transit travel, and the ability to capture multimodal travel, such as the use of bikeshare to access transit. This data set was then qualitatively compared with traditional transit data sources, including travel surveys and automated fare collection data. The findings suggest that the data set has potential advantages over traditional data sources and could help transit planners better understand how passengers travel.

Social landlords own more than one third of the housing stock in the Netherlands. Especially in the decades after World War II, social landlords were both strongly controlled and strongly subsidised by the government. In the 1990Is, the government relaxed her hold on the social landlords but also ended all subsidisation. It was assumed that the social housing sector as a whole could make enough money by exploiting and partly selling the social housing stock to achieve all housing policy objectives. Those social landlords that are not able to make ends meet should be assisted by other social landlords. This paper describes a risk analysis model that we developed to determine the financial risks for the Dutch social housing sector as a whole. The development of the financial position of the social housing sector depends on a large number of uncertain factors. These factors can mainly be grouped in political uncertainties, macro economic uncertainties and housing market uncertainties. Our model uses Monte Carlo simulation to assess the risk caused by macro economic and housing market uncertainties. It can also be used to determine the consequences of different government policies. The results of the model show that with the current government policy, the social housing sector as a whole is not at risk. However, there are large differences between different regions. The most important uncertainty is in the house price development.

The spillover effects of U.S. uncertainties on the systemic tail risk of Chinese enterprises