Graph and download economic data for JLN 3-Month Ahead Macroeconomic Uncertainty (JLNUM3M) from Jul 1960 to Jun 2025 about uncertainty, 3-month, and USA.

Graph and download economic data for JLN 1-Month Ahead Macroeconomic Uncertainty (JLNUM1M) from Jul 1960 to Jun 2025 about 1-month, uncertainty, and USA.

The dataset contains data used in the article Measuring Macroeconomic Uncertainty Using Internet Search Data: The Case of Poland. The Macroeconomic Uncertainty Index (MUI) is a new indicator developed to measure macroeconomic uncertainty in Poland. The data covers the period from 1.2004 to 3.2024. The MUI is a high-frequency, real-time measure of macroeconomic uncertainty based on actual behavior (internet searches). It leverages Google Trends (GT) data, capturing real-time public interest in economic topics through internet search activity.

Graph and download economic data for JLN 1-Year Ahead Macroeconomic Uncertainty (JLNUM12M) from Jul 1960 to Jun 2025 about 1-year, uncertainty, and USA.

Replication data for "The Macroeconomic Uncertainty Premium in the Corporate Bond Market" by Turan G. Bali, Avanidhar Subrahmanyam, and Quan Wen, published in the Journal of Financial and Quantitative Analysis Vol 56 Issue 5 August 2021, DOI https://doi.org/10.1017/S0022109020000538. Corrigendum published Vol 58 Issue 7 November 2023 https://doi.org/10.1017/S0022109023001102.

We generalize the Max Share approach to allow for simultaneous identification of a multiplicity of shocks in a Structural Vector Autoregression. Our machinery therefore overcomes the well-known drawbacks that individually identified shocks (i) tend to be correlated to each other or (ii) can be separated under orthogonalizations with weak economic ground. We show that identification corresponds to solving a nontrivial optimization problem. We provide conditions for non-emptiness of solutions and point-identification, and Bayesian algorithms for estimation and inference. We use the approach to study the effects of uncertainty and financial shocks, allowing for the possibility that the former responds contemporaneously to other shocks, distinguishing macroeconomic from financial uncertainty and credit supply shocks. Using U.S. data we find that financial uncertainty mimics a demand shock, while the interpretation of macro uncertainty is more mixed. Furthermore, variation in uncertainty partially represents the endogenous response of uncertainty to other shocks.

Graph and download economic data for Economic Policy Uncertainty Index for United States (USEPUINDXD) from 1985-01-01 to 2025-10-02 about academic data, uncertainty, indexes, and USA.

The data and programs replicate tables and figures from "Macroeconomic uncertainty and the COVID-19 pandemic: Measure and impacts on the Canadian economy", by Moran, Stevanovic and Touré. Please see the ReadMe file for additional details.

China’s export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China’s macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China’s financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China’s macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China’s and the United States’ economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model’s sampling results are good. China’s pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China’s pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market.

Research data associated with the manuscript: [1] Górajski, M., Kuchta, Z., 2022, Designing Optimal Macroeconomic Policy Rules under Parameter Uncertainty: A Stochastic Dominance Approach.

This work is supported by the National Science Centre in Poland under Grant No. 2017/26/D/HS4/00942.

It contains all user-defined MATLAB and R functions that implement our algorithms and replicate all results. We group them into seven folders: 1. main_data It performs the data preparation process.

1. main_estimation It estimates 25 versions of the Erceg, Henderson, and Levine (2000) small-scale DSGE model (EHL model). They differ by the monetary policy rule. We consider eight Taylor-type rules (see Table 1) and one nominal GDP targeting rule (H.2) (see Appendix H).

2. main_measuring_uncertainty It evaluates the MWL and OPFC distributions for all versions of the EHL model.

3. main_compare_losses It contains the novel EP Bayesian tests for the SDk relations from Section 4.2. We use these tests to compare the MWLs.

4. main_robust_simple_rules It replicates all Bayesian and min-max robust strategies.

5. main_simulations It collects all codes that perform the simulations of the EHL model with SDk-optimal and estimated policy rules.

6. main_performance_BayesEP_SDk_tests It assesses the performance of the EP Bayesian tests for SDk relations.

Abstract In this paper, we offer a Bayesian decision-theoretic approach to policy evaluation in rational expectation models. First, we show how to correctly assess and rank simple policy rules under the welfare loss minimization criterion in the presence of uncertainty about the model's structural parameters. We consider a Bayesian policymaker that assesses the effectiveness of policy actions, by comparing the distributions of welfare losses using stochastic dominance orderings. Second, we propose a new Bayesian testing procedure to verify higher and infinite degrees of stochastic dominance. Third, we demonstrate a potential use of the suggested approach to a dynamic stochastic general equilibrium model, estimated for the U.S. economy. We show that using stochastic dominance to rank simple monetary policy rules yields different rankings than well-established robust approaches. The contemporaneous monetary policy rule that reacts to inflation and the output gap, with an interest rate smoothing mechanism, minimizes the welfare loss for all decision-makers who admit infinite degree stochastic dominance preferences.

This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (hereafter L84-S61; Van Veen et al, 2001; Daron and Stainforth, 2013). To run these simulations, we used the E-forth ensemble generator (de Melo Viríssimo and Stainforth, in preparation), which is a MATLAB toolboox that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (de Melo Viríssimo and Stainforth, 2023).

These model outputs are presented and discussed in the Preprint "Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective". The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to Daron and Stainforth (2013) and de Melo Viríssimo et al. (2024).

All files uploaded were generated from simulations run by the lead author.

For specific information about each file uploaded, please refer to the README file. The details of each experiment are also presented in the supplementary materials of the preprint above. If you have any questions, please feel free to contact me.

References:

Van Veen et al. (2001): https://onlinelibrary.wiley.com/doi/abs/10.1034/j.1600-0870.2001.00241.x

Daron and Stainforth (2013): https://dx.doi.org/10.1088/1748-9326/8/3/034021

de Melo Viríssimo and Stainforth (2023): https://doi.org/10.5194/egusphere-egu23-14755

de Melo Viríssimo et al. (2024): https://doi.org/10.1063/5.0180870

de Melo Viríssimo and Stainforth (in preparation): to appear

This paper exploits a data rich environment to provide direct econometric estimates of time-varying macroeconomic uncertainty. Our estimates display significant independent variations from popular uncertainty proxies, suggesting that much of the variation in the proxies is not driven by uncertainty. Quantitatively important uncertainty episodes appear far more infrequently than indicated by popular uncertainty proxies, but when they do occur, they are larger, more persistent, and are more correlated with real activity. Our estimates provide a benchmark to evaluate theories for which uncertainty shocks play a role in business cycles. (JEL C53, D81, E32, G12, G35, L25)

This paper evaluates the effects of high-frequency uncertainty shocks on a set of low-frequency macroeconomic variables representative of the US economy. Rather than estimating models at the same common low frequency, we use recently developed econometric models, which allow us to deal with data of different sampling frequencies. We find that credit and labor market variables react the most to uncertainty shocks in that they exhibit a prolonged negative response to such shocks. When looking at detailed investment subcategories, our estimates suggest that the most irreversible investment projects are the most affected by uncertainty shocks. We also find that the responses of macroeconomic variables to uncertainty shocks are relatively similar across single-frequency and mixed-frequency data models, suggesting that the temporal aggregation bias is not acute in this context.

Replication Data for peer-reviewed article published in Journal of Applied Economics. Paper published online May 2, 2018

This article introduces the use of the sign restrictions methodology to identify uncertainty shocks. We apply our methodology to a class of vector autoregression models with stochastic volatility that allow volatility fluctuations to impact the conditional mean. We combine sign restrictions on the conditional mean and conditional second moment impulse responses to identify financial and macro uncertainty shocks. On U.S. data, we find stronger evidence that financial uncertainty shocks lead to a decline in real activity and an easing of the federal funds rate relative to macro uncertainty shocks. Supplementary materials for this article are available online.

The dataset comprises monthly time series for exchange rates among the United States, Japan, Canada, the United Kingdom, France, Germany, and Italy. Explanatory variables include the output; the 3-month interest rates, the CPI, economic policy uncertainty indices, financial risk indicators such as implied equity market volatility (VIX), and geopolitical risk indicator, the U.S. monetary policy uncertainty, the U.S. trade policy uncertainty, the U.S. monetary policy surprise, term spread, and dividend yields. Macroeconomic series are drawn from the Federal Reserve Bank of St. Louis (FRED), OECD Main Economic Indicators, IMF International Financial Statistics, and national statistical agencies. Economic policy uncertainty and geopolitical risk indices come from policyuncertainty.com and the Caldara–Iacoviello dataset. Quarterly GDP data are interpolated to monthly frequency using the Chow–Lin method to match the frequency of other series. Monthly GDP data are obtained by interpolation. The EPU data are smoothed by a local level model. The explanatory data are transformed by natural logarithms.

The sample spans January 1999 to March 2025, subject to data availability.

This data is mainly used to analyze the risk correlation among economic uncertainly,geopolitical risk and energy price,and can also be applied to the TVP-VAR model to analyze the correlation between different variables using the time-varying parameter model,which has great potential for reuse. The risk relationship between economic uncertainly.At the same time,since it is macroeconomic data,it does not involve any moral and ethical issues., , , # Economic uncertainty, geopolitical risk and U.S. energy price risk spillover: An empirical study based on the risk spillover model

Economic uncertainty, geopolitical risk and U.S. Energy Price risk spillover: An empirical study based on the Risk spillover model

Description of the data and file structure

The data set consists of the economic uncertainty index for the United States from 2009 to the end of 2023, the geopolitical risk index, and the time series data of major energy prices

Data potential This data is mainly used to analyze the risk correlation among economic uncertainty, geopolitical risk and energy price, and can also be applied to the TVP-VAR model to analyze the correlation between different variables using the time-varying parameter model, which has great potential for reuse. The risk relationship between economic uncertainty, geopolitical risk and energy price can be analyzed more accurately. At the same time, since it is macroeconomic data, it does not ...

The increase in macroeconomic uncertainty leads to inefficiency in the financial and banking sectors, resulting in a rise in Non-Performing Loans (NPLs). When macroeconomic uncertainty increases, financial institutions experience higher inefficiencies, reflected in increased NPLs, and with proper management solutions, the economy can move toward sustainability. This research analyzes the effect of severe macroeconomic shocks on the NPLs of the Iranian banking system using the Time-Varying Parameter Vector Autoregressions (TVP-VAR) model and a Panel Data Model. The study utilizes data from 2007 to 2021 on key macroeconomic indicators such as economic growth rate, inflation rate, interest rate, unemployment rate, and exchange rate, along with the ratio of Non-Current Claims to Total Facilities as an index of credit risk and the ratio of loans to total assets as a risk-taking index for banks. Our innovation lies in analyzing these variables dynamically, accounting for their correlation and mutual impact. The findings indicate that a 1% increase in inflation leads to a 0.0061% increase in NPLs, while a 1% rise in the unemployment rate results in a 0.0182% increase in NPLs. Conversely, a 1% increase in GDP growth reduces NPLs by 0.0036%. Furthermore, shocks to interest rates, exchange rates, and economic growth increase credit risk, with a 1% interest rate shock raising the default rate from 7.8% to 9.2% over time.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Trade (EMVMACROTRADE) from Jan 1985 to Aug 2025 about volatility, uncertainty, equity, trade, and USA.

This study examines whether innovations to macroeconomic uncertainty affect labor market outcomes for men and women differently. Three measures of uncertainty are utilized in turn within a vector autoregression (VAR) and the dynamic responses of various labor market ratios to uncertainty shocks are analyzed. These labor market ratios are the ratio of men’s to women’s median earnings, the ratio of men’s to women’s labor force participation, and the ratio of men’s to women’s unemployment. Findings reveal that increases in macroeconomic uncertainty lead to recessionary outcomes, with the unemployment rate for men rising higher than that for women. There is evidence that men’s labor force participation declines more than women’s during such shocks, but the ratio of real earnings is largely unchanged. When uncertainty is proxied by stock market volatility, most of the increase in men’s unemployment relative to women’s is due to uncertainty’s adverse impact on real GDP growth. When uncertainty is related to economic policy uncertainty, however, it is uncertainty itself that drives the higher relative unemployment rate for men. Halting the estimation at 2019Q4 and generating out of sample forecasts show that the COVID-19 recession reversed this trend, as women were more disproportionately affected than men.

The increase in macroeconomic uncertainty leads to inefficiency in the financial and banking sectors, resulting in a rise in Non-Performing Loans (NPLs). When macroeconomic uncertainty increases, financial institutions experience higher inefficiencies, reflected in increased NPLs, and with proper management solutions, the economy can move toward sustainability. This research analyzes the effect of severe macroeconomic shocks on the NPLs of the Iranian banking system using the Time-Varying Parameter Vector Autoregressions (TVP-VAR) model and a Panel Data Model. The study utilizes data from 2007 to 2021 on key macroeconomic indicators such as economic growth rate, inflation rate, interest rate, unemployment rate, and exchange rate, along with the ratio of Non-Current Claims to Total Facilities as an index of credit risk and the ratio of loans to total assets as a risk-taking index for banks. Our innovation lies in analyzing these variables dynamically, accounting for their correlation and mutual impact. The findings indicate that a 1% increase in inflation leads to a 0.0061% increase in NPLs, while a 1% rise in the unemployment rate results in a 0.0182% increase in NPLs. Conversely, a 1% increase in GDP growth reduces NPLs by 0.0036%. Furthermore, shocks to interest rates, exchange rates, and economic growth increase credit risk, with a 1% interest rate shock raising the default rate from 7.8% to 9.2% over time.