Bayesian Reconciliation of Return Predictability.

This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The bivariate VAR system comprises asset returns and a further prediction variable, such as the dividend-price ratio, and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004. "Predictive Regressions: A Reduced-Bias Estimation Method." Journal of Financial and Quantitative Analysis 39: 813-41). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to a system comprising annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021, and the logarithmic dividend-price ratio. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed. Then, instead of the dividend-price ratio, some prediction variables proposed in Welch and Goyal (2008. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction." Review of Financial Studies 21: 1455-508) are used. Also with these prediction variables, only weak evidence for return predictability is supported by Bayesian testing. These results are corroborated with an out-of-sample forecasting analysis.

Financial and economic uncertainties and their effects on the economy.

We estimate new indices measuring financial and economic uncertainty in the euro area, Germany, France, the United Kingdom and Austria, following the approach of Jurado et al. (Am Econ Rev 105:1177-1216, 2015), which measures uncertainty by the degree of predictability. We perform an impulse response analysis in a vector error correction framework, where we focus on the impact of both local and global uncertainty shocks on industrial production, employment and the stock market. We find that global financial and economic uncertainties have significant negative effects on local industrial production, employment, and the stock market, while we find hardly any influence of local uncertainty on these variables. In addition we perform a forecasting analysis, where we assess the merits of uncertainty indicators for forecasting industrial production, employment and the stock market, using different performance measures. The results suggest that financial uncertainty significantly improves the forecasts of the stock market in terms of profit-based measures, while economic uncertainty gives, in general, more insight when forecasting macroeconomic variables.

Socioeconomic factors and suicide: an analysis of 18 industrialized countries for the years 1983 through 2007.

OBJECTIVE: To evaluate the association between socioeconomic factors and suicide rates. METHODS: Analysis of time series of suicide rates, gross domestic product, unemployment rates, labor force participation, and divorce rates of 18 countries are analyzed by the application of panel-vector error correction models. Main outcome measures are the association between the socioeconomic factors and suicide rates. RESULTS: Decreasing economic growth and increasing divorce rates are significantly associated with increasing suicide rates in men. For women, increasing economic growth, increasing unemployment, and increasing divorce rates are significantly associated with increasing suicides. Increasing female labor force participation is associated with decreasing suicides. CONCLUSION: Socioeconomic factors are associated with suicide rates. However, this relationship differs by sex. The current results provide a strong argument that suicide prevention strategies must include the monitoring of socioeconomic development.

Economic growth and the incidence of occupational injuries in Austria.

OBJECTIVES: The aim of this paper was to analyze the impact of economic growth measured by real gross domestic product (GDP) on the incidence of occupational injuries in Austria. METHODS: The relationship between GDP and the occupational injury rate of the wage-earning population between 1955 and 2004 was analyzed using an error correction model. The sample size increased from 1.568,371 persons in 1955 to 2.656,952 in 2004. Occupational injuries were divided into fatal and non-fatal injuries. RESULTS: Occupational injuries (fatal and non-fatal) decreased from 8.59% to 4.08%: non-fatal injuries decreased from 8.56% to 4.07%; fatal injuries decreased from 0.03% to 0.01%. Austrian GDP increased from EUR 37.7 billion to EUR 202.8 billion (base year 1995). Statistical analysis clearly shows that a growing economy is associated with declining injury rates (fatal and non-fatal). Two mechanisms are discussed. Firstly, rising GDP is accompanied by greater investment in safer technologies and occupational safety measures. Secondly, booming economies are associated with a reduced risk of unemployment, which is already known to be a risk factor for occupational injuries. CONCLUSIONS: Economic development appears to have an impact on the incidence of occupational injuries in Austria. Health policy should emphasize the necessity for safety at work particularly in phases of economic slowdown.