The Macroeconomic Effects of Shocks to Large Banks’ Capital

We propose a simple approach to quantifying the macroeconomic effects of shocks to large banks’ leverage. We first estimate a standard dynamic model of leverage targeting at the bank level and use it to derive an aggregate measure of the economic capital buffer of large US bank holding corporations. We then evaluate the response of key macro variables to a shock to this aggregate bank capital buffer using standard monetary VAR models. We find that shocks to the capital of large US banks explain a substantial share of the variance of credit to firms and real activity.     

Mixed‐frequency models with moving‐average components

Temporal aggregation in general introduces a moving‐average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed‐frequency (MF) model. The MA component is generally neglected, likely to preserve the possibility of ordinary least squares estimation, but the consequences have never been properly studied in the MF context. In this paper we show, analytically, in Monte Carlo simulations and in a forecasting application on US macroeconomic variables, the relevance of considering the MA component in MF mixed‐data sampling (MIDAS) and unrestricted MIDAS models (MIDAS–autoregressive moving average (ARMA) and UMIDAS‐ARMA). Specifically, the simulation results indicate that the short‐term forecasting performance of MIDAS‐ARMA and UMIDAS‐ARMA are better than that of, respectively, MIDAS and UMIDAS. The empirical applications on nowcasting US gross domestic product (GDP) growth, investment growth, and GDP deflator inflation confirm this ranking. Moreover, in both simulation and empirical results, MIDAS‐ARMA is better than UMIDAS‐ARMA.     

Macroeconomic forecast accuracy in a data‐rich environment

The performance of six classes of models in forecasting different types of economic series is evaluated in an extensive pseudo out‐of‐sample exercise. One of these forecasting models, regularized data‐rich model averaging (RDRMA), is new in the literature. The findings can be summarized in four points. First, RDRMA is difficult to beat in general and generates the best forecasts for real variables. This performance is attributed to the combination of regularization and model averaging, and it confirms that a smart handling of large data sets can lead to substantial improvements over univariate approaches. Second, the ARMA(1,1) model emerges as the best to forecast inflation changes in the short run, while RDRMA dominates at longer horizons. Third, the returns on the S&P 500 index are predictable by RDRMA at short horizons. Finally, the forecast accuracy and the optimal structure of the forecasting equations are quite unstable over time.     

The cost-effectiveness and monetary benefits of dabigatran in the prevention of arterial thromboembolism for patients with non-valvular atrial fibrillation in the Netherlands

Background: Atrial fibrillation (AF) causes a significant health and economic burden to the Dutch society. Dabigatran was proven to have at least similar efficacy and a similar or better safety profile when compared to vitamin K antagonists (VKAs) in preventing arterial thromboembolism in patients with AF.

Objective: To evaluate the cost-effectiveness and monetary benefit of dabigatran vs VKAs in Dutch patients with non-valvular AF. Value-based pricing considerations and corresponding negotiations on dabigatran will be explicitly considered.

Methods: The base case economic analysis was conducted from the societal perspective. Health effects and costs were analysed using a Markov model. The main model inputs were derived from the RE-LY trial and Dutch observational data. Univariate, probabilistic sensitivity, and various scenario analyses were performed.

Results: Dabigatran was cost saving compared to VKAs. A total of 4,552 QALYs were gained, and €13,892,288 was saved in a cohort of 10,000?AF patients. The economic value of dabigatran was strongly related to the costs of VKA control that are averted. Notably, dabigatran was cost saving compared to VKAs if annual costs of VKA control exceeded €159 per person, or dabigatran costs were below €2.81 per day.

Conclusion: Dabigatran was cost saving compared to VKAs for the prevention of atrial thromboembolism in patients with non-valvular AF in the Netherlands. This result appeared robust in the sensitivity analysis. Furthermore, volume based reduction of the price in the Netherlands will further increase the monetary benefits of dabigatran.     

Debt Spreads Between GSEs and Other Corporations

Policy analysis of the housing GSEs—Freddie Mac, Fannie Mae, and the Federal Home Loan Bank System—has largely centered on a comparison of their cost advantages relative to the benefits they provide to consumers and the market. Researchers generally treat their lower funding costs as the largest component of their cost advantage and measure it by a comparison of spreads between yields on non-GSE securities and GSE securities. This paper provides the first econometric analysis of such spreads. Special components of this research are separate analysis of debentures and medium-term notes, a comparison with all financial firms and a banking subsample, and the introduction of liquidity proxies. Comparing Freddie Mac and Fannie Mae debt with non-GSE debt rated AA– gives an estimated range of 27 to 30 basis points without the inclusion of the liquidity proxies, and a range of 22 to 27 basis points with their inclusion, over 1995–2000.