Financial conditions and the risks to economic growth in the United States since 1875

We explore the historical relationship between financial conditions and real economic growth for quarterly U.S. data from 1875 to 2017 with a flexible empirical copula modelling methodology. We compare specifications with both linear and non-linear dependence, and with both Gaussian and non-Gaussian marginal distributions. Our results indicate strong statistical support for models that are both non-Gaussian and nonlinear for our historical data, with considerable heterogeneity across sub-samples. We demonstrate that ignoring the contribution of financial conditions typically understates the conditional downside risks to economic growth in crises. For example, accounting for financial conditions more than doubles the probability of negative growth in the year following the 1929 stock market crash

Measuring output gap uncertainty

We propose a methodology for producing density forecasts for the output gap in real time using a large number of vector autoregessions in inflation and output gap measures. Density combination utilizes a linear mixture of experts framework to produce potentially non-Gaussian ensemble densities for the unobserved output gap. In our application, we show that data revisions alter substantially our probabilistic assessments of the output gap using a variety of output gap measures derived from univariate detrending filters. The resulting ensemble produces well-calibrated forecast densities for US inflation in real time, in contrast to those from simple univariate autoregressions which ignore the contribution of the output gap. Combining evidence from both linear trends and more flexible univariate detrending filters induces strong multi-modality in the predictive densities for the unobserved output gap. The peaks associated with these two detrending methodologies indicate output gaps of opposite sign for some observations, reflecting the pervasive nature of model uncertainty in our US data

U.K. World War I and interwar data for business cycle and growth analysis

This article contributes new time series for studying the U.K. economy during World War I and the interwar period. The time series are per capita hours worked and average tax rates of capital income, labor income, and consumption. Uninterrupted time series of these variables are provided for an annual sample that runs from 1913 to 1938. We highlight the usefulness of these time series with several empirical applications. We use per capita hours worked in a growth accounting exercise to measure the contributions of capital, labor, and productivity to output growth. The average tax rates are employed in a Bayesian model averaging experiment to reevaluate the Benjamin and Kochin (1979) regression.

The McKenna rule and U.K. World War I finance

The United Kingdom employed the McKenna rule to conduct fiscal policy during World War I (WWI) and the interwar period. Named for Reginald McKenna, Chancellor of the Exchequer (1915–16), the McKenna rule committed the United Kingdom to a path of debt retirement, which we show was forward-looking and smoothed in response to shocks to the real economy and tax rates. The McKenna rule was in the tradition of the “English method” of war finance because the United Kingdom taxed capital to finance WWI. Higher rates of capital taxation also paid for debt retirement during and subsequent to WWI. The United Kingdom was motivated to implement the McKenna rule because of a desire to achieve a balance between fairness and equity. However, the McKenna rule adversely affected the real economy, according to a permanent income model. WWI and interwar U.K. data support the prediction that real activity is lower in response to higher past debt retirement rates.

UK World War I and interwar data for business cycle and growth analysis

This article contributes new time series for studying the UK economy during World War I and the interwar period. The time series are per capita hours worked and average capital income, labor income, and consumption tax rates. Uninterrupted time series of these variables are provided for an annual sample that runs from 1913 to 1938. The authors highlight the usefulness of these time series with several empirical applications. The per capita hours worked data are used in a growth accounting exercise to measure the contributions of capital, labor, and productivity to output growth. The average tax rates are employed in a Bayesian model averaging experiment to reevaluate the Benjamin and Kochin (1979) regression.Business cycles ; Economic development ; Real-time data

A Real Time Tax Smoothing Based Fiscal Policy Rule

In this paper we consider the real-time implementation of a fiscal policy rule based on tax smoothing (Barro (1979) and Bohn (1998)). We show that the tax smoothing approach, augmented by fiscal habit considerations, provides a surprisingly accurate description of US budget surplus movements. In order to investigate the robustness of the policy implications of the rule, we construct a real-time US fiscal data set, complementing the data documented by Croushore and Stark (2001). For each variable we record the different vintages, reflecting the remeasurements that occur over time. We demonstrate that the easily constructed rule provided a useful benchmark for policy analysis that is robust to real-time remeasurements

The Cost Efficiency of UK Debt Management: A Recursive Modelling Approach

This paper presents an empirical analysis of the efficiency of the UK debt management authorities' (DMA) behaviour from a cost minimisation perspective over the period January 1985 to March 1995. During this period, the maturity structure of the government's bond portfolio was subject to frequent fine-tuning, aimed principally at lowering interest costs. The authors examine the efficiency of the DMA's behaviour from a cost minimisation perspective. Using a bi-variate version of the recursive modelling procedure applied to forecasting stock returns by Pesaran and Timmermann (1995, 2000), it is shown that bond returns are forecastable but that the predictive power of macroeconomic variables is time-dependent. The impact of adjusting the bond portfolio in response to these forecasts is simulated. The simulated average interest costs are lower than those resulting from the DMA's actual real-time behaviour. However, a substantial reduction in interest costs requires large monthly changes in the portfolio's maturity structure.Government debt management, Cost minimisation, Recursive modelling

Real-time inflation forecast densities from ensemble phillips curves

A popular macroeconomic forecasting strategy takes combinations across many models to hedge against model instabilities of unknown timing; see (among others) Stock andWatson (2004) and Clark and McCracken (2009). In this paper, we examine the effectiveness of recursive-weight and equal-weight combination strategies for density forecasting using a time-varying Phillips curve relationship between inflation and the output gap. The densities reflect the uncertainty across a large number of models using many statistical measures of the output gap, allowing for a single structural break of unknown timing. We use real-time data for the US, Australia, New Zealand and Norway. Our main finding is that the recursive-weight strategy performs well across the real-time data sets, consistently giving well-calibrated forecast densities. The equal-weight strategy generates poorly-calibrated forecast densities for the US and Australian samples. There is little difference between the two strategies for our New Zealand and Norwegian data. We also find that the ensemble modeling approach performs more consistently with real-time data than with revised data in all four countries

Parkinson’s Disease: Molecular Mechanisms and Treatments

Parkinson’s disease is a motor system disorder that is caused primarily by the loss of dopamine-producing brain cells. The most affected brain structure is the pars compacta of the substantia nigra. This area of the brain is essential to the control of voluntary movement, and so its impairment leads to symptoms such as tremors, rigidity, and impaired balance. The neuronal protein alpha-synuclein has been shown to be heavily involved in the pathogenesis of the disease at the cellular level. The currently available treatments for PD mainly target dopamine regulation, and there been no cure developed for the disease at present. New treatments must be explored by an evaluation and synthesis of the current research and should be adjusted for each patient individually