The effects of longevity and distortions on education and retirement

This article studies the impact of longevity and taxation on life-cycle decisions and long-run income. Individuals allocate optimally their total lifetime between education, working and retirement. They also decide at each moment how much to save or consume out of their income, and after entering the labor market how to divide their time between labor and leisure. The model incorporates experience-earnings profiles and the return-to-education function that follows evidence from the labor literature. In this setup, increases in longevity raises the investment in education - time in school - and retirement. The model is calibrated to the U.S. and is able to reproduce observed schooling levels and the increase in retirement, as the evidence shows. Simulations show that a country equal to the U.S. but with 20% smaller longevity will be 25% poorer. In this economy, labor taxes have a strong impact on the per capita income, as it decreases labor effort, time at school and retirement age, in addition to the general equilibrium impact on physical capital. We conclude that life-cycle effects are relevant in analyzing the aggregate outcome of taxation.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.