First-order Vector Autoregression Research Articles

Life expectancy and lifespan disparity forecasting: a long short-term memory approach

After the World War II, developed countries experienced a constant decline in mortality. As a result, life expectancy has never stopped increasing, despite an evident deceleration in developed countries, e.g. England, USA and Denmark. In this paper, we propose a new approach for forecasting life expectancy and lifespan disparity based on the recurrent neural networks with a long short-term memory. This type of neural network leads to predicting future values of longevity indexes while maintaining the significant influence of the past trend, but at the same time adequately reproducing the recent trend into forecasting. The model was applied to five countries for two fitting periods focusing on the forecasting life expectancy and lifespan disparity, both independently and simultaneously at birth and age 65. The results were compared to the projections obtained by four different models, namely, the Double Gap, ARIMA, CoDa and Lee-Carter in the independent case and the first-order Vector Autoregression model in the simultaneous case. Our predictions seem to be coherent with historical trends and biologically reasonable, providing a more accurate portrait of the future life expectancy and lifespan disparity.

УМОВИ ТОРГІВЛІ ТА ІНДУСТРІАЛІЗАЦІЯ: РЕАЛІЇ В УКРАЇНІ

he paper explores the impact of terms of trade on the industrialization and economic growth in Ukraine due to significant vulnerability of national economy to foreign economic shocks, its openness and mainly commodity structure of exports. In this research we have chosen manufacturing value added as percent in GDP to identify periods of industrialization, as its growth corresponds to periods of accelerated industrial development and vice versa. Also we considered GDP per capita as indicator of national economy’s performance. The changes in terms of trade were investigated based on the analysis of terms of trade adjustments, which are determined by the ability to import goods and services minus exports at constant prices. As an empirical research tool vector autoregressive models have been chosen to explore the relationship between endogenous and exogenous variables in dynamics. Thus, the endogenous variables in the model are the annual growth rate of GDP per capita, manufacturing value added and terms of trade adjustments in 1991-2018. Therefore, the first-order vector autoregression model was constructed to examine this relationship. According to the results of research, acceleration of terms of trade adjustments growth rate (deterioration of terms of trade) in Ukraine leads to fluctuations in the manufacturing value added growth with an increase of almost 3% in the second period and further declining. It indicates an increase in industrial production in response to the deterioration of terms of trade in the short run and possible intensification of innovative economic growth triggers. Fluctuations in manufacturing value added account for from almost 7% to 14% in fluctuations of GDP per capita growth. In turn, fluctuations in terms of trade adjustments account for only from 3% to almost 5%. At the same time, fluctuations in manufacturing value added from 8% to 13% are explained by fluctuations in terms of trade adjustments.

Dividend Yields, Dividend Growth, and Return Predictability in the Cross Section of Stocks

AbstractThere is a generalized conviction that variation in dividend yields is exclusively related to expected returns and not to expected dividend growth, for example, Cochrane’s (2011) presidential address. We show that this pattern, although valid for the aggregate stock market, is not true for portfolios of small and value stocks, where dividend yields are related mainly to future dividend changes. Thus, the variance decomposition associated with the aggregate dividend yield has important heterogeneity in the cross section of equities. Our results are robust to different forecasting horizons, econometric methodology (long-horizon regressions or first-order vector autoregression), and alternative decomposition based on excess returns.

Arbitrage-Free Gaussian Affine Term Structure Model with Observable Factors

This paper analyzes a simple discrete-time affine multifactor model of the term structure of interest rates in which the pricing factors that follow a Gaussian first-order vector autoregression are observable and there are no possibilities for risk-free arbitrage. We present the theoretical results for the compatible risk-neutral dynamics of observable factors in a maximally flexible way consistent with no-arbitrage under the assumption that the factor loadings of some yields are specified exogenously.

INTERTEMPORAL PORTFOLIO ALLOCATION AND HEDGING DEMAND: AN APPLICATION TO SOUTH AFRICA

This paper analyses the intertemporal hedging demand for stocks and bonds in South Africa, the United Kingdom and the United States. The analysis is done using an approximate solution method for the optimal consumption and wealth portfolio problem of an infinitely long-lived investor. Investors are assumed to have Epstein-Zin-Weil-type preferences and face asset returns described by a first-order vector autoregression in returns and state variables. The results show that the mean intertemporal hedging demands for stocks are considerably smaller in SA than in the UK or the US, whilst the mean intertemporal hedging demand for bonds are not significantly different from zero in any of the countries considered. Furthermore, it is found that stocks in the US and the UK do not present a useful hedging opportunity for an investor in SA, nor do SA stocks present a useful hedging opportunity for investors from the UK or the US.

An Idiographic Examination of Day-to-Day Patterns of Substance Use Craving, Negative Affect, and Tobacco Use Among Young Adults in Recovery

Psychological constructs, such as negative affect and substance use cravings that closely predict relapse, show substantial intraindividual day-to-day variability. This intraindividual variability of relevant psychological states combined with the “one day at a time” nature of sustained abstinence warrant a day-to-day investigation of substance use recovery. This study examines day-to-day associations among substance use cravings, negative affect, and tobacco use among 30 college students in 12-step recovery from drug and alcohol addictions. To account for individual variability in day-to-day process, it applies an idiographic approach. The sample of 20 males and 10 females (mean age = 21) was drawn from members of a collegiate recovery community at a large university. Data were collected with end-of-day data collections taking place over an average of 26.7 days. First-order vector autoregression models were fit to each individual predicting daily levels of substance use cravings, negative affect, and tobacco use from the same 3 variables 1 day prior. Individual model results demonstrated substantial interindividual differences in intraindividual recovery process. Based on estimates from individual models, cluster analyses were used to group individuals into 2 homogeneous subgroups. Group comparisons demonstrate distinct patterns in the day-to-day associations among substance use cravings, negative affect, and tobacco use, suggesting the importance of idiographic approaches to recovery management and that the potential value of focusing on negative affect or tobacco use as prevention targets depends on idiosyncratic processes.

The Long-Run Relation between Returns, Earnings, and Dividends

We examine the predictive ability of the aggregate earnings yield for market returns and earnings growth by estimating a variance decomposition. Based on weighted long-horizon regressions for the 1946-2015 period, we find that most of the variation in the earnings yield is due to return predictability, with earnings growth predictability assuming a secondary role. However, by estimating a variance decomposition based on a first-order vector autoregression (VAR), we find that the major driving force is earnings growth predictability. We show that the share of earnings predictability implied from the VAR is due to a misspecification driven by the unusual high volatility in aggregate U.S. earnings during the recent financial crisis (2007-2009). Our results suggest that the practice of analyzing long-run return and cash-flow predictability from a VAR can be misleading, and reinforce previous evidence that what drives variation in aggregate price ratios is return (instead of cash-flow) predictability.

A Stochastic Programming Approach For Multi-Period Portfolio Optimization With Transaction Costs

This paper uses stochastic programming to solve multi-period investment problems. We combine the feature of asset return predictability with practically relevant constraints arising in a multi-period investment context. The objective is to maximize the expected utility of the returns the periods to balance the liabilities. Asset returns and state variables follow a first-order vector auto-regression and the associated uncertainty is described by discrete scenario trees. To deal with the long time intervals involved in multi-period problems, we consider short-term decisions, and incorporate a solution for the long, subsequent steady-state period to account for end effects.

A stochastic programming approach for multi-period portfolio optimization

This paper extends previous work on the use of stochastic linear programming to solve life-cycle investment problems. We combine the feature of asset return predictability with practically relevant constraints arising in a life-cycle investment context. The objective is to maximize the expected utility of consumption over the lifetime and of bequest at the time of death of the investor. Asset returns and state variables follow a first-order vector auto-regression and the associated uncertainty is described by discrete scenario trees. To deal with the long time intervals involved in life-cycle problems we consider a few short-term decisions (to exploit any short-term return predictability), and incorporate a closed-form solution for the long, subsequent steady-state period to account for end effects.

Constructing and estimating a realistic optimizing model of monetary policy

A dynamic stochastic general-equilibrium (DSGE) model with real and nominal, both price and wage, rigidities succeeds in capturing some key nominal features of U.S. business cycles. Additive technology shocks, as well as multiplicative shocks, are introduced and shown to be crucial. Monetary policy is specified as an interest rate targeting rule following developments in the structural vector autoregression (VAR) literature. The interaction between real and nominal rigidities is essential to reproduce the liquidity effect of monetary policy. The model is estimated by maximum likelihood on U.S. data, and its fit is comparable to that of an unrestricted first-order VAR. Besides producing reasonable impulse responses and second moments, this model replicates a feature of U.S. business cycles, never captured by previous research with DSGE models, that an increase in interest rates predicts a decrease in output two to six quarters in the future.

Monetary Policy in a Stochastic Equilibrium Model with Real and Nominal Rigidities

A dynamic stochastic general-equilibrium (DSGE) model with real and nominal rigidities succeeds in capturing some key nominal features of U.S. business cycles. Additive technology shocks, as well as multiplicative shocks, are introduced. Monetary policy is specified following the developments in the structural vector autoregression (VAR) literature. Interaction between real and nominal rigidities is essential to reproduce the liquidity effect of monetary policy. The model is estimated by maximum likelihood on U.S. data, and its fit is comparable to that of an unrestricted first-order VAR. Besides producing reasonable impulse responses and second moments, this model replicates a feature of U.S. business cycles, never captured by previous research with DSGE models, that an increase in interest rates predicts a decrease in output two to six quarters in the future. Finally, some policy implications are discussed.