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To capture the conditional correlations between bivariate financial responses at different quantile levels, this paper considers the Bayesian quantile regression for bivariate vector autoregressive models. With the well known location-scale mixture representation for the asymmetric Laplace distribution, a working likelihood is obtained. By introducing the latent variables, a new Gibbs sampling algorithm is developed for drawing the posterior samples for the parameters and latent variables. The numerical simulation implies that the Gibbs sampling algorithm converges fast and the Bayesian quantile estimators perform well. Finally, a real example is given to discuss the relationship between the Canadian dollar to U.S. dollar exchange rate and long term annual interest rate of Canada. [ABSTRACT FROM AUTHOR]

As life expectancy continues to increase, there is a growing concern that the same pace of health improvement may not follow. An ageing population spending more years in disability and long-term sickness can place a significant financial burden on society. It is therefore crucial for governments to accurately forecast not just life expectancy but also healthy life expectancy. In particular, examining the highest healthy life expectancy can provide valuable information, as it represents the current best experience worldwide. Although there have been numerous studies on forecasting life expectancy, relatively few authors have investigated the forecasting of healthy life expectancy, often due to health data limitations. In this paper, we propose a Bayesian approach to co-model the highest healthy life expectancy and the highest life expectancy. The resulting forecasts would offer useful insights for governments in shaping healthcare and social policies to improve the wellbeing of seniors and retirees. [ABSTRACT FROM AUTHOR]

Investigating the relationship between Gross Domestic Product and unemployment is one of the most important challenges in macroeconomics. In this paper, we compare French and German economies in terms of the dynamic linkage between these variables. In particular, we use an empirical methodology to investigate how much the relationship between Gross Domestic Product and unemployment growth rates are dynamically different in the two major European economies over the period 2003–2019. To this aim, a Vector Autoregressive model is specified for each country to jointly model the growth rate of the two variables. Then a new statistical test is proposed to assess the distance between the two estimated models. Results indicate that the dynamic linkage between Gross Domestic Product and unemployment is very similar in the two countries. This empirical evidence does not imply identical product and labor markets in France and Germany, but it ensures that in these markets there are common dynamics. This could favor the process of economic convergence between the two countries. [ABSTRACT FROM AUTHOR]

A recent literature has shown that REIT returns contain strong evidence of bull and bear dynamic regimes that may be best captured using nonlinear econometric models of the Markov switching type. In fact, REIT returns would display regime shifts that are more abrupt and persistent than in the case of other asset classes. In this paper we ask whether and how simple linear predictability models of the vector autoregressive (VAR) type may be extended to capture the bull and bear patterns typical of many asset classes, including REITs. We find that nonlinearities are so deep that it is impossibile for a large family of VAR models to either produce similar portfolio weights or to yield realized, ex-post out-of-sample long-horizon portfolio performances that may compete with those typical of bull and bear models. A typical investor with intermediate risk aversion and a 5-year horizon ought to be ready to pay an annual fee of up to 5.7 % to have access to forecasts of REIT returns that take their bull and bear dynamics into account instead of simpler, linear forecast. [ABSTRACT FROM AUTHOR]

Studies on the relationship between exchange rates and traded goods prices typically find evidence of incomplete pass-through, usually explained by pricing-to-market behaviour. Although economic theory predicts that incomplete pass-through may also be linked to the presence of non-tariff barriers to trade, variables reflecting such a link is rarely included in empirical models. In this paper, we estimate a pricing-to-market model for Norwegian import prices on textiles and wearing apparels, controlling for non-tariff barriers to trade and shift in imports from high- to low-cost countries. We apply the cointegrated VAR approach and develop measures of foreign prices based on superlative price indices (including the Törnqvist and Fischer price indices) and a data calibration method necessary to approximate relative price levels across countries. Our measures of foreign prices thereby account for inflationary differences and varying import shares and price level differences (known as the China effect) among trading partners. We show that these measures of foreign prices, unlike standard measures used in the pricing-to-market literature, are likely to produce unbiased estimates of pass-through. Once the China effect is controlled for, we find little evidence that pass-through has changed alongside trade liberalisation. [ABSTRACT FROM AUTHOR]

n interval time series (ITS) is a time series where each period is described by an interval. In finance, ITS can describe the temporal evolution of the high and low prices of an asset throughout time. These price intervals are related to the concept of volatility and are worth considering in order to place buy or sell orders. This article reviews two approaches to forecast ITS. On the one hand, the first approach consists of using univariate or multivariate forecasting methods. The possible cointegrating relation between the high and low values is analyzed for multivariate models and the equivalence of the VAR models is shown for the minimum and the maximum time series, as well as for the center and radius time series. On the other hand, the second approach adapts classic forecasting methods to deal with ITS using interval arithmetic. These methods include exponential smoothing, the k-NN algorithm and the multilayer perceptron. The performance of these approaches is studied in two financial ITS. As a result, evidences of the predictability of the ITS are found, especially in the interval range. This fact opens a new path in volatility forecasting. [ABSTRACT FROM AUTHOR]

This paper provides closed-form formulae for computing the asymptotic covariance matrices of the estimated autocovariance and autocorrelation functions of stable VAR models by means of the delta method. These covariance matrices can be used to construct asymptotic confidence bands for the estimated autocovariance and autocorrelation functions to assess the underlying estimation uncertainty. The usefulness of the formulae for empirical work is illustrated by an application to inflation and output gap data for the U.S. economy indicating the existence of a significant short-run Phillips-curve tradeoff. [ABSTRACT FROM AUTHOR]

Recent arguments, motivated partly by the new fiscal theory of price level, suggest that fiscal deficits undermine price stability in transition economies. This paper addresses these claims by examining vector-autoregressive models of inflation for three transition economies (Bulgaria, Romania and Russia). The results indicate that fiscal deficits have increased inflation in Bulgaria and Romania but not in the case of Russia. In Bulgaria and Romania, money aggregates and exchange rate have also been more influential to inflation than fiscal deficits. The analysis based on this method therefore suggests that while fiscal deficits have some influence on inflation, monetary factors mostly determine inflation in these three countries. [ABSTRACT FROM AUTHOR]

Recent arguments, motivated partly by the new fiscal theory of price level, suggest that fiscal deficits undermine price stability in transition economies. This paper addresses these claims by examining vector-autoregressive models of inflation for three transition economies (Bulgaria, Romania and Russia). The results indicate that fiscal deficits have increased inflation in Bulgaria and Romania but not in the case of Russia. In Bulgaria and Romania, money aggregates and exchange rate have also been more influential to inflation than fiscal deficits. The analysis based on this method therefore suggests that while fiscal deficits have some influence on inflation, monetary factors mostly determine inflation in these three countries. [ABSTRACT FROM AUTHOR]

We review the derivation of the fast orthogonal search algorithm, first proposed by Korenberg, with emphasis on its application to the problem of estimating coefficient matrices of vector autoregressive models. New aspects of the algorithm not previously considered are examined. One of these is the application of the algorithm to estimate coefficient matrices of a vector autoregressive process with time-varying coefficients when multiple realizations of the said process are available. Computer simulations were also performed to characterize the statistical properties of the estimates. The results show that even for shorter time series the algorithm works well and obtains good estimates of the time-varying parameters. Statistical characterization indicates that the standard deviation of the estimates decreases as $${1 \mathord{\left/ {\vphantom {1 {\sqrt N }}} \right. \kern-\nulldelimiterspace} {\sqrt N }}{\text{ }}\left( N \right.$$ being the length of the time series), a typical behavior of least-squares estimators. Another key aspect of the approach, which has previously been considered, is its direct extension to the parameter estimation of vector nonlinear autoregressive models. Nonlinear terms can be added to the model and the same algorithm can be applied to effectively estimate their associated parameters. Using chaotic time series generated from the Lorenz equations, the algorithm produces a model that captures the nonlinear structure of the data and exhibits the same chaotic attractor as that of the original system. © 2002 Biomedical Engineering Society. PAC2002: 8710+e, 0210Yn, 0230Lt [ABSTRACT FROM AUTHOR]