A BAYESIAN HIERARCHICAL MODEL FOR CONSTRAINED DISTRIBUTED LAG FUNCTIONS: ESTIMATING THE TIME COURSE OF HOSPITALIZATION ASSOCIATED WITH AIR POLLUTION EXPOSURE

Numerous time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased levels of hospital admissions, typically at 0, 1, or 2 days after an air pollution episode. An important research aim is to extend existing statistical models so that a more detailed understanding of the time course of hospitalization after exposure to air pollution can be obtained. Information about this time course, combined with prior knowledge about biological mechanisms, could provide the basis for hypotheses concerning the mechanism by which air pollution causes disease. Previous studies have identified two important methodological questions: (1) How can we estimate the shape of the distributed lag between increased air pollution exposure and increased mortality or morbidity? and (2) How should we estimate the cumulative population health risk from short-term exposure to air pollution? Distributed lag models are appropriate tools for estimating air pollution health effects that may be spread over several days. However, estimation for distributed lag models in air pollution and health applications is hampered by the substantial noise in the data and the inherently weak signal that is the target of investigation. We introduce an hierarchical Bayesian distributed lag model that incorporates prior information about the time course of pollution effects and combines information across multiple locations. The model has a connection to penalized spline smoothing using a special type of penalty matrix. We apply the model to estimating the distributed lag between exposure to particulate matter air pollution and hospitalization for cardiovascular and respiratory disease using data from a large United States air pollution and hospitalization database of Medicare enrollees in 94 counties covering the years 1999-2002.

Bayesian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortality

In this paper, we develop Bayesian hierarchical distributed lag models for estimating associations between daily variations in summer ozone levels and daily variations in cardiovascular and respiratory (CVDRESP) mortality counts for 19 U.S. large cities included in the National Morbidity Mortality Air Pollution Study (NMMAPS) for the period 1987 - 1994. At the first stage, we define a semi-parametric distributed lag Poisson regression model to estimate city-specific relative rates of CVDRESP associated with short-term exposure to summer ozone. At the second stage, we specify a class of distributions for the true city-specific relative rates to estimate an overall effect by taking into account the variability within and across cities. We perform the calculations with respect to several random effects distributions (normal, t-student, and mixture of normal), thus relaxing the common assumption of a two-stage normal-normal hierarchical model. We assess the sensitivity of the results to: 1) lag structure for ozone exposure; 2) degree of adjustment for long-term trends; 3) inclusion of other pollutants in the model;4) heat waves; 5) random effects distributions; and 6) prior hyperparameters. On average across cities, we found that a 10ppb increase in summer ozone level for every day in the previous week is associated with 1.25 percent increase in CVDRESP mortality (95% posterior regions: 0.47, 2.03). The relative rate estimates are also positive and statistically significant at lags 0, 1, and 2. We found that associations between summer ozone and CVDRESP mortality are sensitive to the confounding adjustment for PM_10, but are robust to: 1) the adjustment for long-term trends, other gaseous pollutants (NO_2, SO_2, and CO); 2) the distributional assumptions at the second stage of the hierarchical model; and 3) the prior distributions on all unknown parameters. Bayesian hierarchical distributed lag models and their application to the NMMAPS data allow us estimation of an acute health effect associated with exposure to ambient air pollution in the last few days on average across several locations. The application of these methods and the systematic assessment of the sensitivity of findings to model assumptions provide important epidemiological evidence for future air quality regulations.

Effects of quantitative easing on the USA economy: A test for policy effectiveness

The catastrophic disruption in the USA financial system in the wake of the financial crisis prompted the Federal Reserve to launch a Quantitative Easing (QE) programme in late 2008. In line with Pesaran and Smith (2014), I use a policy effectiveness test to assess whether this massive asset purchase programme was effective in stimulating the economic activity in the USA. Specifically, I employ an Autoregressive Distributed Lag Model (ARDL), in order to obtain a counterfactual for the USA real GDP growth rate. Using data from 1983Q1 to 2009Q4, the results show that the beneficial effects of QE appear to be weak and rather short-lived. The null hypothesis of policy ineffectiveness is not rejected, which suggests that QE did not have a meaningful impact on output growth.

Is devaluation expansionary or contractionary? empirical evidence from Fiji

Devaluation has been traditionally promoted as an effective tool for increasing exports and improving the external position of the devaluing country if a nominal devaluation results in expenditure switching. In this article, our aim is to model the relationship between currency devaluations and output for Fiji. Following the approach in Bahmani et al. (2002), we extend the traditional model by incorporating other monetary and fiscal policy variables. We achieve our goal by using the recently developed bounds testing approach to cointegration and the autoregressive distributed lag model and find that devaluation is expansionary in the case of Fiji.

Estimating impacts of consumer prices on house prices

Real estate is widely considered as a reliable hedge of inflation rate and there have been many literatures examining the inflation-hedging characteristics of the real estate.  The study described in the paper focuses on testing the significances of impacts of consumer price on house price in eight Australia's capital cities.  The Autoregressive Distributed Lag model is introduced to obtain  the estimates of the coefficient.  The significances of the impacts are defined as the accept probability of t statistics of the coefficients.  Analyses and comparisons of these significances suggested that the impacts of consumer prices on house prices depend on the inherent characteristics of cities. 

Examining the relationship between trade balance and exchange rate : the case of China's trade with the USA

In this paper, we investigate the nexus between China's trade balance and the real exchange rate vis-à-vis the USA. Using the bounds testing approach to cointegration, we find evidence that China's trade balance and real exchange rate vis-à-vis the USA are cointegrated, and using the autoregressive distributed lag model we find that in both the short run and the long run a real devaluation of the Chinese RMB improves the trade balance; as a result, there is no evidence of a J-curve type adjustment.

Determinants of female fertility in Taiwan, 1966–2001 : empirical evidence from cointegration and variance decomposition analysis

This study investigates the determinants of the fertility rate in Taiwan over the period 1966–2001. Consistent with theory, the key explanatory variables in Taiwan's fertility model are real income, infant mortality rate, female education and female labor force participation rate. The test for cointegration is based on the recently developed bounds testing procedure while the long-run and short-run elasticities are based on the autoregressive distributed lag model. Among our key results, female education and female labor force participation rate are found to be the key determinants of fertility in Taiwan in the long run. The variance decom-position analysis indicates that in the long run approximately 45percent of the variation in fertility is explained by the combined impact of female labor force participation, mortality and income, implying that socioeconomic development played an important role in the fertility transition in Taiwan. This result is consistent with the traditional structural hypothesis.

Savings behaviour in Fiji : an empirical assessment using the ARDL approach to cointegration

Purpose – This paper aims to delineate the short- and long-run relationships between savings, real interest rate, income, current account deficits (CADs) and age dependency ratio in Fiji using cointegration and error correction models over the period 1968-2000.

Design/methodology/approach – The recently developed bounds testing approach to cointegration is used, which is applicable irrespective of whether the underlying variables are integrated of order one or order zero. Given the small sample size in this study, appropriate critical values were extracted from Narayan. To estimate the short- and long-run elasticities, the autoregressive distributed-lag model is used.

Findings – In the short- and long-run: a 1 per cent increase in growth rate increases savings by over 0.07 and 0.5 per cent, respectively; a 1 per cent increase in the CAD reduces savings rate by 0.01 and 0.02 per cent, respectively; and the negative coefficient on the real interest rate implies that the income effect dominates the substitution effect, while in the short-run the total effect of the real interest rate is positive, implying that the substitution effect dominates the income effect.

Originality/value – This paper makes the first attempt at estimating the savings function for the Fiji Islands. Given that Fiji's capital market is poorly developed, the empirical findings here have direct policy relevance.

Why do Australians, New Zealanders and Americans travel to Fiji? Evidence from cointegration, unit roots and parameter stability tests

The aim of this study was to estimate the demand for Fiji’s tourism from its three main source markets—Australia, New Zealand, and the US—using the bounds testing approach to cointegration. Our main finding was that visitor arrivals to Fiji and its key determinants are cointegrated over the 1970–2000 period. We then used the autoregressive distributed lag model to estimate short-run and long-run elasticities and found that income in origin countries, transport costs, and prices were significant determinants of Fiji’s tourism demand. We also found that coups negatively impact visitor arrivals from all markets. In testing for parameter stability, we established that the series were integrated of order one in the presence of a structural break. We then used the Hansen test for parameter stability and found that the parameters of our long-run model are stable over time.

An empirical analysis of Fiji’s import demand function

Purpose – This paper aims to estimate a disaggregated import demand model for Fiji using relative prices, total consumption, investment expenditure and export expenditure variables for the period 1970 to 2000.

Design/methodology/approach – The recently developed bounds testing approach to cointegration to test for a long run relationship is used, while the autoregressive distributed lag model is used to estimate short run and long run elasticities. These methodologies are shown to perform well in small sample sizes, particularly given that the bounds F-test critical values for small sample sizes generated by Narayan in 2004 and 2005 are used.

Findings – Amongst the key results it is found: a long run cointegration relationship among the variables when import demand is the dependent variable; and import demand to be inelastic and statistically significant at the 1 per cent level with respect to all the explanatory variables in both the long-run and the short-run.

Originality/value – The disaggregated import demand model estimated here provides a complete picture of the determinants of Fiji's imports. This model can be used by Fijian policy makers to draw pertinent policies and forecast import demand for Fiji.

Can investors hedge residential price dynamics of Australia's capital cities?

The study described in this paper focuses on testing the short-run and
long-run relationships between house price and consumer price indices in Australia’s capital cities from 1998 to 2008. The autoregressive distributed lag model is adopted to obtain the estimates of the short-run relationships, while the error correction model is used to investigate the long-run relationships. The t-statistic is used to compute the significance of these relationships. The research results give no evidence that house price indices are correlated with consumer price indices in the short run. However, the long-run relationships between house and consumer price indices exist in most of the cities.

O nexo causal entre crédito rural e crescimento do produto agropecuário na economia brasileira

O nexo causal entre desenvolvimento financeiro e crescimento econômico vem ganhando destaque na literatura desde o início dos anos 1990. As principais linhas teóricas nessa área buscam demonstrar qual a significância da relação e o sentido da causalidade, se houver. Causalidade unidirecional no sentido do desenvolvimento financeiro para o crescimento econômico, bicausalidade entre ambos, e causalidade reversa, no sentido do crescimento para o desenvolvimento financeiro, são as principais hipóteses testadas nas pesquisas empíricas. O presente trabalho de tese tem por objetivo avaliar o nexo causal entre crédito (como um indicador do desenvolvimento financeiro) e crescimento no setor agropecuário brasileiro. O crédito rural como proporção do PIB agropecuário cresceu substancialmente desde meados da década de 90, passando de 15,44% em 1996 para 65,24% em 2014. Ao longo do período 1969-2014, a razão média anual entre crédito rural e PIB agropecuário foi de 43,87%. No mesmo período, o produto agropecuário cresceu em média 3,76% ao ano. Questiona-se se no mercado rural o crédito causa o crescimento agropecuário, se ocorre causalidade reversa ou se se opera a hipótese de bicausalidade. Para avaliar o nexo causal entre essas duas variáveis econômica foram empregados quatro procedimentos metodológicos: teste de causalidade de Granger em uma representação VAR com a abordagem de Toda e Yamamoto, teste de causalidade de Granger em um modelo FMOLS (Fully Modified OLS), teste de causalidade de Granger em um modelo ARDL (Autoregressive-Distributed Lag) e teste de causalidade de Granger no domínio da frequência, com o uso do método de Breitung e Candelon. Os resultados mostram de forma uniforme a presença de causalidade unidirecional do crédito rural para o crescimento do produto agropecuário. Causalidade reversa, no sentido do crescimento agropecuário para o crédito rural, não foi detectada de forma significativa em nenhum dos quatro métodos empregados. A não detecção de bicausalidade pode ser uma evidência do impacto da forte política de subsídio governamental ao crédito rural. A decisão do Governo quanto ao montante anual de crédito rural disponível a taxas de juros subsidiadas pode estar impedindo que o desempenho do setor, medido pela sua taxa de crescimento, exerça uma influência significativa na dinâmica do crédito rural. Os resultados também abrem a possibilidade a testar a hipótese de exogeneidade do crédito rural, o que seria uma extensão direta dos resultados obtidos.

The impact of temperature on mortality in Tianjin, China : a case-crossover design with a distributed lag non-linear model

Background There has been increasing interest in assessing the impacts of temperature on mortality. However, few studies have used a case–crossover design to examine non-linear and distributed lag effects of temperature on mortality. Additionally, little evidence is available on the temperature-mortality relationship in China, or what temperature measure is the best predictor of mortality. Objectives To use a distributed lag non-linear model (DLNM) as a part of case–crossover design. To examine the non-linear and distributed lag effects of temperature on mortality in Tianjin, China. To explore which temperature measure is the best predictor of mortality; Methods: The DLNM was applied to a case¬−crossover design to assess the non-linear and delayed effects of temperatures (maximum, mean and minimum) on deaths (non-accidental, cardiopulmonary, cardiovascular and respiratory). Results A U-shaped relationship was consistently found between temperature and mortality. Cold effects (significantly increased mortality associated with low temperatures) were delayed by 3 days, and persisted for 10 days. Hot effects (significantly increased mortality associated with high temperatures) were acute and lasted for three days, and were followed by mortality displacement for non-accidental, cardiopulmonary, and cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature. Conclusions In Tianjin, extreme cold and hot temperatures increased the risk of mortality. Results suggest that the effects of cold last longer than the effects of heat. It is possible to combine the case−crossover design with DLNMs. This allows the case−crossover design to flexibly estimate the non-linear and delayed effects of temperature (or air pollution) whilst controlling for season.