Estimating income and price elasticities of imports for Fiji in a cointegration framework

This paper estimates an import demand model for Fiji using the recently developed bounds testing approach to cointegration for the period 1972 to 1999. To estimate the long-run elasticities, we use three approaches: the autoregressive distributed lag (ARDL) model, the dynamic ordinary least squares (DOLS) approach and the fully modified ordinary least squares technique. Our results indicate a long-run cointegration relationship among the variables when import volume is the dependent variable. We find that the coefficient on income is elastic while the coefficient on relative prices (import price relative to domestic price) is unitary elastic in the long run. The error correction mechanism reveals that after any shock(s) to the determinants of import demand equilibrium is attained after 2 1/2 years.

Economic growth with coal, oil and renewable energy consumption in China: prospects for fuel substitution

We examine the relationship between Chinese aggregate production and consumption of three main energy commodities: coal, oil and renewable energy. Both autoregressive distributed lag (ARDL) and vector error correction modeling (VECM) show that Chinese growth is led by all three energy sources. Economic growth also causes coal, oil and renewables consumption, but with negative own-price effects for coal and oil and a strong possibility of fuel substitution through positive cross-price effects. The results further show coal consumption causing pollution, while renewable energy consumption reduces emissions. No significant causation on emissions is found for oil. Hence, making coal both absolutely and relatively expensive compared to oil and renewable energy encourages shifting from coal to oil and renewable energy, thereby improving economic and environmental sustainability.

Identifying the determinants of renewable energy consumption in leading renewable energy investor emerging countries

This article analyses the determinants of renewable energy consumption in six major emerging economies who are proactively accelerating the adoption of renewable energy. The long-run elasticities from both panel methods (fully modified ordinary least square and dynamic least square) and the time series method (autoregressive distributed lag) seem to be pretty consistent. For Brazil, China, India and Indonesia, in the long-run, renewable energy consumption is significantly determined by income and pollutant emission. However, for Philippines and Turkey, income seems to be the main driver for renewable energy consumption. In the short-run, for Brazil and China bi-directional causalities between renewable energy and income; and between renewable energy and pollutant emission are found. This research justifies the efforts undertaken by emerging countries to reduce the carbon intensity by increasing the energy efficiency and substantially increasing the share of renewable in the overall energy mix

Debt-growth linkages in EMU across countries and time horizons

This paper contributes to the literature by empirically examining whether the influence of public debt on economic growth differs between the short and the long run and presents different patterns across euro-area countries. To this end, we use annual data from both central and peripheral countries of the European Economic and Monetary Union (EMU) for the 1960-2012 period and estimate a growth model augmented for public debt using the Autoregressive Distributed Lag (ARDL) bounds testing approach. Our findings tend to support the view that public debt always has a negative impact on the long-run performance of EMU countries, whilst its short-run effect may be positive depending on the country.

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.

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.

Temperature variation between neighboring days and mortality: a distributed lag non-linear analysis

Objectives To investigate whether a sudden temperature change between neighboring days has significant impact on mortality. Methods A Poisson generalized linear regression model combined with a distributed lag non-linear models was used to estimate the association of temperature change between neighboring days with mortality in a subtropical Chinese city during 2008–2012. Temperature change was calculated as the current day’s temperature minus the previous day’s temperature. Results A significant effect of temperature change between neighboring days on mortality was observed. Temperature increase was significantly associated with elevated mortality from non-accidental and cardiovascular diseases, while temperature decrease had a protective effect on non-accidental mortality and cardiovascular mortality. Males and people aged 65 years or older appeared to be more vulnerable to the impact of temperature change. Conclusions Temperature increase between neighboring days has a significant adverse impact on mortality. Further health mitigation strategies as a response to climate change should take into account temperature variation between neighboring days.

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.

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.

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.

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.

Real Estate Investment and Uncertainty: Econometric Modelling using Finnish Data

The purpose of this paper is to test for the effect of uncertainty in a model of real estate investment in Finland during the hihhly cyclical period of 1975 to 1998. We use two alternative measures of uncertainty. The first measure is the volatility of stock market returns and the second measure is the heterogeneity in the answers of the quarterly business survey of the Confederation of Finnish Industry and Employers. The econometric analysis is based on the autoregressive distributed lag (ADL) model and the paper applies a 'general-to-specific' modelling approach. We find that the measure of heterogeneity is significant in the model, but the volatility of stock market returns is not. The empirical results give some evidence of an uncertainty-induced threshold slowing down real estate investment in Finland.