Combined Analysis of the Short-Term Effects of Photochemical Air Pollutants on Mortality within the EMECAM Project

In recent years, some epidemiologic studies have attributed adverse effects of air pollutants on health not only to particles and sulfur dioxide but also to photochemical air pollutants (nitrogen dioxide and ozone). The effects are usually small, leading to some inconsistencies in the results of the studies. Furthermore, the different methodologic approaches of the studies used has made it difficult to derive generic conclusions. We provide here a quantitative summary of the short-term effects of photochemical air pollutants on mortality in seven Spanish cities involved in the EMECAM project, using generalized additive models from analyses of single and multiple pollutants. Nitrogen dioxide and ozone data were provided by seven EMECAM cities (Barcelona, Gijón, Huelva, Madrid, Oviedo, Seville, and Valencia). Mortality indicators included daily total mortality from all causes excluding external causes, daily cardiovascular mortality, and daily respiratory mortality. Individual estimates, obtained from city-specific generalized additive Poisson autoregressive models, were combined by means of fixed effects models and, if significant heterogeneity among local estimates was found, also by random effects models. Significant positive associations were found between daily mortality (all causes and cardiovascular) and NO2, once the rest of air pollutants were taken into account. A 10 μg/m3 increase in the 24-hr average 1-day NO2 level was associated with an increase in the daily number of deaths of 0.43% [95% confidence interval(CI), –0.003–0.86%] for all causes excluding external. In the case of significant relationships, relative risks for cause-specific mortality were nearly twice as much as that for total mortality for all the photochemical pollutants. Ozone was independently related only to cardiovascular daily mortality. No independent statistically significant relationship between photochemical air pollutants and respiratory mortality was found. The results in this study suggest that, given the present levels of photochemical pollutants, people living in Spanish cities are exposed to health risks derived from air pollution

Evidence for the chaotic origin of Northern Annular Mode variability

Exponential spectra are found to characterize variability of the Northern Annular Mode (NAM) for periods less than 36 days. This corresponds to the observed rounding of the autocorrelation function at lags of a few days. The characteristic persistence timescales during winter and summer is found to be ∼5 days for these high frequencies. Beyond periods of 36 days the characteristic decorrelation timescale is ∼20 days during winter and ∼6 days in summer. We conclude that the NAM cannot be described by autoregressive models for high frequencies; the spectra are more consistent with low-order chaos. We also propose that the NAM exhibits regime behaviour, however the nature of this has yet to be identified.

Separating skill from luck in REIT mutual funds

This study uses a bootstrap methodology to explicitly distinguish between skill and luck for 80 Real Estate Investment Trust Mutual Funds in the period January 1995 to May 2008. The methodology successfully captures non-normality in the idiosyncratic risk of the funds. Using unconditional, beta conditional and alpha-beta conditional estimation models, the results indicate that all but one fund demonstrates poor skill. Tests of robustness show that this finding is largely invariant to REIT market conditions and maturity.

Business cycle asymmetries: characterisation and testing based on Markov-switching autoregressions

Tests for business cycle asymmetries are developed for Markov-switching autoregressive models. The tests of deepness, steepness, and sharpness are Wald statistics, which have standard asymptotics. For the standard two-regime model of expansions and contractions, deepness is shown to imply sharpness (and vice versa), whereas the process is always nonsteep. Two and three-state models of U.S. GNP growth are used to illustrate the approach, along with models of U.S. investment and consumption growth. The robustness of the tests to model misspecification, and the effects of regime-dependent heteroscedasticity, are investigated.

Bicorrelations and cross-bicorrelations as non-linearity tests and tools for exchange rate forecasting

This paper proposes and implements a new methodology for forecasting time series, based on bicorrelations and cross-bicorrelations. It is shown that the forecasting technique arises as a natural extension of, and as a complement to, existing univariate and multivariate non-linearity tests. The formulations are essentially modified autoregressive or vector autoregressive models respectively, which can be estimated using ordinary least squares. The techniques are applied to a set of high-frequency exchange rate returns, and their out-of-sample forecasting performance is compared to that of other time series models

Robust approaches to forecasting

We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium-correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, impulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods are likely to perform well. The robust methods are applied to forecasting US GDP using autoregressive models, and also to autoregressive models with factors extracted from a large dataset of macroeconomic variables. We consider forecasting performance over the Great Recession, and over an earlier more quiescent period.

Real-time factor model forecasting and the effects of instability

Factor forecasting models are shown to deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one quarter ahead) horizon. Excluding the pre-Great Moderation years from the factor forecasting model estimation period (but not from the data used to extract factors) results in a marked fillip in factor model forecast accuracy, but does the same for the AR model forecasts. The relative performance of the factor models compared to the AR models is largely unaffected by whether the exercise is in real time or is pseudo out-of-sample.

The housing market channel of monetary policy transmission in the Euro area

Purpose – The purpose of this paper is to explore the role of the housing market in the monetary policy transmission to consumption among euro area member states. It has been argued that the housing market in one country is then important when its mortgage market is well developed. The countries in the euro area follow unitary monetary policy, however, their housing and mortgage markets show some heterogeneity, which may lead to different policy effects on aggregate consumption through the housing market. Design/methodology/approach – The housing market can act as a channel of monetary policy shocks to household consumption through changes in house prices and residential investment – the housing market channel. We estimate vector autoregressive models for each country and conduct a counterfactual analysis in order to disentangle the housing market channel and assess its importance across the euro area member states. Findings – We find little evidence for heterogeneity of the monetary policy transmission through house prices across the euro area countries. Housing market variations in the euro area seem to be better captured by changes in residential investment rather than by changes in house prices. As a result we do not find significantly large house price channels. For some of the countries however, we observe a monetary policy channel through residential investment. The existence of a housing channel may depend on institutional features of both the labour market or with institutional factors capturing the degree of household debt as is the LTV ratio. Originality/value – The study contributes to the existing literature by assessing whether a unitary monetary policy has a different impact on consumption across the euro area countries through their housing and mortgage markets. We disentangle monetary-policy-induced effects on consumption associated with variations on the housing markets due to either house price variations or residential investment changes. We show that the housing market can play a role in the monetary transmission mechanism even in countries with less developed mortgage markets through variations in residential investment.

Measuring time series predictability using support vector regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.

Stock Markets and Household Wealth: Can a Stock Market Crash Cause a Recession in The U.S. Economy?

Stock market wealth effects on the level of consumption in the United States economy have been constantly debated; there is evidence for arguments for and against its prominence and its symmetry. This paper seeks to investigate the strength of its negative effect by creating models to analyze unexpected shocks to the Standard and Poor's 500 index. First, a transmission mechanism between the stock market and GDP is established through the use of second-order vector autoregressive models. Following which, theory from the life cycle model and adaptations of previous researchers' models are used to create a structural model. This paper finds that stock market wealth effects are small, but important to consider, especially if markets are overpriced; this claim is corroborated by evidence from simulation of 'alternative scenarios' and the historical experiences of 1987 and 2001.

Application of extended multivariate modeling for information flow analysis of event related responses

Event related potential (ERP) analysis is one of the most widely used methods in cognitive neuroscience research to study the physiological correlates of sensory, perceptual and cognitive activity associated with processing information. To this end information flow or dynamic effective connectivity analysis is a vital technique to understand the higher cognitive processing under different events. In this paper we present a Granger causality (GC)-based connectivity estimation applied to ERP data analysis. In contrast to the generally used strictly causal multivariate autoregressive model, we use an extended multivariate autoregressive model (eMVAR) which also accounts for any instantaneous interaction among variables under consideration. The experimental data used in the paper is based on a single subject data set for erroneous button press response from a two-back with feedback continuous performance task (CPT). In order to demonstrate the feasibility of application of eMVAR models in source space connectivity studies, we use cortical source time series data estimated using blind source separation or independent component analysis (ICA) for this data set.

The importance of common cyclical features in VAR analysis: a Monte-Carlo study

Despite the commonly held belief that aggregate data display short-run comovement, there has been little discussion about the econometric consequences of this feature of the data. We use exhaustive Monte-Carlo simulations to investigate the importance of restrictions implied by common-cyclical features for estimates and forecasts based on vector autoregressive models. First, we show that the ìbestî empirical model developed without common cycle restrictions need not nest the ìbestî model developed with those restrictions. This is due to possible differences in the lag-lengths chosen by model selection criteria for the two alternative models. Second, we show that the costs of ignoring common cyclical features in vector autoregressive modelling can be high, both in terms of forecast accuracy and efficient estimation of variance decomposition coefficients. Third, we find that the Hannan-Quinn criterion performs best among model selection criteria in simultaneously selecting the lag-length and rank of vector autoregressions.

O canal de crédito na transmissão de política monetária: evidências para o Brasil

Este trabalho traz avaliação empírica a respeito do canal de crédito no Brasil, feita com base no artigo de Holtemöller (2002). Para tanto, foi feita análise descritiva sobre a evolução do crédito no país, bem como testes econométricos utilizando dados monetários, de crédito e economia real. Observamos o aumento da importância do crédito nos últimos anos, assim como o aumento do endividamento corporativo via emissão de títulos. Portanto, seria natural esperar que o canal de crédito no mecanismo de transmissão da política monetária também se tornasse mais importante. Contudo, a análise empírica mostra que seus efeitos sobre a atividade econômica são limitados. Após estimações feitas para o canal monetário tradicional, a partir de vetores autorregressivos estruturais (SVAR) com vetores de correção de erros (VEC), incluímos variáveis de crédito para avaliar o impacto sobre o produto. Apesar de concluirmos que choques de política monetária possuem efeitos sobre a oferta de crédito, o impacto de condições creditícias restritivas sobre a produção industrial é pequeno. Alguns fatores como a existência de crédito corporativo direcionado via BNDES, maior importância da captação via mercado de capitais, medidas macroprudenciais adotadas e aumento do prazo médio concorrem para esse resultado.

Fiscal policy in Brazil: from counter-cyclical response to crisis

The main goal of this article is to identify the dynamic effects of fiscal policy on output in Brazil from 1997 to 2014, and, more specifically, to estimate those effects when the output falls below its potential level. To do so, we estimate VAR (vector autoregressive) models to generate impulse-response functions and causality/endogeneity tests. Our most remarkable results indicate the following channel of economic policy in Brazil: to foster output, government spending increases causing increases in both tax rates and revenue and the short-term interest rate. A fiscal stimulus via spending seems efficient for economic performance as well as monetary policy; however, the latter operates pro-cyclically in the way we defined here, while the former is predominantly countercyclical. As the monetary shock had a negative effect on GDP growth and GDP growth responded positively to the fiscal shock, it seems that the economic policy has given poise to growth with one hand and taken it with the other one. The monetary policy is only reacting to the fiscal stimuli. We were not able to find any statistically significant response of the output to tax changes, but vice versa seems work in the Brazilian case.

Análise comparativa e teste empírico da validade dos modelos CAPM tradicional e condicional: o caso das ações da Petrobrás

ALVES, Janaína da Silva. Análise comparativa e teste empírico da validade dos modelos CAPM tradicional e condicional: o caso das ações da Petrobrás. Revista Ciências Administrativas, Fotaleza, v. 13, n. 1, p.147-157, ago. 2007.