Search results

We investigate how career disruptions in terms of job loss may impact morbidity for individuals diagnosed with type 2 diabetes (T2D). Combining unique, high-quality longitudinal data from the Swedish National Diabetes Register (NDR) with matched employer–employee data, we focus on individuals diagnosed with T2D, who are established on the labor market and who lose their job in a mass layoff. Using a conditional difference-in-differences evaluation approach, our results give limited support for job loss having an impact on health behavior, diabetes progression, and cardiovascular risk factors.

In this chapter we approach the estimation of dynamic stochastic general equilibrium models through a moments-based estimator, the empirical likelihood. We attempt to show that this inference process can be a valid alternative to maximum likelihood, which has been one of the preferred choices of the related literature to estimate these models. The empirical likelihood estimator is characterized by a simple setup and only requires knowledge about the moments of the data generating process of the model. In this context, we exploit the fact that these economies can be formulated as a set of moment conditions to infer on their parameters through this technique. For illustrational purposes, we consider a standard real business cycle model with a constant relative risk averse utility function and indivisible labor, driven by a normal technology shock.

This chapter analyzes the empirical relationship between the pricesetting/consumption behavior and the sources of persistence in inflation and output. First, a small-scale New-Keynesian model (NKM) is examined using the method of moment and maximum likelihood estimators with US data from 1960 to 2007. Then a formal test is used to compare the fit of two competing specifications in the New-Keynesian Phillips Curve (NKPC) and the IS equation, that is, backward- and forward-looking behavior. Accordingly, the inclusion of a lagged term in the NKPC and the IS equation improves the fit of the model while offsetting the influence of inherited and extrinsic persistence; it is shown that intrinsic persistence plays a major role in approximating inflation and output dynamics for the Great Inflation period. However, the null hypothesis cannot be rejected at the 5% level for the Great Moderation period, that is, the NKM with purely forward-looking behavior and its hybrid variant are equivalent. Monte Carlo experiments investigate the validity of chosen moment conditions and the finite sample properties of the chosen estimation methods. Finally, the empirical performance of the formal test is discussed along the lines of the Akaike's and the Bayesian information criterion.

In this paper, we consider the estimation of volatility parameters in the context of a linear regression where the disturbances follow a stochastic volatility (SV) model of order one with Gaussian log-volatility. The linear regression represents the conditional mean of the process and may have a fairly general form, including for example finite-order autoregressions. We provide a computationally simple two-step estimator available in closed form. Under general regularity conditions, we show that this two-step estimator is asymptotically normal. We study its statistical properties by simulation, compare it with alternative generalized method-of-moments (GMM) estimators, and present an application to the S&P composite index.

Purpose – The purpose of this chapter is to estimate non-Gaussian distributions by means of Johnson distributions. An empirical illustration on hedge fund returns is detailed.

Methodology/approach – To fit non-Gaussian distributions, the chapter introduces the family of Johnson distributions and its general extensions. We use both parametric and non-parametric approaches. In a first step, we analyze the serial correlation of our sample of hedge fund returns and unsmooth the series to correct the correlations. Then, we estimate the distribution by the standard Johnson system of laws. Finally, we search for a more general distribution of Johnson type, using a non-parametric approach.

Findings – We use data from the indexes Credit Suisse/Tremont Hedge Fund (CSFB/Tremont) provided by Credit Suisse. For the parametric approach, we find that the SU Johnson distribution is the most appropriate, except for the Managed Futures. For the non-parametric approach, we determine the best polynomial approximation of the function characterizing the transformation from the initial Gaussian law to the generalized Johnson distribution.

Originality/value of chapter – These findings are novel since we use an extension of the Johnson distributions to better fit non-Gaussian distributions, in particular in the case of hedge fund returns. We illustrate the power of this methodology that can be further developed in the multidimensional case.

Statistical inference (estimation and testing) for the stochastic volatility (SV) model Taylor (1982, 1986) is challenging, especially likelihood-based methods which are difficult to apply due to the presence of latent variables. The existing methods are either computationally costly and/or inefficient. In this paper, we propose computationally simple estimators for the SV model, which are at the same time highly efficient. The proposed class of estimators uses a small number of moment equations derived from an ARMA representation associated with the SV model, along with the possibility of using “winsorization” to improve stability and efficiency. We call these ARMA-SV estimators. Closed-form expressions for ARMA-SV estimators are obtained, and no numerical optimization procedure or choice of initial parameter values is required. The asymptotic distributional theory of the proposed estimators is studied. Due to their computational simplicity, the ARMA-SV estimators allow one to make reliable – even exact – simulation-based inference, through the application of Monte Carlo (MC) test or bootstrap methods. We compare them in a simulation experiment with a wide array of alternative estimation methods, in terms of bias, root mean square error and computation time. In addition to confirming the enormous computational advantage of the proposed estimators, the results show that ARMA-SV estimators match (or exceed) alternative estimators in terms of precision, including the widely used Bayesian estimator. The proposed methods are applied to daily observations on the returns for three major stock prices (Coca-Cola, Walmart, Ford) and the S&P Composite Price Index (2000–2017). The results confirm the presence of stochastic volatility with strong persistence.

Empirical evidence shows that banking development has a significant impact on macro-level economic growth through the finance-growth nexus and also highlights the prominent effect of development on local economy and household welfare, particularly in developing countries with restricted access to financial systems. The authors investigated the role of local banking development in affecting household welfare in Thailand which is a modest degree of financial access compare to other countries. The authors focus on the development of the banking sector in four dimensions, including financial depth, financial stability, financial efficiency and financial inclusion, and its impact on household welfare using the generalized method of moments approach to address the endogeneity problem. The authors employ biennial household welfare data from the National Statistical Office survey from 2007 to 2019 which covers all provinces in Thailand. The findings suggest that each type of banking development significantly affects household income and consumption in Thailand, although in different ways. Financial depth decreases income and consumption expenditure, while financial inclusion increases income and consumption expenditure (level effect). However, there are insignificant impacts on volatility of household income and consumption (volatility effect). Our findings prove that the implementation of policies to promote banking development either promote or decrease household welfare. This study can provide insight on policy impact and assist policymakers in considering the adoption of banking development policies to promote growth of the local economy, while at the same time aiming to reduce welfare inequality.

The chapter empirically investigates the effects of tariff imposition on manufacturing trade comparatively in the north and south economies across the globe during the last three decades. Traditionally north and south represent the developed and developing world, respectively. Along with the volume and balance of trade, the study accounts for both export and import separately to observe their dynamisms under the tariff regime and makes comparisons between developing and developed groups of countries. Using World Development Indicators (2019) and World Integrated Trade Solutions (2019) databases on 77 developing and 48 developed nations for 1991–2018, the robust difference panel generalized method of moments estimates imply that impositions of domestic tariffs significantly reduce manufacturing trade in both groups of countries; however, developing countries experience this effect in a greater extent.

The major objective of this study is to inspect the differences in the effect of derivatives on the stability between banks from emerging countries and those from recently developed countries.

According to the repercussions of the recent financial crisis, we divide the whole period into normal period “the pre-crisis period,” 2003–2006, and turbulent period “the crisis & post-crisis period,” 2007–2011. We use the Generalized Methods of Moments (GMM) estimator technique developed by Blundell and Bond (1998) to estimate our regressions.

Our main conclusions show that, in general, using derivatives by banks from emerging countries deteriorates their stability especially during the turbulent period, whereas, using derivatives do not weaken the stability of banks from recently developed countries. We deduce that banks from emerging countries are more destabilized by using derivatives than banks from recently developed countries.