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We perform a comprehensive Monte Carlo simulation analysis of a variant of the nonstationary continuous-time stochastic growth model with Cobb–Douglas technology developed in Feicht and Stummer (2010), where for every (short-term, middle-term, long-term) time horizon the corresponding dynamic transitional sample path values were derived explicitly, that is, in closed form.

In particular, we study how much the outcoming (e.g., German empirical data adjusted) economy values are affected by changes of the involved economically meaningful parameters. Furthermore, we obtain realistically low savings rates, as well as a reasonably fast speed of recovery in situations where the abovementioned model economy is suddenly and considerably disturbed by a “crash” (macroeconomic disaster).

The purpose of this paper is to study the problem of optimal Ramsey taxation in a finite-planning-horizon, representative-agent endogenous growth model including government expenditures as a productive input in capital formation and also with hidden actions.

Technically, Malliavin calculus and forward integrals are naturally introduced into the macroeconomic theory when economic agents are faced with different information structures arising from a non-Markovian environment.

The major result shows that the well-known Judd-Chamley Theorem holds almost surely if the depreciation rate is strictly positive, otherwise Judd-Chamley Theorem only holds for a knife-edge case or on a Lebesgue measure-zero set when the physical capital is completely sustainable.

The author believes that the approach developed as well as the major result established is new and relevant.

In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

The topic of volatility measurement and estimation is central to financial and more generally time-series econometrics. In this chapter, we begin by surveying models of volatility, both discrete and continuous, and then we summarize some selected empirical findings from the literature. In particular, in the first sections of this chapter, we discuss important developments in volatility models, with focus on time-varying and stochastic volatility as well as nonparametric volatility estimation. The models discussed share the common feature that volatilities are unobserved and belong to the class of missing variables. We then provide empirical evidence on “small” and “large” jumps from the perspective of their contribution to overall realized variation, using high-frequency price return data on 25 stocks in the DOW 30. Our “small” and “large” jump variations are constructed at three truncation levels, using extant methodology of Barndorff-Nielsen and Shephard (2006), Andersen, Bollerslev, and Diebold (2007), and Aït-Sahalia and Jacod (2009a, 2009b, 2009c). Evidence of jumps is found in around 22.8% of the days during the 1993–2000 period, much higher than the corresponding figure of 9.4% during the 2001–2008 period. Although the overall role of jumps is lessening, the role of large jumps has not decreased, and indeed, the relative role of large jumps, as a proportion of overall jumps, has actually increased in the 2000s.

We evaluate the performance of fixed income mutual funds using stochastic discount factors motivated by continuous-time term structure models. Time-aggregation of these models for discrete returns generates new empirical “factors,” and these factors contribute significant explanatory power to the models. We provide a conditional performance evaluation for US fixed income mutual funds, conditioning on a variety of discrete ex-ante characterizations of the states of the economy. During 1985–1999 we find that fixed income funds return less on average than passive benchmarks that do not pay expenses, but not in all economic states. Fixed income funds typically do poorly when short-term interest rates or industrial capacity utilization rates are high, and offer higher returns when quality-related credit spreads are high. We find more heterogeneity across fund styles than across characteristics-based fund groups. Mortgage funds underperform a GNMA index in all economic states. These excess returns are reduced, and typically become insignificant, when we adjust for risk using the models.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

The internal factors that influence the decision to change dividend growth rates include two competing models: the earnings and free cash flow models. As far as each of the components of each model is considered, the informative and efficient dividend payout decisions require that managers have to focus on the significant component(s) only. This study examines the cointegration, significance, and explanatory power of those components empirically. The expected outcomes serve two objectives. First, on an academic level, it is interesting to examine the extent to which payout practices meet the premises of the earnings and free cash flow models. The latter considers dividends and financing decisions as two faces of the same coin. Second, on a professional level, the outcomes help focus the management’s efforts on the activities that can be performed when considering a change in dividend growth rates.

This study uses data for the firms listed in two indexes: Dow Jones Industrial Average (DJIA30) and NASDAQ100. The data cover quarterly periods from 30 June 1989 to 31 March 2011. The methodology includes (a) cointegration analysis in order to test for model specification and (b) classical regression in order to examine the explanatory power of the components of earnings and free cash flow models.

The results conclude that: (a) Dividends growth rates are cointegrated with the two models significantly; (b) Dividend growth rates are significantly and positively associated with growth in sales and cost of goods sold only. Accordingly, these are the two activities that firms’ management need to focus on when considering a decision to change dividend growth rates, (c) The components of the earnings and free cash flow models explain very little of the variations in dividends growth rates. The results are to be considered a call for further research on the external (market-level) determinants that explain the variations in dividends growth rates. Forthcoming research must separate the effects of firm-level and market-level in order to reach clear judgments on the determinants of dividends growth rates.

This study contributes to the related literature in terms of offering updated robust empirical evidence that the decision to change dividend growth rate is discretionary to a large extent. That is, dividend decisions do not match the propositions of the earnings and free cash flow models entirely. In addition, the results offer solid evidence that financing trends in the period 1989–2011 showed heavy dependence on debt financing compared to other related studies that showed heavy dependence on equity financing during the previous period 1974–1984.