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This paper examines a nonparametric CUSUM-type test for common trends in large panel data sets with individual fixed effects. We consider, as in Zhang, Su, and Phillips (2012), a partial linear regression model with unknown functional form for the trend component, although our test does not involve local smoothings. This conveniently forgoes the need to choose a bandwidth parameter, which due to a lack of a clear and sensible information criteria is difficult for testing purposes. We are able to do so after making use that the number of individuals increases with no limit. After removing the parametric component of the model, when the errors are homoscedastic, our test statistic converges to a Gaussian process whose critical values are easily tabulated. We also examine the consequences of having heteroscedasticity as well as discussing the problem of how to compute valid critical values due to the very complicated covariance structure of the limiting process. Finally, we present a small Monte Carlo experiment to shed some light on the finite sample performance of the test.

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

This paper analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

Our chapter raises serious questions about the long-term efficiency of stock prices in relation to the realized returns of the underlying corporate real assets. In our large-scale calculations that cover horizons of 10, 20, 30, 40, and 50 years, returns on corporate real assets suffer a long-term decline, and have been below the yields of 10-year Treasury bonds since 1973. Real assets that received more external financing from capital markets and institutions actually report even lower realized long-term returns. The decline in realized returns cannot be attributed to declining risks as the volatilities of realized returns have been increasing over time. These surprising results may stimulate fresh debate on the roles and long-term performance of capital markets and institutions.

Vintage capital growth models have been at the heart of growth theory in the 1960s. This research line collapsed in the late 1960s with the so-called embodiment controversy and the technical sophisitication of the vintage models. This chapter analyzes the astonishing revival of this literature in the 1990s. In particular, it outlines three methodological breakthroughs explaining this resurgence: a growth accounting revolution, taking advantage of the availability of new time series; an optimal control revolution, allowing to safely study vintage capital optimal growth models; and a vintage human capital revolution, along with the rise of economic demography, accounting for the vintage structure of human capital similarly to physical capital age structuring. The related literature is surveyed.

The railway track system is the platform by which loads from moving trains are transferred to the underlying soil or supporting infrastructure such as bridges. The most common type of railway track system is ballasted track, which has been in use for over a century. Ballasted track has proved versatile. It can be constructed using locally available materials and with modifications to the rails and sleepers, crossings transferring trains from one route to another can be created. The structure of a ballasted track system consists of two main parts. The upper portion, termed the superstructure, comprises the rails, fastenings and sleepers. It is formed of components whose shape, stiffness and strength are designed and closely controlled. Below the superstructure is the substructure, which comprises the ballast and sub-ballast. Although the materials used in the substructure may have been specified, their engineering properties and geometric placement are less well controlled. In this chapter, we will explore how a typical ballasted track system transfers load to the ground and the ways in which the track form deteriorates, requiring maintenance and eventually renewal.