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This paper uses the capital asset pricing model to show that, in realistic circumstances, double taxation and differential tax rates on personal and capital-gains income affect corporate stock values and financial policies in non-neutral ways. This non-neutrality holds whenever inflation is uncertain and tax-avoidance activity is neither costless nor riskless. The model also allows us to explore how a series of frequently proposed changes in the interplay of corporate and personal taxes would affect corporate dividend payouts and debt usage. Our analysis clarifies that conscientious efforts to integrate corporate and personal tax rates must make supporting changes in the size and character of capital-gains tax preferences built into the tax code.

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.

In the context of competing theoretical economic–econometric models and corresponding estimators, we demonstrate a semiparametric combining estimator that, under quadratic loss, has superior risk performance. The method eliminates the need for pretesting to decide between members of the relevant family of econometric models and demonstrates, under quadratic loss, the nonoptimality of the conventional pretest estimator. First-order asymptotic properties of the combined estimator are demonstrated. A sampling study is used to illustrate finite sample performance over a range of econometric model sampling designs that includes performance relative to a Hausman-type model selection pretest estimator. An important empirical problem from the causal effects literature is analyzed to indicate the applicability and econometric implications of the methodology. This combining estimation and inference framework can be extended to a range of models and corresponding estimators. The combining estimator is novel in that it provides directly minimum quadratic loss solutions.

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.

We survey the literature on the Risk Augmented Mincer equation that seeks to estimate the compensation for uncertainty in the future wage to be earned after completing an education. There is wide empirical support for the predicted positive effect of wage variance and the negative effect of wage skew. We discuss robustness of the findings across specifications, potential bias from unobserved heterogeneity and selectivity and consider the core issue of students' information on benefits from education.

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.

The notion that asset diversification reduces risk is ancient and can be traced as far back as the Talmud which states, “A man should always keep his wealth in three forms: one-third in real estate, another in merchandise, and the remainder in liquid assets” (Baba Metzia, verse 42a). Somewhat more recently, in 1738, Daniel Bernoulli observed, “it is advisable to divide goods which are exposed to some small danger into several small portions rather than to risk them all together” (1738/1954, p. 30). Arguably, however, it was not until 1935 that the future Nobel laureate J. R. Hicks offered some early direction for modern portfolio theory. Although his research was more concerned with explaining the demand for money, he points out two important considerations for modeling risk. Hicks writes, “The risk factor comes into our problem in two ways: First, as affecting the expected period of investment, and second, as affecting the expected net yield of investment” (Hicks, 1935, p. 7). Regarding Hicks' first point, both Markowitz (1952) and Roy (1952) emplace their analyses in a one-period investment horizon. Second, and even more relevant to modern portfolio theory, is Hicks' suggestion of using an expected value calculated with subjective probabilities. Hicks continues, “It is convenient to represent these probabilities to oneself, in statistical fashion, by a mean value, and some measure of dispersion” (1935, p. 8). Clearly, Hicks comes very close to articulating a mean–variance solution. Crucially, and unlike Roy or Markowitz, Hicks does not develop this line of reasoning nor does he suggest the particular use of variance or standard deviation as that measure of risk. Nonetheless, Hicks' suggestion anticipates the work of Markowitz and Roy.¹