We develop a new consistent conditional moment test of functional form based on nuisance parameter indexed sample moments. We reduce the nuisance parameter space to known countable sets, provide a new vantage into why existing parametric moment condition tests work, and uncover a new class of revealing weights. These results are exploited to construct a weighted average conditional moment test,...

There are a large number of tests for instability or breaks in coefficients in regression models designed for different possible departures from the stable model. We make two contributions to this literature. First, we consider a large class of persistent breaking processes that lead to asymptotically equivalent efficient tests. Our class allows for many or relatively few breaks, clustered brea...

We consider an exact factor model with the restriction of unobservable common stochastic trends imposed by non-stationary factors as considered by Zhou and Solberger (2012). Conditional on this, we propose a homogenous likelihood ratio test for unit roots in the idiosyncratic components. The likelihood approach has long been overlooked in this framework due to numerical burdens, and though part...

We propose tests for structural change in conditional distributions via quantile regressions. To avoid misspecification on the conditioning relationship, we construct the tests based on the residuals from local polynomial quantile regressions. In particular, the tests are based upon the cumulative sums of generalized residuals from quantile regressions and have power against local alternatives ...

This chapter provides an overview of pseudo-out-of-sample tests of unconditional predictive ability. We begin by providing an overview of the literature, including both empirical applications and theoretical contributions. We then delineate two distinct methodologies for conducting inference: one based on the analytics in West (1996) and the other based on those in Giacomini and White (2006). T...

Numerous authors have suggested that the price-earnings (P/E) ratio can be used to predict the future movement of stock prices. Such arguments are based on the belief that P/E ratios are mean-reverting. But are the S&P P/E ratios really mean reverting? A review of the literature finds arguments on both sides, but the issue of mean reversion has not been tested adequately. Using unit roots and m...

We consider statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and state-year di erences-in-di erences studies with clustering on state. In such settings default standard erro...

This paper proposes a variant of the classical HAUSMAN specifi cation test commonly employed to decide whether the estimation of a random-eff ects model is a viable alternative to estimating fi xed eff ects. Whereas the classical test probes the equality of fi xedand random eff ects, the proposed variant focuses on the equality of between-groups and fi xed-eff ects coeffi cients. While both tes...

In the absence of uniformly most powerful (UMP) tests or uniformly most powerful invariant (UMPI) tests, King (1987c) suggested the use of Point Optimal (PO) tests, which are most powerful at a chosen point under the alternative hypothesis. This paper surveys the literature and major developments on point optimal testing since 1987 and suggests some areas for future research. Topics include tes...

Within a flexible parametric regression framework (Hamilton, 2001) we provide further evidence on the existence of a nonlinear component in the quarterly growth rate of the US real GNP. We implement a battery of new tests for neglected nonlinearity based on the theory of random fields (Dahl and González-Rivera, 2003). We find that the nonlinear component is driven by the fifth lag of the growth...