Lynda Khalaf

Inflation dynamics and the New Keynesian Phillips Curve: An identification robust econometric analysis

In this paper, we use identification-robust methods to assess the empirical adequacy of a New Keynesian Phillips Curve (NKPC) equation. We focus on the Gali and Gertler’s (1999) specification, on both U.S. and Canadian data. Two variants of the model are studied: one based on a rationalexpectations assumption, and a modification to the latter which consists in using survey data on inflation expectations. The results based on these two specifications exhibit sharp differences concerning: (i) identification difficulties, (ii) backward-looking behavior, and (ii) the frequency of price adjustments. Overall, we find that there is some support for the hybrid NKPC for the U.S., whereas the model is not suited to Canada. Our findings underscore the need for employing identificationrobust inference methods in the estimation of expectations-based dynamic macroeconomic relations.

Simulation‐Based Finite Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

Multivariate Tests of Mean–Variance Efficiency With Possibly Non-Gaussian Errors

We develop exact mean–variance efficiency tests of the market portfolio in the context of (conditional and unconditional) capital asset pricing models (CAPM), allowing for a wide class of possibly non-Gaussian error distributions. The proposed procedures are applicable in a general multivariate linear regression framework, and exactness is achieved through Monte Carlo test techniques. We also perform exact multivariate diagnostic checks. Empirical results show that the Gaussian assumption is rejected, temporal instabilities are apparent, and mean–variance efficiency is rejected over several subperiods, but finite-sample methods that allow for nonnormality and conditioning information substantially reduce the number of rejections.

On Jumps and ARCH Effects in Natural Resource Prices: An Application to Pacific Northwest Stumpage Prices

Continuous-time models of natural resource prices usually preclude the possibility of large changes (jumps) resulting from unexpected events. To test for the presence of jumps and/or ARCH effects, we combine bounds and the Monte Carlo test technique to obtain finite-sample, level-exact p-values. We apply this methodology to stumpage prices from the Pacific Northwest and find evidence of jumps and ARCH effects. To assess the impact of neglecting jumps on the decision to harvest old-growth timber, we develop an autonomous, infinite-horizon stopping model for which we provide a new method of resolution. Our numerical results show the importance of modeling jumps explicitly. Copyright 2002, Oxford University Press.

Identification-Robust Estimation and Testing of the Zero-Beta CAPM

We propose exact simulation-based procedures for: (i) testing mean-variance efficiency when the zero-beta rate is unknown, and (ii) building confidence intervals for the zero-beta rate. On observing that this parameter may be weakly identified, we propose LR-type statistics as well as heteroskedascity and autocorrelation corrected (HAC) Wald-type procedures, which are robust to weak identification and allow for non-Gaussian distributions including parametric GARCH structures. In particular, we propose confidence sets for the zero-beta rate based on inverting exact tests for this parameter; these sets provide a multivariate extension of Fiellers technique for inference on ratios. The exact distribution of LR-type statistics for testing efficiency is studied under both the null and the alternative hypotheses. The relevant nuisance parameter structure is established and finite-sample bound procedures are proposed, which extend and improve available Gaussianspecific bounds. Furthermore, we study the invariance to portfolio repacking property for tests and confidence sets proposed. The statistical properties of available and proposed methods are analyzed via aMonte Carlo study. Empirical results on NYSE returns show that exact confidence sets are very different from the asymptotic ones, and allowing for non-Gaussian distributions affects inference results. Simulation and empirical results suggest that LR-type statistics - with p-values corrected using the Maximized Monte Carlo test method - are generally preferable to their Wald-HAC counterparts from the viewpoints of size control and power.

The Environmental Kuznets Curve: Tipping Points, Uncertainty and Weak Identification

We consider an empirical estimation of the Environmental Kuznets Curve (EKC) for carbon dioxide and sulphur, with a focus on confidence set estimation of the tipping point. Various econometric – parametric and nonparametric – methods are considered, reflecting the implications of persistence, endogeneity, the necessity of breaking down our panel regionally, and the small number of countries within each panel. In particular, we propose an inference method that corrects for potential weak-identification of the tipping point. Weak identification may occur if the true EKC is linear while a quadratic income term is nevertheless imposed into the estimated equation. Relevant literature to date confirms that non-linearity of the EKC is indeed not granted, which provides the motivation for our work. Viewed collectively, our results confirm an inverted U-shaped EKC in the OECD countries but generally not elsewhere, although a local-pollutant analysis suggest favorable exceptions beyond the OECD. Our measures of uncertainty confirm that it is difficult to identify economically plausible tipping points. Policy-relevant estimates of the tipping point can nevertheless be recovered from a local-pollutant long-run or non-parametric perspective.

Finite sample multivariate tests of asset pricing models with coskewness

Exact inference methods are proposed for asset pricing models with unobservable risk-free rates and coskewness; specifically, the Quadratic Market Model (QMM) which incorporates the effect of asymmetry of return distribution on asset valuation. In this context, exact tests are appealing given (i) the increasing popularity of such models in finance, (ii) the fact that traditional market models (which assume that asset returns move proportionally to the market) have not fared well in empirical tests, (iii) finite sample QMM tests are unavailable even with Gaussian errors. Empirical models are considered where the procedure to assess the significance of coskewness preference is LR-based, and relates to the statistical and econometric literature on dimensionality tests which are interesting in their own right. Exact versions of these tests are obtained, allowing for non-normality of fundamentals. A simulation study documents the size and power properties of asymptotic and finite sample tests. Empirical results with well-known data sets reveal temporal instabilities over the full sampling period, namely 1961-2000, though tests fail to reject the QMM restrictions over 5-year sub-periods.

A cross‐section analysis of financial market integration in North America using a four factor model

Purpose - The purpose of this paper is to examine financial integration across North American stock markets from January 1984 to December 2003. Design/methodology/approach - The paper uses an arbitrage pricing theory framework. The risk factors considered are the three Fama and French factors augmented with momentum for both countries as well as their international counterparts. Both the domestic and international four factor models in cross section and test for partial, mild, and strong financial integration are estimated. The domestic and international model are estimated on domestic portfolios and on a subset of Canadian cross listings matched with American stocks. Findings - Results can be summarized as follows: first, results show stronger evidence of mild rather than partial or strong integration in both domestic portfolios and interlisted stocks. Second, interlisted stocks appear at first glance to be more integrated than the domestic portfolios, but this result can be attributed to the poor explanatory power of the models applied to interlisted stocks. Once the authors rule out the case where the model does not generate statistically important risk premiums for both countries, the evidence of integration is similar in both domestic and interlisted stocks. Third, the domestic and international models have similar explanatory power, although the domestic model performs better with the Canadian interlisted stocks are found. Originality/value - The results suggest that, in an international context, a portfolio manager is better off using the four factor model as a benchmark in cross sections rather than the single market. Furthermore, if the agency problem described in Karolyi is ignored, Canadian interlisted stocks and Canadian domestic portfolios have the same diversification potential.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

An Identification-Robust Test for Time-Varying Parameters in the Dynamics of Energy Prices

We test for the presence of time-varying parameters (TVP) in the long-run dynamics of energy prices for oil, natural gas and coal, within a standard class of mean-reverting models. We also propose residual-based diagnostic tests and examine out-of-sample forecasts. In-sample LR tests support the TVP model for coal and gas but not for oil, though companion diagnostics suggest that the model is too restrictive to conclusively fit the data. Out-of-sample analysis suggests a randomwalk specification for oil price, and TVP models for both real-time forecasting in the case of gas and long-run forecasting in the case of coal