Spectral Density Bandwidth Choice and Prewhitening in the Generalized Method of Moments Estimators for the Asset Pricing Model

This paper investigates the performances of GMM estimates using kernel methods with and without prewhitening and the VARHAC method in a representative agent exchange economy. A Monte Carlo study is conducted to evaluate the issues of estimating the spectral density functions, e.g., parametric vs. nonparametric, data-based bandwidth selection, and prewhitening procedures. The Monte Carlo results show that kernel methods with prewhitening procedure outperform others in terms of statistical inferences. The deviations from true parameter values, however, are larger for kernel methods with prewhitening procedure. Therefore, there exists efficiency/bias trade-off when choosing HAC covariance estimation method.Asset Pricing

Panel Cointegration with Global Stochastic Trends

This paper studies estimation of panel cointegration models with cross-sectional dependence generated by unobserved global stochastic trends. The standard least squares estimator is, in general, inconsistent owing to the spuriousness induced by the unobservable I(1) trends. We propose two iterative procedures that jointly estimate the slope parameters and the stochastic trends. The resulting estimators are referred to respectively as CupBC (continuously-updated and bias-corrected) and the CupFM (continuously-updated and fully-modified) estimators. We establish their consistency and derive their limiting distributions. Both are asymptotically unbiased and asymptotically mixed normal and permit inference to be conducted using standard test statistics. The estimators are also valid when there are mixed stationary and non-stationary factors, as well as when the factors are all stationary

Asymptotic Inference in Censored Regression Models Revisited

This paper establishes that regressors in the models with censored dependent variables need not be bounded for the standard asymptotic results to apply. Thus, regressors that grow monotonically with the observation index may be acceptable. It also purports to provide an upper bound on the rate at which regressors may grow

Consistent Estimation with Weak Instruments in Panel Data

This note analyzes the asymptotic distribution for instrumental variables regression for panel data when the available instruments are weak. We show that consistency can be established in panel data

A Monte Carlo Comparison of Tests for Cointegration in Panel Data

This paper surveys recent developments and provides Monte Carlo comparison on various tests proposed for cointegration in panel data. In particular, tests for two panel models, varying intercepts and varying slopes, and varying intercepts and common slopes are presented from the literature with a total of seven tests being simulated. In all cases, results on empirical size and size-adjusted power are given

Testing for structural change in panel data: GDP growth, consumption growth, and productivity growth

In this paper we first estimate the growth rates of real per capita GDP, real per capita Consumption, and Productivity (real GDP per worker) for the following panels of countries: (1) OPEC countries, (2) industrialized countries, and (3) based on geographic location. We then test for a structural change in the growth rates for each group and also attempt to identify the change point for each group. If there is a significant change, then the growth rates are estimated before and after the break for comparison. It is found that industrial countries, in general, experienced slowdowns in growth in the early 1970s, whereas less developed countries also experienced slowdowns in growth, but the timing of the slowdowns was in the mid to late 1970s.

Simulated Maximum Likelihood Estimation of the Linear Expenditure System with Binding Non-Negativity Constraints

This paper discusses issues on the estimation of consumer demand equations subject to binding non-negative constraints. We propose computationally feasible specifications and a simulated maximum likelihood (SML) method for demand systems. Our study shows that the econometric implementation of the SML estimates can avoid high-dimensional integration problems. As contrary to the simulation method of moments and simulated pseudo-likelihood methods that require the simulation of demand quantities subject to nonnegativity constraints for consumers in the sample, the SML approach requires only simulation of the likelihood function. The SML approach avoids solving for simulated demand quantities because the likelihood function is conditional on observed demand quantities. We have applied SML approach for the linear expenditure system (LES) with non-negativity constraints. The results of a seven-goods demand system are presented. The results provide empirical evidence on the importance of taking into account possible cross equation correlations in disturbances.Simulated likelihood, Linear expenditure system, Non-negativity constraints, Multivariate censored variables, Nonlinear simultaneous equations

Wavelet-Based Testing for Serial Correlation of Unknown Form in Panel Models

Wavelet analysis is a new mathematical tool developed as a unified field of science over the last decade. As spatially adaptive analytic tools, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent/strong dependence, seasonality or use of seasonal data such as quarterly and monthly data, business cycles, and other kinds of periodicity. This paper proposes a new class of wavelet-based tests for serial correlation of unknown form in the estimated residuals of an error component model, where the error components can be one-way or two-way, the individual and time effects can be fixed or random, the regressors may contain lagged dependent variables or deterministic/stochastic trending variables. The proposed tests are applicable to unbalanced heterogeneous panel data. They have a convenient null limit N (0,1) distribution. No formulation of an alternative is required, and the tests are consistent against serial correlation of unknown form. We propose and justify a data-driven finest scale that, in an automatic manner, converges to zero under the null hypothesis of no serial correlation and grows to infinity as the sample size increases under the alternative, ensuring the consistency of the proposed tests. Simulation studies show that the new tests perform rather well in small and finite samples in comparison with some existing popular tests for panel models, and can be used as an effective evaluation procedure for panel models. KEY WORD: error component, panel model, hypothesis testing, serial correlation of unknown form, spectral peak, unbalanced panel data, wavelet

Detecting Neglected Nonlinearity in Dynamic Panel Data with Time-Varying Conditional Heteroskedasticity

A new class of specification tests is proposed to detect for neglected nonlinearity and dynamic misspecification in panel models. The tests can detect a wide range of model misspecifications while being robust to conditional heteroskedasticity and higher order time-varying moments of unknown form. They check a large number of lags so that they can capture dynamic misspecification at any lag order. The large number of lag orders does not cause loss of degrees of freedom because our tests naturally discount higher order lags, which is consistent with the stylized fact that economic behaviors are more affected by recent past events than by remote past events. No specific estimation method is required, and the tests have the appealing "nuisance parameter free" property that parameter estimation uncertainty has no impact on the limit distribution of the test. Simulations show the proposed tests have good finite sample properties. It is important to take into account conditional heteroskedasticity; failure to do so will cause overrejection of a correct linear panel model. Our tests have omnibus and robust power against a variety of alternatives relative to some existing tests for linearity in panel modelsConditional heteroskedasticity, Dynamic panel Model, Generalized spectral derivative, Hausman's test, Joint limit asymptotics, Linearity, Martingale.