MODELING ADVERTISING CARRYOVER IN FLUID MILK: COMPARISON OF ALTERNATIVE LAG SPECIFICATIONS

The performance of restricted estimators such as Almon and Shiller in modeling advertising carryover is tested and compared to the unrestricted OLS estimator, using 1971-1988 monthly New York City fluid milk market data. Results indicate that in the absence of autocorrelation and multicollinearity among the lagged advertising variables, the unrestricted OLS estimator is still the preferred estimator, based on Mean Square Error and Root Mean Square Percent Error criteria. In this case, the Almon and Shiller estimators perform equally well, although next only to the OLS estimator. In the presence of autocorrelation or multicollinearity however, the restricted estimators may outperform the OLS estimator, in a MSE sense, with the flexible Shiller estimator (which subsumes the Almon) being more desirable.Marketing,

Cancerphobia: Electromagnetic Fields and Their Impact on Residential Loan Values

This article provides a matrix representation of the adjustment grid estimator. From this representation, one can invoke the Gauss-Mrkov theorem to examine the efficiency of ordinary least squares (OLS) and the grid estimator that uses OLS estimates of the adjustments (the "plug-in" grid method). In addition, this matrix representation suggests a generalized least squares version of the grid method, labeled herin as the total grid estimator. Based on the empirical experiments, the total grid estimator outperformed the plug-in grid estimator, which in turn outperformed the OLS.

Cointegrating Polynomial Regressions

This paper develops a fully modified OLS estimator for cointegrating polynomial regressions, i.e. for regressions including deterministic variables, integrated processes and powers of integrated processes as explanatory variables and stationary errors. The errors are allowed to be serially correlated and the regressors are allowed to be endogenous. The paper thus extends the fully modified approach developed in Phillips and Hansen (1990). The FM-OLS estimator has a zero mean Gaussian mixture limiting distribution, which is the basis for standard asymptotic inference. In addition Wald and LM tests for specification as well as a KPSS-type test for cointegration are derived. The theoretical analysis is complemented by a simulation study which shows that the developed FM-OLS estimator and tests based upon it perform well in the sense that the performance advantages over OLS are by and large similar to the performance advantages of FM-OLS over OLS in cointegrating regressions.Cointegrating polynomial regression, fully modified OLS estimation, integrated process, testing

A NOTE ON SPURIOUS REGRESSION IN PANELS WITH CROSS-SECTION DEPENDENCE

This paper analyses regression of two independent stationary panels with cross-sectional dependence. It is shown that the pooling least squares (PLS) estimator converges to zero in probability while the individual OLS estimator converges to a random variable. However, the PLS-based and the OLS-based t-statistics diverge, so the null hypothesis of no correlation tends to be spuriously rejected.Research Methods/ Statistical Methods,

Local GMM Estimation of Time Series Models with Conditional Moment Restrictions

This paper investigates statistical properties of the local GMM (LGMM) estimator for some time series models defined by conditional moment restrictions. First, we consider Markov processes with possible conditional heteroskedasticity of unknown form and establish the consistency, asymptotic normality, and semi-parametric efficiency of the estimator. Second, inspired by simulation results showing that the LGMM estimator has a significantly smaller bias than the OLS estimator, we undertake a higher-order asymptotic expansion and analyze the bias properties of the LGMM estimator. The structure of the asymptotic expansion of the LGMM estimator reveals an interesting contrast with the OLS estimator that helps to explain the bias reduction in the LGMM estimator. The practical importance of these findings is evaluated in terms of a bond and option pricing exercise based on a diffusion model for spot interest rate.Conditional moment restrictions; Local GMM; Higher-order expansion; Conditional heteroskedasticity

Zipfs Law for Cities: A Cross Country Investigation

This paper assesses the empirical validity of Zipf¿s Law for cities, using new data on 73countries and two estimation methods ¿ OLS and the Hill estimator. With either estimator,we reject Zipf¿s Law far more often than we would expect based on random chance; for 53out of 73 countries using OLS, and for 30 out of 73 countries using the Hill estimator. TheOLS estimates of the Pareto exponent are roughly normally distributed, but those of the Hillestimator are bimodal. Variations in the value of the Pareto exponent are better explained bypolitical economy variables than by economic geography variables.Cities, Zipf¿s Law, Pareto distribution, Hill estimator

New Wine in Old Bottles: A Sequential Estimation Technique for the LPM

The conditions under which ordinary least squares (OLS) is an unbiased and consistent estimator of the linear probability model (LPM) are unlikely to hold in many instances. Yet the LPM still may be the correct model or, perhaps, justified for practical reasons. A sequential least squares (SLS) esti-mation procedure is introduced that may outperform OLS in terms of finite sample bias and yields a consistent estimator. Monte Carlo simulations reveal that SLS outperforms OLS, probit and logit in terms of mean squared error of the predicted probabilities. An empirical example is provided.Linear Probability Model, Sequential Least Squares, Consistency, Monte Carlo

The Role of "Leads" in the Dynamic OLS Estimation of Cointegrating Regression Models

In this paper, we consider the role of "leads" of the first difference of integrated variables in the dynamic OLS estimation of cointegrating regression models. We demonstrate that the role of leads is related to the concept of Granger causality and that in some cases leads are unnecessary in the dynamic OLS estimation of cointegrating regression models. Based on a Monte Carlo simulation, we find that the dynamic OLS estimator without leads substantially outperforms that with leads and lags; we therefore recommend testing for Granger noncausality before estimating models.Cointegration, dynamic ordinary least squares estimator, Granger causality