P.A.V.B. Swamy

THE NEW KEYNESIAN PHILLIPS CURVE IN A TIME-VARYING COEFFICIENT ENVIRONMENT: SOME EUROPEAN EVIDENCE

We examine whether the importance of lagged inflation in the New Keynesian Phillips Curve (NKPC) may be a result of specification biases. NKPCs are estimated for four countries: France, Germany, Italy, and the United Kingdom. Using time-varying coefficient (TVC) estimation, a procedure that can deal with possible specification biases, we find support for the NKPC model that excludes lagged inflation. Our results indicate a Phillips-curve relationship for the countries considered that does not contain an inertial element.

Correcting for Omitted-Variable and Measurement-Error Bias in Autoregressive Model Estimation with Panel Data

The parameter estimates based on an econometric equation are biased and can also be inconsistent when relevant regressors are omitted from the equation or when included regressors are measured with error. This problem gets complicated when the `true functional form of the equation is unknown. Here, we demonstrate how auxiliary variables, called concomitants, can be used to remove omitted-variable and measurement-error biases from the coefficients of an equation with the unknown `true functional form. The method is specifically designed for panel data. Numerical algorithms for enacting this procedure are presented and an illustration is given using a practical example of forecasting small-area employment from nonlinear autoregressive models.

How stable are monetary policy rules: estimating the time-varying coefficients in monetary policy reaction function for the US

The relation among the federal funds rate and the Federal Reserves expectations about future values of certain policy relevant variables is considered. The coefficients of this relation are biased (i) when relevant explanatory variables are omitted, (ii) when the included explanatory variables are measured with error, (iii) when the functional form of the relation is misspecified. These biases present obstacles to verifying the conditions for monetary policies to be effective. It is explained how auxiliary variables, called concomitants, can be used to remove some of these biases without assuming that the true functional form of the relation is known. An analysis of the US quarterly data on the variables in a reaction function for 1960Q1-2000Q4 is given using our methods with a description of a numerical algorithm for enacting our methods.

Empirical best linear unbiased prediction in misspecified and improved panel data models with an application to gasoline demand

We emphasize using our solutions to the problems of omitted variables, measurement errors, and unknown functional forms to improve model specification, and to estimate the mean square error of an empirical best linear unbiased predictor of an individual drawing of the dependent variable of an improved model. We illustrate using data to compare the forecasting performances of misspecified and improved models of the U.S. gasoline market. The performance criterion used is the tightness of the distribution of the absolute relative errors in out-of-sample multi-step-ahead forecasts around zero. The results show that significant improvements in forecasting accuracy can be obtained by improving the specifications of misspecified models. Numerical algorithms for generating forecasts from a rolling forecast method are presented

An efficient method of estimating the true value of a population characteristic from its discrepant estimates

A fruitful method of pooling data from disparate sources, such as a set of sample surveys, is developed. This method proceeds by finding the first two moments of two conditional distributions derived from a joint distribution of two sample estimators of employment for each of several geographical areas. The nature of the two estimators is such that one of them can yield a better estimate of national employment than the other. The regression of the former estimator on the latter estimator with stochastic intercept and slope is used to generate an improved estimator that is equal to bias- and error-corrected estimator for each area with probability 1. This analysis is extended to cases where more than two estimates of employment are available for each area.

An Analysis of Differences in Earnings Between Small and Large Commercial Banks

This paper introduces a simple, yet rich, measure of efficiency changes based on the revenue-generating-ability (RGA) principle. Using this principle, we explain the connections between efficiency changes and the variables, such as pretax profits, interest expense, non-interest expense, profit margins, loan loss provision, and asset quality. These connections are used to explain earnings differences between small and large commercial banks.