A Comparative Study Of Alternative Econometric Packages: An Application To Italian Deposit Interest Rates
AbstractIn examining the determinants of Italian deposit interest rates, we compares alternative econometric packages for estimating panel data. We focus on bank deposits, one of the main forms Italian households use to invest their financial wealth. We survey the literature on deposit rates, with particular reference to the large number of US studies. The empirical analysis is based on more than 8,000 observations for the years 1990-1996. Bank interest rates are taken from the Central Credit Register. We consider the rates on current accounts, certificates of deposit, and total deposits. Other variables are obtained from the Banking Supervision1s statistical returns. We look at the influence on interest rates of the Herfindahl index, the number of banks in each province, the rate of growth in deposits, the custodial holdings of bonds, the ratio of banking costs to total assets.With this abundance of panel data, many different specifications have been estimated using the fixed- and random-effects models. Our purpose is to examine the caveats about numerical accuracy raised by McCullogh and Vinod, who are concerned that little attention is paid to numerical accuracy in the selection of econometric packages. We compare the numerical value of the estimates of three of the most popular econometric packages featuring built-in panel data estimation algorithms: LIMDEP, STATA, and TSP. As a numerical benchmark we used Modeleasy, a general-purpose language allowing matrix operations.The preliminary results look quite promising:1) fixed-effects algorithms are numerically the same to the available decimal places.2) random-effects algorithms yield slightly different results because of the method for computing the variance components.In addition, we compare the relative efficiency of the random-effects algorithms provided by the three packages. This is done by means of a set of suitably designed Monte Carlo experiments, varying the time span and the number of provinces taken into account.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 160.
Date of creation: 05 Jul 2000
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