Composite indices based on partial least squares

In this paper, we compare Principal Component Analysis (PCA) and Partial Least Squares (PLS) methods to generate weights for composite indices. In this context we also consider various treatments of non-metric variables when constructing such composite indices. Using simulation studies we find that dummy coding for non-metric variables yields satisfactory performance compared to more sophisticated statistical procedures. In our applications we illustrate how PLS can generate weights that differ substantially from those obtained with PCA, increasing the composite indices' predictive performance for the outcome variable considered

Does Politics Matter in the Conduct of Fiscal Policy? Political Determinants of the Fiscal Sustainability: Evidence from Seven Individual Central and Eastern European Countries (CEEC)

This paper aims at assessing the fiscal sustainability and its political determinants in seven Central and Eastern European Countries (CEEC), namely Estonia, Latvia, Lithuania, Poland, Slovenia, Slovakia and the Czech Republic. First, using the recent sustainability approach of Bohn (1998) based on fiscal reaction function, econometric findings using Ordinary Least Squares (OLS) reveal a positive response of the primary surplus to changes in debt in several countries. In other words, fiscal policy is sustainable in Baltic countries, Slovenia and Slovakia, but not in Poland and in the Czech Republic. Second, by introducing political dummy variables, we test the electoral budget cycle and the partisan cycle theories. We find the presence of electoral and partisan cycle in Poland but not in the rest of our countries.Fiscal reaction function, Public debt sustainability, Political budget cycles, Time series

Analisis Data Panel untuk Menguji Pengaruh Risiko terhadap Return Saham Sektor Farmasi dengan Least Square Dummy Variable

Panel data analysis is a method of studying pooling observations on a cross-section of subjects over several time periods. There are several types of panel data analytic models, constant coefficients models, fixed effects models, and random effects models. Fixed effects models would have constant slopes but intercepts that differ according to the cross-sectional (group) unit. While the intercept is cross-section (group) specific, it may or may not differ over time. To show how to test for the presence of statistically significant group and/or time effects, i-1 dummy variables are used to designate the particular group, so we use Least Squares Dummy Variable method. In this paper, we use this method for testing the relationship between risk and stock return at farmation sector data in Indonesia for the time period 2007-2008. The empirical results showed that the model is statistically significant time effects

Unconditional maximum likelihood estimation of dynamic models for spatial panels

This paper hammers out the estimation of a fixed effects dynamic panel data model extended either to include spatial error autocorrelation or a spatially lagged dependent variable. To overcome the inconsistencies associated with the traditional least squares dummy estimator, the models are first-differenced to eliminate the fixed effects and then the unconditional likelihood function is derived taking into account the density function of the first-differenced observations on each spatial unit. When exogenous variables are omitted, the exact likelihood function of both models is found to exist. When exogenous variables are included, the presample values of these variables and thus the likelihood function must be approximated. Two leading cases are considered: the Bhargava and Sargan approximation and the Nerlove and Balestra approximation. As an application, a dynamic demand model for cigarettes is estimated based on panel data from 46 American states over the period 1963 to 1992.

Nearly Unbiased Estimation in Dynamic Panel Data Models with Exogenous Variables

This paper introduces a new estimator for the fixed effects dynamic panel data model withexogenous variables. This estimator does not share some of the drawbacks of recently developed IVand GMM estimators and has a good performance even in small samples. The nearly unbiased estimatoris derived as a bias correction of the within estimator (least squares dummy variable estimator).The estimator is applied to a model of unemployment dynamics at the U.S. state level for the1991-2000 period

Nearly Unbiased Estimation in Dynamic Panel Data Models with Exogenous Variables

This paper introduces a new estimator for the fixed effects dynamic panel data model with exogenous variables. This estimator does not share some of the drawbacks of recently developed IV and GMM estimators and has a good performance even in small samples. The nearly unbiased estimator is derived as a bias correction of the within estimator (least squares dummy variable estimator). The estimator is applied to a model of unemployment dynamics at the U.S. state level for the 1991-2000 period

An empirical assessment of exchange arrangements and inflation performance

Abstract. This article provides empirical support for the hypothesis that different exchange rate regimes have an impact on inflation in advanced, emerging and developing countries. The effects of different exchange rate regimes on inflation performance are examined through least squares dummy variables regressions using panel data on 125 countries for the post-Bretton Woods (1974-1999) period. Also, this article addresses the issue of measurement errors in the classification of exchange rate regimes by using four different classification schemes. Three de facto and one de jure classifications are used. Consequently, the sensitivity of these results to alternative exchange rate classifications is also tested. The empirical findings indicate that countries with fixed regimes tend to have a lower inflation rate compared to floating and intermediate exchange rate regimes, particularly in emerging and developing countries.Keywords. Exchange rate regimes, Inflation.JEL. E31, F31, F33

The Effects of Intermarriage on the Earnings of Female Immigrants in the United States

This paper investigates the effects of intermarriage on the earnings of female immigrants in the United States. The main empirical question asked is whether immigrant females married to US-born spouses have higher earnings than those of immigrant females married to other immigrants. Using 1970 and 1870 samples of IPUMS data, I estimate an earnings equation through OLS. I also correct for the labor force selection bias using the Heckman procedure. I finally take into account the endogeneity of intermarriage and apply a twostage least squares (2SLS) estimation procedure. I find that there is a positive marriage premium among immigrant females in the United States but a negative intermarriage premium for exogamously married females compared to endogamously married females. My results show that the longer the immigrant stays in the host country, the higher her wages, which is evidence for the assimilation effect over time. I find some evidence for a negative labor force selection bias among immigrant females. In other words, higher human capital women may select themselves out of the labor force, while lower human capital women are working for wages. Among those who are in the labor force, however, married females earn more than singles. I also conclude that being an immigrant from an English-speaking country does not have any impact on wages. Both premiums become statistically insignificant in difference from zero when 2SLS is used as an estimation procedure