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Keywords:Regression analysis 

Conference Paper
Transparency, legal structure, and value relevance of banks: global evidence

Proceedings , Paper 865

Working Paper
A note on the coefficient of determination in models with infinite variance variables

Since the seminal work of Mandelbrot (1963), alpha-stable distributions with infinite variance have been regarded as a more realistic distributional assumption than the normal distribution for some economic variables, especially financial data. After providing a brief survey of theoretical results on estimation and hypothesis testing in regression models with infinite-variance variables, we examine the statistical properties of the coefficient of determination in models with alpha-stable variables. If the regressor and error term share the same index of stability alpha
International Finance Discussion Papers , Paper 895

Working Paper
Aggregated vs. disaggregated data in regression analysis: implications for inference

This note demonstrates why regression coefficients and their statistical significance differ across degrees of data aggregation. Given the frequent use of aggregated data to explain individual behavior, data aggregation can result in misleading conclusions regarding the economic behavior of individuals.
Working Papers , Paper 2002-024

Working Paper
Estimation of panel data regression models with two-sided censoring or truncation

This paper constructs estimators for panel data regression models with individual specific heterogeneity and two-sided censoring and truncation. Following Powell (1986) the estimation strategy is based on moment conditions constructed from re-censored or re-truncated residuals. While these moment conditions do not identify the parameter of interest, they can be used to motivate objective functions that do. We apply one of the estimators to study the effect of a Danish tax reform on household portfolio choice. The idea behind the estimators can also be used in a cross sectional setting.
Working Paper Series , Paper WP-2011-08

Working Paper
Maximum-likelihood estimation of fractional cointegration with application to the short end of the yield curve

We estimate a multivariate autoregressive fractionally-integrated moving-average (ARFIMA) model to illustrate a cointegration testing methodology based on joint estimates of the fractional orders of integration of a cointegrating vector and its parent series. Although previous work has recognized that deviations from long-run relationships could exhibit long memory and go undetected in traditional 1(1)/i (0) cointegration analysis, previous tests for fractional cointegration relied on a two-step testing procedure and maintained the assumption in the second step that the parent series were ...
Working Papers , Paper 1994-027

Journal Article
Technology and skill requirements: implications for establishment wage structures

New England Economic Review , Issue May , Pages 139-156

Complex eigenvalues and trend-reverting fluctuations

Autoregressions of quarterly or annual aggregate time series provide evidence of trend-reverting output growth and of short-term dynamic adjustment that appears to be governed by complex eigenvalues. This finding is at odds with the predictions of reasonably parameterized, convex one-sector growth models, most of which have positive real characteristic roots. We study a class of one-sector economies, overlapping generations with finite life spans of L greater than or equal to 3, in which aggregate saving depends nontrivially on the distribution of wealth among cohorts. If consumption goods ...
Staff Report , Paper 255

Working Paper
A note on the estimation of linear regression models with Heteroskedastic measurement errors

I consider the estimation of linear regression models when the independent variables are measured with errors whose variances differ across observations, a situation that arises, for example, when the explanatory variables in a regression model are estimates of population parameters based on samples of varying sizes. Replacing the error variance that is assumed common to all observations in the standard errors-in-variables estimator by the mean measurement error variance yields a consistent estimator in the case of measurement error heteroskedasticity. However, another estimator, which I call ...
Working Paper Series , Paper WP-01-23


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