Microdata Imputations and Macrodata Implications: Evidence from the Ifo Business Survey

A widespread method for now- and forecasting economic macro level parameters such as GDP growth rates are survey-based indicators which contain early information in contrast to official data. But surveys are commonly affected by nonresponding units which can produce biases if these missing values can not be regarded as missing at random. As many papers examined the effect of nonresponse in individual or household surveys, only less is known in the case of business surveys. So, literature leaves a gap on this issue. For this reason, we analyse and impute the missing observations in the Ifo Business Survey, a large business survey in Germany. The most prominent result of this survey is the Ifo Business Climate Index, a leading indicator for the German business cycle. To reflect the underlying latent data generating process, we compare different imputation approaches for longitudinal data. After this, the microdata are aggregated and the results are compared with the original indicators to evaluate their implications on the macro level. Finally, we show that the bias is minimal and ignorable

Weighted Mixed Regression Estimation Under Biased Stochastic Restrictions

The paper considers the construction of estimators of regression coefficients in a linear regression model when some stochastic and biased apriori information is available. Such apriori information is framed as stochastic restrictions. The dominance conditions of the estimators are derived under the criterion of mean squared error matrix

Simultaneous Prediction of Actual and Average Values of Study Variable Using Stein-rule Estimators

The simultaneous prediction of average and actual values of study variable in a linear regression model is considered in this paper. Generally, either of the ordinary least squares estimator or Stein-rule estimators are employed for the construction of predictors for the simultaneous prediction. A linear combination of ordinary least squares and Stein-rule predictors are utilized in this paper to construct an improved predictors. Their efficiency properties are derived using the small disturbance asymptotic theory and dominance conditions for the superiority of predictors over each other are analyzed

Regularized Proportional Odds Models

The proportional odds model is commonly used in regression analysis to predict the outcome for an ordinal response variable. The maximum likelihood approach becomes unstable or even fails in small samples with relatively large number of predictors. The ML estimates also do not exist with complete separation in the data. An estimation method is developed to address these problems with MLE. The proposed method uses pseudo observations to regularize the observed responses by sharpening them so that they become close to the underlying probabilities. The estimates can be computed easily with all commonly used statistical packages supporting the fitting of proportional odds models with weights. Estimates are compared with MLE in a simulation study and two real life data sets

Performance of Double k-class Estimators for Coefficients in Linear Regression Models with Non Spherical Disturbances under Asymmetric Losses

The risk of the family of feasible generalized double k-class estimators under LINEX loss function is derived in a linear regression model. The disturbances are assumed to be non-spherical and their variance covariance matrix is unknown

Mean Squared Error Matrix comparison of Least Squares and Stein-Rule Estimators for Regression Coefficients under Non-normal Disturbances

Choosing the performance criterion to be mean squared error matrix, we have compared the least squares and Stein-rule estimators for coefficients in a linear regression model when the disturbances are not necessarily normally distributed. It is shown that none of the two estimators dominates the other, except in the trivial case of merely one regression coefficient where least squares is found to be superior in comparisons to Stein-rule estimators

Risk Performance Of Stein-Rule Estimators Over The Least Squares Estimators Of Regression Coefficients Under Quadratic Loss Structures

This paper presents a general loss function under quadratic loss structure and discusses the comparison of risk functions associated with the unbiased least squares and biased Stein-rule estimators of the coefficients in a linear regression model

Stein-Rule Estimation under an Extended Balanced Loss Function

This paper extends the balanced loss function to a more general set up. The ordinary least squares and Stein-rule estimators are exposed to this general loss function with quadratic loss structure in a linear regression model. Their risks are derived when the disturbances in the linear regression model are not necessarily normally distributed. The dominance of ordinary least squares and Stein-rule estimators over each other and the effect of departure from normality assumption of disturbances on the risk property is studied