Missing data is one of the most common challenges facing empirical researchers. This paper presents a general GMM framework for dealing with missing data on explanatory variables or instrumental variables. For a linear-regression model with missing covariate data, an efficient GMM estimator under minimal assumptions on missingness is proposed. The estimator, which also allows for a specificatio...

This paper introduces a novel approach to investigate the dynamics of state distributions, which accommodate both cross-sectional distributions of repeated panels and intra-period distributions of a time series observed at high frequency. In our approach, densities of the state distributions are regarded as functional elements in a Hilbert space, and are assumed to follow a functional autoregre...

This paper uses a functional coefficient regression to estimate time-varying betas and alphas in the conditional capital asset pricing model. Functional coefficient representation relaxes the strict assumptions on the structure of betas and alphas by combining the predictors into an index that best captures time variations in betas and alphas and estimates them nonparametrically. This index in ...

Structural change has been conjectured to lead to an upward bias in the estimated forward expectations coefficient in New-Keynesian Phillips curves. We present a simple New-Keynesian model that enables us to assess this proposition. In particular, we investigate the issue of upward bias in the estimated coefficients of the expectations variable in the New-Keynesian Phillips curve based on a mod...

In this paper a pairwise comparison estimation procedure is proposed for the regression coefficients in a censored transformation model. The main advantage of the new estimator is that it can accommodate covariate dependent censoring without the requirement of smoothing parameters, trimming procedures, or stringent tail behavior restrictions. We also modify the pairwise estimator for other vari...

A unifying framework to test for causal effects in non-linear models is proposed. We consider a generalized linear-index regression model with endogenous regressors and no parametric assumptions on the error disturbances. To test the significance of the effect of an endogenous regressor, we propose a test statistic that is a kernel-weighted version of the rank correlation statistic (tau) of Ken...

This study aims to examine the effect of investment and consumption spending on Indonesia’s economic growth. The data used is the quarterly time series data from the first quarter of year 2003 to the fourth quarter of year 2013, comprising consumption spending, investment and economic growth. For the purpose of analysis, the autoregressive distributed lag (ARDL) model is used. The result of the...

In this paper we propose simulation based Bayesian inference procedures in a cost system that includes the cost function and the cost share equations augmented to accommodate technical and allocative inefficiency. Markov Chain Monte Carlo techniques are proposed and implemented for Bayesian inferences on costs of technical and allocative inefficiency, input price distortions and over(under-) us...

The purpose of this paper is to investigate the ability of parameter instability tests in regressions with I(1) processes to discriminate between changes in the cointegrating relationship and changes in the marginal distribution of the regressors. Using annual data for the G-7 countries and the Purchasing Power Parity, we conclude that the regression coefficient between the price level differen...

This chapter gives an account of the recent literature on estimating models for panel count data. Specifically, the treatment of unobserved individual heterogeneity that is correlated with the explanatory variables and the presence of explanatory variables that are not strictly exogenous are central. Moment conditions are discussed for these type of problems that enable estimation of the parame...