Modeling Parameter Heterogeneity in Cross Country Growth Regression Models

Given the failure of the conventional linear Solow growth model to establish reliable results in the analysis cross-country growth performance, this paper proposes a new framework using the concept of hierarchy of time-scales.  By hierarchy of time scales, I mean that slower moving variables such as culture, play a major role in determining medium moving variables such as institutions, and which in turn play a major role in determining faster moving variables such as the conventional determinants of economics growth.  This approach provides a systematic way of thinking about the heterogeneity in the cross-country growth performance.  In the context of the Solow growth model the hierarchical approach suggests a local generalization of the Solow growth model in the form of a semiparametric varying parameter model along the lines of Hastie and Tibshirani (1992). Using the varying coefficient model, this paper studies two examples. In the first example the parameters of the model vary according to initial human capital while in the second they vary according to a measure of ethnic diversity.  The results suggest that there exists substantial parameter heterogeneity in the cross-country growth process.Empirical Growth, Heterogeneity, Hierarchy of Time-Scales, Varying Parameters

Modeling parameter heterogeneity in cross-country regression models

We employ various local generalizations of the Solow growth model that model parameter heterogeneity using human development at the beginning of the period with adult literacy rates and life expectancy at birth as a proxy. The model takes the form of a semiparametric varying coefficient model along the lines of Hastie and Tibshirani (1992). The empirical results show substantial parameter heterogeneity in the cross-country growth process, a finding that is consistent with the presence of multiple steady state equilibria and the emergence of convergence clubs.Solow growth model, parameter heterogeneity, varying coefficient model, human development

A Projection Pursuit Approach to Cross Country Growth Data

The empirical modeling of the cross-country differences in growth behavior is undoubtedly one of the most predominant research topics in applied macro-econometrics.  However, despite the vast research effort it seems that there are only a few firm conclusions on the sources of cross-country differences.  Unlike the bulk of the literature which focuses on linear parametric models this paper studies a semi-parametric way of modelling.  In particular, it employs projection pursuit regression (PPR) to model the mean regression function of the growth process by a sum of unknown ridge functions (functions of linear combinations of covariates).  PPR model was proposed by Friedman and Stuetzle (1981) to approximate high dimensional functions by simpler functions that operate in low dimensional spaces-typically one-dimensional.  My findings identify non-linear relationships among the basic Solow-type variables.  In particular, initial income and human capital affect growth in a very nonlinear way. Furthermore, there is evidence of interaction effects between human capital and initial income as well as between initial income and population growth rates.   The findings suggest the presence of two steady-state equilibria that classify countries into two groups with different convergence characteristics.

Should macroeconomic forecasters use daily financial data and how?

We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of MIDAS regressions. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters. Our findings show that while on average the predictive ability of all models worsens substantially following the financial crisis, the models we propose suffer relatively less losses than the traditional ones. Moreover, these predictive gains are primarily driven by the classes of government securities, equities, and especially corporate risk.MIDAS, macro forecasting, leads, daily financial information, daily factors.

Structural Threshold Regression

This paper introduces the structural threshold regression model that allows for an endogeneous threshold variable as well as for endogenous regressors. This model provides a parsimonious way of modeling nonlinearities and has many potential applications in economics and finance. Our framework can be viewed as a generalization of the simple threshold regression framework of Hansen (2000) and Caner and Hansen (2004) to allow for the endogeneity of the threshold variable and regime specific heteroskedasticity. Our estimation of the threshold parameter is based on a concentrated least squares method that involves an inverse Mills ratio bias correction term in each regime. We derive its asymptotic distribution and propose a method to construct bootstrap confidence intervals. We also provide inference for the slope parameters based on GMM. Finally, we investigate the performance of the asymptotic approximations and the bootstrap using a Monte Carlo simulation that indicates the applicability of the method in finite samples.

Failure to Launch? The Role of Land Inequality in Transition Delays

Recent work in the growth literature has provided various explanations for transition delays and the great divergence. This paper provides empirical support for one theory of transition delays: initial land inequality. Our analysis is designed to elucidate the channels via which land inequality can affect long-run economic performance. Using a new historical data set for land inequality (Frankema (2009)) we employ duration analysis to investigate whether higher levels of land inequality lead to longer delays in the extension of primary schooling. We then investigate whether such delays affect long-run economic performance via their effect on contemporaneous schooling. Our findings suggest that land inequality is a key determinant of delays in schooling, and that such delays have a significant negative impact on long-run output.growth takeoffs, schooling, duration analysis, model uncertainty, institutions.

Do Institutions Rule? The Role of Heterogeneity in the Institutions vs. Geography Debate

We uncover evidence of substantial heterogeneity in the growth experience of countries using a structural threshold regression methodology. Our findings suggest that studies that seek to promote mono-causal explanations in the institutions versus geography debate in growth are potentially misleading.Threshold Regression, Endogenous Threshold Variables, Growth, Institutions, Geography

Structural Threshold Regression

This paper introduces the structural threshold regression model that allows for an endogeneous threshold variable as well as for endogenous regressors. This model provides a parsimonious way of modeling nonlinearities and has many potential applications in economics and .finance. Our framework can be viewed as a generalization of the simple threshold regression framework of Hansen (2000) and Caner and Hansen (2004) to allow for the endogeneity of the threshold variable and regime specific heteroskedasticity. Our estimation of the threshold parameter is based on a concentrated least squares method that involves an inverse Mills ratio bias correction term in each regime. We derive its asymptotic distribution and propose a method to construct bootstrap confidence intervals. We also provide inference for the slope parameters based on GMM. Finally, we investigate the performance of the asymptotic approximations and the bootstrap using a Monte Carlo simulation that indicates the applicability of the method in finite samples.nonlinear regression, endogenous threshold, sample split, regime shifts, inverse Mills ratio

Structural Threshold Regression

This paper extends the simple threshold regression framework of Hansen (2000) and Caner and Hansen (2004) to allow for endogeneity of the threshold variable. We develop a concentrated least squares estimator of the threshold parameter based on an inverse Mills ratio bias correction. We show that our estimator is consistent and investigate its performance using a Monte Carlo simulation that indicates the applicability of the method in finite samples.