Estimating Gravity Models of International Trade with Correlated Time-Fixed Regressors: To IV or not IV?

Gravity type models are widely used in international economics. In these models the inclusion of time-fixed regressors like geographical or cultural distance, language and institutional (dummy) variables is often of vital importance e.g. to analyse the impact of trade costs on internationalization activity. This paper analyses the problem of parameter inconsistency due to a correlation of the time-fixed regressors with the combined error term in panel data settings. A common solution is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981) since a standard Fixed Effect Model (FEM) estimation is not applicable. However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman-Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, simple non-IV rival estimators performs equally well or even better. We illustrate these findings by estimating gravity type models for German regional export activity within the EU. The results show that the HT specification tends to overestimate the role of trade costs proxied by geographical distance.Gravity model, Exports, Instrumental variables, two-step estimators, Monte Carlo simulations

Estimating Gravity Models of International Trade with Correlated Time-Fixed Regressors: To IV or not IV?

Gravity type models are widely used in international economics. In these models the inclusion of time-fi0xed regressors like geographical or cultural distance, language and institutional (dummy) variables is often of vital importance e.g. to analyse the impact of trade costs on internationalization activity. This paper assesses the problem of parameter inconsistency due to a correlation of the time-fixed regressors with the combined error term in panel data settings. A common solution is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981) since a standard Fixed Effect Model (FEM) estimation is not applicable. However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman-Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, simple non-IV rival estimators performs equally well or even better. We illustrate these findings by estimating gravity type models for German regional export activity within the EU. The results show that the HT specification is likely to overestimate the role of trade costs proxied by geographical distance.Gravity model, Exports, Instrumental variables, two-step estimators, Monte Carlo.

Bootstrap Methods and Applications in Econometrics - A Brief Survey

This paper provides a brief survey of the bootstrap and its use in econometrics. As an introduction, the paper gives a description of the basics of the method, with a special emphasis on boostrap testing. A fairly large amount of space is devoted to discuss why bootstrap tests provide refinements compared to equivalent asymptotic tests. A series of recent different applications in the econometrics literature is then surveyed, in order to give a picture of this rapidly evolving research field.Bootstrap; Sample Reuse Methods; Simulation Methods

The Hausman test, and some alternatives, with heteroskedastic data

The Hausman test is used in applied economic work as a test of misspecification. It is most commonly thought of (wrongly some would say) as a test of whether one or more explanatory variables in a regression model is endogenous. There are several versions of the test available with modern software, some of them suggesting opposite conclusions about the null hypothesis. We explore the size and power of the alternative tests to find the best option. Secondly, the usual Hausman contrast test requires one estimator to be efficient under the null hypothesis. If data are heteroskedastic, the least squares estimator is no longer efficient. Options for carrying out a Hausman-like test in this case include estimating an artificial regression and using robust standard errors, or bootstrapping the covariance matrix of the two estimators used in the contrast, or stacking moment conditions leading to two estimators and estimating them as a system. We examine these options in a Monte Carlo experiment. We conclude that in both these cases the preferred test is based on an artificial regression, perhaps using a robust covariance matrix estimator if heteroskedasticity is suspected. If instruments are weak (not highly correlated with the endogenous regressors), however, no test procedure is reliable. If the test is designed to choose between the least squares estimator and a consistent alternative, the least desirable test has some positive aspects. We also investigate the impact of various types of bootstrapping. Our results suggest that in large samples, wild (correcting for heteroskedasticity) bootstrapping is a slight improvement over asymptotics in models with weak instruments. Lastly, we consider another model where heteroskedasticity is present - the count data model. Our Monte Carlo experiment shows that the test using stacked moment conditions and the second round estimator has the best performance, but which could still be improved upon by bootstrapping

"Aggregation Bias" DOES explain the PPP puzzle

This article summarizes our views on the role of an "aggregation bias" in explaining the PPP Puzzle, in response to the several papers recently written in reaction to our initial contribution. We discuss in particular the criticisms of Imbs, Mumtaz, Ravn and Rey (2002) presented in Chen and Engel (2005). We show that their contentions are based on: (i) analytical counter-examples which are not empirically relevant; (ii) simulation results minimizing the extent of "aggregation bias"; (iii) unfounded claims on the impact of measurement errors on our results; and (iv) problematic implementation of small-sample bias corrections. We conclude, as in our original paper, that "aggregation bias" goes a long way towards explaining the PPP puzzle

Globalization and Knowledge Spillover: International Direct Investment, Exports and Patents

This paper examines the impact of the three main channels of international trade on domestic innovation, namely outward direct investment, inward direct investment (IDI) and exports. The number of Triadic patents serves as a proxy for innovation. The data set contains 37 countries that are considered to be highly competitive in the world market, covering the period 1994 to 2005. The empirical results show that increased exports and outward direct investment are able to stimulate an increase in patent output. In contrast, IDI exhibits a negative relationship with domestic patents. The paper shows that the impact of IDI on domestic innovation is characterized by two forces, and the positive effect of cross-border mergers and acquisitions by foreigners is less than the negative effect of the remaining IDI.International direct investment; Export; Triadic Patent; Outward Direct Investment; Inward Direct Investment; R&D; negative binomial model

Globalization and Knowledge Spillover: International Direct Investment, Exports and Patents

This paper examines the impact of the three main channels of international trade on domestic innovation, namely outward direct investment, inward direct investment (IDI) and exports. The number of Triadic patents serves as a proxy for innovation. The data set contains 37 countries that are considered to be highly competitive in the world market, covering the period 1994 to 2005. The empirical results show that increased exports and outward direct investment are able to stimulate an increase in patent output. In contrast, IDI exhibits a negative relationship with domestic patents. The paper shows that the impact of IDI on domestic innovation is characterized by two forces, and the positive effect of cross-border mergers and acquisitions by foreigners is less than the negative effect of the remaining IDI.R&D;export;international direct investment;inward direct investment;negative binomial model;triadic patent;outward direct investment

Searching for an Environmental Kuznets Curve in Carbon Dioxide Pollutant in Latin American Countries

This study utilized a semiparametric panel model to estimate environmental Kuznets curves (EKC) for carbon dioxide (CO2) in 15 Latin American countries, using hitherto unused data on forestry acreage in each country. Results showed an N-shaped curve for the region; however, the shape of the curve is sensitive to the removal of some groups of countries. Specification tests support a semiparametric panel model over a parametric quadratic specification.CO2, forest acreage, environmental Kuznets curve, Latin American countries, semiparametric regression model, Agribusiness, Environmental Economics and Policy, Research and Development/Tech Change/Emerging Technologies, C14, C33, Q23, Q53,

Using Non-parametric Methods in Econometric Production Analysis: An Application to Polish Family Farms

Econometric estimation of production functions is one of the most common methods in applied economic production analysis. These studies usually apply parametric estimation techniques, which obligate the researcher to specify the functional form of the production function. Most often, the Cobb-Douglas or the Translog production function is used. However, the specification of a functional form for the production function involves the risk of specifying a functional form that is not similar to the “true” relationship between the inputs and the output. This misspecification might result in biased estimation results—including measures that are of interest of applied economists, such as elasticities. Therefore, we propose to use nonparametric econometric methods. First, they can be applied to verify the functional form used in parametric estimations of production functions. Second, they can be directly used for estimating production functions without specifying a functional form and thus, avoiding possible misspecification errors. We use a balanced panel data set of farms specialized in crop production that is constructed from Polish FADN data for the years 2004-2007. Our analysis shows that neither the Cobb-Douglas function nor the Translog function are consistent with the “true” relationship between the inputs and the output in our data set. We solve this problem by using non-parametric regression. This approach delivers reasonable results, which are on average not too different from the results of the parametric estimations but many individual results are rather different.Farm Management,

Testing a Simple Structural Model of Endogenous Growth

The efect of taxation on growth is embodied in a model of a small open economy with endogenous growth. The structural model is estimated on post-war panel data for 76 countries and the bootstrap is used to produce the model’s sampling variation. Panel data regressions of growth on taxation do not reject this model but do reject a model with no tax effects.endogenous growth, taxation, business regulation, bootstrap, model validation.