What determines international tourist arrivals in India?

Using data for 18 major tourist originating countries to India from 2001 to 2015, this study examines the major determinants of international tourist arrivals in India. The results indicate that past experiences of the tourists, per capita income in the tourist originating country, relative costs of living between India and the country of origin, and the level of infrastructure development in India are key determinants of international tourist arrivals in India. Furthermore, both transportation and communication infrastructure are important in attracting tourists to India. In particular, evidence suggests that availability of road and air network and telephone connections has favourable impacts on international tourist arrivals in India. These results are robust to the inclusion of additional variables. These results have important policy implications.     

Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances

Abstract

This paper develops a unified framework for fixed effects (FE) and random effects (RE) estimation of higher-order spatial autoregressive panel data models with spatial autoregressive disturbances and heteroscedasticity of unknown form in the idiosyncratic error component. We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define both an RE and an FE spatial generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators and derive their joint asymptotic distribution, which is robust to heteroscedasticity of unknown form in the idiosyncratic error component. Finally, we derive a robust Hausman test of the spatial random against the spatial FE model.     

Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.     

Reopening the convergence debate: A new look at cross-country growth empirics

There are two sources of inconsistency in existing cross-country empirical work on growth: correlated individual effects and endogenous explanatory variables. We estimate a variety of cross-country growth regressions using a generalized method of moments estimator that eliminates both problems. In one application, we find that per capita incomes converge to their steady-state levels at a rate of approximately 10 percent per year. This result stands in sharp contrast to the current consensus, which places the convergence rate at 2 percent. We discuss the theoretical implications of this finding. In another application, we perform a test of the Solow model. Again, contrary to prior reults, we reject both the standard and the augmented version of the model.     

金融风险计量研究--基于VaR模型的分析

本文通过分析风险的本质涵义,提出了定量分析金融风险的路径;通过回顾对于金融风险定量研究的一些模型和方法,详细介绍了VaR模型及其估计技术,指出了风险定量技术的广阔应用前景。     

Foreign direct investment and economic growth: Is more financial development better?

We investigate the extent to which an increase in financial development affects the positive effect of foreign direct investment on economic growth. Although the financial sector is beneficial for economic growth, the effect of further financial development on growth is found to become insignificant. Using a dynamic panel threshold model on 62 middle- and high-income countries spanning the period 1987–2016, we re-examine the possible nonlinearity between finance, foreign direct investment, and growth. Consistent with the “vanishing effect” of financial development, we find significant evidence that foreign direct investment fosters growth in general, but the growth effect of foreign direct investment becomes negligible when the ratio of private sector credit to gross domestic product exceeds 95.6%. This finding is robust to different econometric methods, various subsamples and interaction analyses, and distinct financial development indicators.     

Stock return, risk, and legal environment around the world

This paper investigates how the legal environment in a country influences performance and risk of stock across countries at different developmental stages and of various rules of jurisdiction. Using data of 4916 stocks from 37 countries, our empirical findings confirm that equities in countries with English common law origin have higher risk premiums than those in civil law countries, particularly for countries of the French/Spanish code. The indicators representing high efficiency in law system, low corruption, strong legal protection of investors' rights, and reliable political environment are associated with low risk and high performance. The various elements of legal procedural formalism, however, have differing effects on volatility and return.     

A unified approach to bivariate discrete distributions

Here we introduce a bivariate generalized hypergeometric factorial moment distribution (BGHFMD) through its probability generating function (p.g.f.) whose marginal distributions are the generalized hypergeometric factorial moment distributions introduced by Kemp and Kemp (Bull Int Stat Inst 43:336–338,1969). Well-known bivariate versions of distributions such as binomial, negative binomial and Poisson are special cases of this distribution. A genesis of the distribution and explicit closed form expressions for the probability mass function of the BGHFMD, its factorial moments and the p.g.f.’s of its conditional distributions are derived here. Certain recurrence relations for probabilities, moments and factorial moments of the bivariate distribution are also established.     

Estimating gravity equation models in the presence of sample selection and heteroscedasticity

Gravity models are widely used to explain patterns of trade. However, two stylized features of trade data, sample selection and heteroscedasticity challenge the estimation of gravity models. We propose a two-step method of moments (TS-MM) estimator that deals with both issues. The Monte-Carlo experiments show that the TS-MM estimates are resistant to various combinations of sample selection and heteroscedasticity. Moreover, the TS-MM estimator performs reasonably well even when the data generating process deviates from the TS-MM assumptions. We revisit the world trade in 1990 to illustrate the usefulness of the proposed model, with emphasis on the identification of the extensive margin of trade.     

Engineering robust instruments for GMM estimation of panel data regression models with errors in variables: a note

Econometricians have long recognized the need to account in some way for measurement errors, specification errors and endogeneity to ensure that the ordinary least squares estimator is consistent. This article introduces a new generalized method of moments estimator that relies on robust instruments to estimate panel data regression models containing errors in variables. We show how this GMM approach can be generalized for the panel data framework using higher moments and cumulants as instruments. The new instruments, engineered for greater robustness, are proposed to tackle the pervasive problem of weak instruments.