Identifying spatial interactions in the presence of spatial error autocorrelation: An application to land use spillovers

Spatial spillovers—interaction effects among neighboring agents in space—are a common characteristic of a variety of processes that are of interest to environmental and resource economists. Empirical identification of these interactions is challenging, however, due to the endogenous nature of the interactions and the inevitable unobserved spatial correlation that, if uncontrolled, can result in spurious estimates of the interaction parameters. Traditional spatial econometric models rely on maintained assumptions that impose separate structures for the spatial error and interaction processes and thus are insufficient for solving this identification problem. To identify spatial land use spillovers in a hedonic model of residential housing values, we pursue an alternative approach by exploiting a natural experiment in the data. We use exogenous physical land features that impose a direct constraint on residential development on some, but not all, of the land that falls within our study region and use this to construct a “partial population identifier.” We find that this estimation strategy solves the endogeneity problem and reduces spatial error autocorrelation, but does not fully eliminate it. Estimation of the model using a more restricted sample in combination with the partial population identification strategy is successful in eliminating the remaining spatial error autocorrelation. We conclude that less restrictive approaches to controlling for unobserved spatial correlation, such as the natural experiment pursued here, may provide a superior alternative to identifying spatial spillovers.     

Analyzing long-term average adjustment of financial ratios with spatial interactions

The aim of this paper is to put forward a beta convergence model using spatial interaction to evaluate the dynamics of financial ratios. We overcome some of the limitations that come from the traditional partial adjustment model by relating both models. We show that the parameters of the two models may be connected. As an example, we discuss the case of a large sample of medium to high-tech industrial small and medium enterprises (SMEs) located along the Spanish Mediterranean coast. Our findings support the existence of a long-term average adjustment process in the financial ratios of this set of companies which depends on the characteristics of the firms in their neighborhood.     

Gravity models in integrated panels

This paper shows that standard gravity models of foreign trade include non-stationary variables (bilateral trade and GDP of trading partners). Furthermore, gravity models are characterized by inherited cross-sectional correlation between the panel units (country pairs). Therefore, the results of the standard panel unit root tests are biased and outperformed by the simple cross-sectionally augmented panel unit root test according to Pesaran (J Appl Econom 22:265–312, 2007). Nevertheless, the fixed effects estimator is similar to the dynamic OLS or fully modified OLS, which take into account the non-stationarity of analyzed macroeconomic variables as well as possible endogeneity between output and trade.     

Testing for Granger non-causality in heterogeneous panels

This paper proposes a very simple test of Granger (1969) non-causality for heterogeneous panel data models. Our test statistic is based on the individual Wald statistics of Granger non causality averaged across the cross-section units. First, this statistic is shown to converge sequentially to a standard normal distribution. Second, the semi-asymptotic distribution of the average statistic is characterized for a fixed T sample. A standardized statistic based on an approximation of the moments of Wald statistics is hence proposed. Third, Monte Carlo experiments show that our standardized panel statistics have very good small sample properties, even in the presence of cross-sectional dependence.     

Structural characteristics and influencing factors of spatial correlation network for regional high-quality development in China

On the basis of measuring the regional high-quality development in China from 2011 to 2020,this study uses gravity model to build spatial correlation network,and uses social network analysis method to analyze the structural characteristics and influencing factors of correlation network.The results are shown as follows.First,from 2011 to 2020,the level of regional high-quality development in China is rising gradually,and the discrete characteristics between regions are gradually obvious,showing a...     

Testing for Granger causality in heterogeneous mixed panels

In this paper, we propose a simple Granger causality procedure based on Meta analysis in heterogeneous mixed panels. Firstly, we examine the finite sample properties of the causality test through Monte Carlo experiments for panels characterized by both cross-section independency and cross-section dependency. Then, we apply the procedure for investigating the export led growth hypothesis in a panel data of twenty OECD countries.     

Testing stochastic income convergence in seasonal heterogeneous panels

In this paper we introduce a seasonal version of the Solow–Swan growth model and acquire an empirical income convergence equation. We take this equation as a basis to investigate whether income convergence exists in an OECD sample. To do this, we propose the test statistics under various asymptotic properties for some of the seasonal frequencies in the context of nonstationary heterogeneous panels. Critical values and moments of our statistics are generated and their finite sample performances are examined via Monte Carlo simulations.     

Testing slope homogeneity in large panels with serial correlation

Pesaran and Yamagata (Pesaran, M.H., Yamagata, T., Testing slope homogeneity in large panels, Journal of Econometrics 142, 50–93, 2008) propose a test for slope homogeneity in large panels, which has become very popular in the literature. However, the test cannot deal with the practically relevant case of heteroskedastic and/serially correlated errors. The present note proposes a generalized test that accommodates both features.     

On PPP,unit roots and panels

In this paper we use a combination of time series unit root and cointegration analysis and the Bai and Ng (Econometrica 72:1127–1187, 2004) factor model approach to assess the purchasing power parity hypothesis for four real exchange rate panels. Our main findings are twofold: First, we find robust evidence for nonstationary common components in the real exchange rate panels and hence no evidence for PPP. Second, the presence of nonstationary common components is consistent with rejections of the unit root null hypothesis when applying a battery of first and second generation panel unit root tests, which are known to be adversely affected in the presence of common nonstationary components.     

Sea turtle interactions with Hawaii's longline fishery: an extended multi-objective programming model incorporating spatial and seasonal dimensions

The numerous discussions regarding the advantages and disadvantages of Turkey's becoming a member of the Customs Union has been inconclusive. The empirical analysis that mostly focus on the changes in the volume of trade without much regard to the conjectural changes have also been insufficient. This study attempts to shed light on this issue in a formal analysis of Turkey's international trade by empirically accounting for the changes before and after the Customs Union Agreement (CUA). In doing so, we explicitly account for the concurrent changes in the macroeconomic environment that may have affected Turkey's trade with the rest of the world. Our empirical findings indicate that CUA has not only positively impacted on Turkey's trade, but also led to changes in the behaviour of both exports and imports with regards to their responsiveness to underlying variables.     

A simple test for nonstationarity in mixed panels with incidental trends

Ng (2008) shows how the cross-sectional variance of the observed panel data can be used to construct a simple test for the proportion of non-stationary units. However, in the case with incidental trends the test is distorted. The present note shows how the distortions can be substantially reduced by the use of bias-adjustment. It also investigates the local power of the bias-adjusted test, which is shown to suffer from the same incidental trends problem previously only documented for conventional t$t$-tests.     

Unit root tests for cross-sectionally dependent seasonal panels

Unit root tests are developed for seasonal panel models with cross-sectionally dependent errors. The tests are based on an instrumental variable estimator and have standard Gaussian null asymptotics.     

Testing for unemployment hysteresis in nonlinear heterogeneous panels: International evidence

Nonlinear behavior of unemployment is well documented in the literature, and thus linear unit root tests may not be appropriate in this case. This paper tests for hysteresis of unemployment for 29 OECD countries through the use of a new nonlinear panel unit root test developed by Ucar and Omay (2009). The test examines the joint null hypothesis of linearity and a unit root against the alternative hypothesis of nonlinear stationarity. Large power gains are achieved by both combining cross-sectional information with nonlinearities in the data. In addition, after the unit root null being rejected, we use a sequential panel selection method suggested by Chortareas and Kapetanios (2009) to classify the whole panel into a group of stationary countries and a group of non-stationary countries. The empirical findings show that the nonlinear panel test gives strong evidence in favor of the natural rate hypothesis of unemployment for 23 of 29 OECD countries, in contrast to those obtained by Chang's (2002) linear panel test that 17 countries display evidence of stationary unemployment.     

Bootstrap LM tests for higher-order spatial effects in spatial linear regression models

This paper first extends the methodology of Yang (J Econom 185:33–59, 2015) to allow for non-normality and/or unknown heteroskedasticity in obtaining asymptotically refined critical values for the LM-type tests through bootstrap. Bootstrap refinements in critical values require the LM test statistics to be asymptotically pivotal under the null hypothesis, and for this we provide a set of general methods for constructing LM and robust LM tests. We then give detailed treatments for two general higher-order spatial linear regression models: namely the \(\mathtt{SARAR}(p,q)\) model and the \(\mathtt{MESS}(p,q)\) model, by providing a complete set of non-normality robust LM and bootstrap LM tests for higher-order spatial effects, and a complete set of LM and bootstrap LM tests robust against both unknown heteroskedasticity and non-normality. Monte Carlo experiments are run, and results show an excellent performance of the bootstrap LM-type tests.     

Patent licensing in spatial models

We show that a two-part tariff licensing contract is always optimal to the insider patentee in spatial models irrespective of the size of the innovation or any pre-innovation cost asymmetries. The result provides a simple justification of the prevalence of two-part tariff licensing contracts in industries.     

A new score test for unit roots in heterogeneous panels — Residual likelihood approach

Using the marginal likelihood of the residual, we propose new score type unit root tests for heterogeneous panels. Our tests are more powerful than the t-bar test of Im et al. (2003) for panels with many cross-sectional units and short time spans.