8 research outputs found

    Spatial panel data models with structural change

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    Spatial panel data models are widely used in empirical studies. The existing theories of spatial models so far have largely confine the analysis under the assumption of parameters stabilities. This is unduely restrictive, since a large number of studies have well documented the presence of structural changes in the relationship of economic variables. This paper proposes and studies spatial panel data models with structural change. We consider using the quasi maximum likelihood method to estimate the model. Static and dynamic models are both considered. Large-TT and fixed-TT setups are both considered. We provide a relatively complete asymptotic theory for the maximum likelihood estimators, including consistency, convergence rates and limiting distributions of the regression coefficients, the timing of structural change and variance of errors. We study the hypothesis testing for the presence of structural change. The three super-type statistics are proposed. The Monte Carlo simulation results are consistent with our theoretical results and show that the maximum likelihood estimators have good finite sample performance

    Spatial panel data models with structural change

    Get PDF
    Spatial panel data models are widely used in empirical studies. The existing theories of spatial models so far have largely confine the analysis under the assumption of parameters stabilities. This is unduely restrictive, since a large number of studies have well documented the presence of structural changes in the relationship of economic variables. This paper proposes and studies spatial panel data models with structural change. We consider using the quasi maximum likelihood method to estimate the model. Static and dynamic models are both considered. Large-TT and fixed-TT setups are both considered. We provide a relatively complete asymptotic theory for the maximum likelihood estimators, including consistency, convergence rates and limiting distributions of the regression coefficients, the timing of structural change and variance of errors. We study the hypothesis testing for the presence of structural change. The three super-type statistics are proposed. The Monte Carlo simulation results are consistent with our theoretical results and show that the maximum likelihood estimators have good finite sample performance

    Pricing Offshore Services: Evidence from the Paradise Papers

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    The Paradise Papers represent one of the largest public data leaks comprising 13.4 million con_dential electronic documents. A dominant theory presented by Neal (2014) and Gri_th, Miller and O'Connell (2014) concerns the use of these offshore services in the relocation of intellectual property for the purposes of compliance, privacy and tax avoidance. Building on the work of Fernandez (2011), Billio et al. (2016) and Kou, Peng and Zhong (2018) in Spatial Arbitrage Pricing Theory (s-APT) and work by Kelly, Lustig and Van Nieuwerburgh (2013), Ahern (2013), Herskovic (2018) and Proch_azkov_a (2020) on the impacts of network centrality on _rm pricing, we use market response, discussed in O'Donovan, Wagner and Zeume (2019), to characterise the role of offshore services in securities pricing and the transmission of price risk. Following the spatial modelling selection procedure proposed in Mur and Angulo (2009), we identify Pro_t Margin and Price-to-Research as firm-characteristics describing market response over this event window. Using a social network lag explanatory model, we provide evidence for social exogenous effects, as described in Manski (1993), which may characterise the licensing or exchange of intellectual property between connected firms found in the Paradise Papers. From these findings, we hope to provide insight to policymakers on the role and impact of offshore services on securities pricing

    Essays on Empirical Asset Pricing Models

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    This thesis examines co-movement across industry return and value and momentum asset price anomalies through a new perspective and uses machine learning and spatial econometrics approaches. The first chapter examines the main approaches developed in the cross-section asset pricing literature for finding risk variables. The second chapter focuses on spatial co-movement across US industry returns. We show that spatial co-movement explains the variance in US industry returns after accounting for exposure to common variables, serial dynamics, and industry sector-specific characteristics using a dynamic spatial panel data model. The results show that an investment strategy that buys industry portfolios with high own-return and high spatially connected (neighboring) portfolio return and sells industry portfolios with low own-return and low spatially connected (neighboring) portfolio return generates an annual non-market return of approximately 8%. In the third chapter of the dissertation, we propose a multi-factor model in which the extra variables (apart from the standard market factor) are the innovation in each sparse principal component. Our findings demonstrate that our suggested hedging factors, which include Production (PR), Housing (H), Yield (Y), and Yield Spread (YS), explain a significant portion of the spread in average equity premia of the momentum portfolio deciles and value portfolio returns. In addition, the paper examines whether the multi-factor model is consistent with Merton\u27s (1973) ICAPM framework
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