Investment and liquidity management are analyzed in a sector in which firms are exogenously cash constrained and empirical estimates of Tobin's "q" provide reliable measures of investment opportunity. Across the entire sector, we document substantial realized investment as well as high investment sensitivity to "q". Investment is also sensitive to measures of financial market frictions, suggesting that constraints on retention of cash flow distort investment decisions. Liquidity is managed through dividend policy and access to short-term bank finance, in which bank lines of credit smooth variation in available cash flow and accelerate investment. Using the Kaplan-Zingales method for measuring the degree of financial constraint, we identify substantial differences between investment and liquidity management policies of firms, in which more (less) financially constrained firms in our sample exhibit high (low) investment and liquidity management sensitivity to variables that measure financial market frictions. Copyright (c) 2009 American Real Estate and Urban Economics Association.

A B S T R A C TIntegrating migration velocity analysis and full waveform inversion can help reduce the high non-linearity of the classic full waveform inversion objective function. The combination of inverting for the long and short wavelength components of the velocity model using a dual objective function that is sensitive to both components is still very expensive and have produced mixed results. We develop an approach that includes both components integrated to complement each other. We specifically utilize the image to generate reflections in our synthetic data only when the velocity model is not capable of producing such reflections. As a result, we get the migration velocity analysis working when we need it, and we mitigate its influence when the velocity model produces accurate reflections (possibly first for the low frequencies). This is achieved using a novel objective function that includes both objectives. Applications to a layered model and the Marmousi model demonstrate the main features of the approach. I N T R O D U C T I O NIntroducing migration velocity analysis (MVA) to full waveform inversion (FWI) has been a common affair lately (Xu et al. 2012;Ma et al. 2012;Almomin and Biondi 2012;Fleury and Perrone 2012;Wang et al. 2013). The general objective here is to somehow use MVA to extract velocity information from imaged reflections capable of adhering to the FWI requirements for an initial velocity model. Such requirements include velocity models that are capable of producing reasonably accurate reflections within a half-cycle (controlled by the frequency) of the observed data. Reflections that fit the data are a result of a high-resolution velocity model, which is the main objective of FWI. Missing in this formula for MVA+FWI is often the role that diving waves play early in the velocity model building process in steering the background model.However, MVA has always been lurking in the background as one of our options for building a generally smooth velocity model capable of focusing an image (AlYahya 1989;Symes and Kern 1992;Plessix, De Roeck, and Chavent 1995;Clement, Chavent, and Gómez 2001). That velocity model * can serve sometimes as an initial velocity for FWI, particularly if the frequencies in the data are low enough. However, inserting MVA within the FWI kernel (or in other words marrying the two together) have given us mixed results, and in some cases, a divorce was necessary. The marriages included an objective function that incorporates both objectives (the FWI one and the MVA one) data fitting, and image focusing with a user-defined weighting between the two (i.e., Fleury and Perrone 2012). Moreover, it includes the possibility of a migration/demigration process where we generate the reflections from the demigration instead of requiring highresolution velocity models to produce reflections (i.e., Xu et al. 2012). In this process, we move the residual measure operation of the classic MVA to the data space, and we refer to the approach as reflection waveform inversion (RWI). Alternat...

Real estate investment trust (REIT) dividend policies and dividend announcement effects during the 2008-2009 liquidity crisis are examined. Multinomial logit results indicate that REITs with higher market leverage or lower marketto-book ratios are more likely to cut dividends, suspend dividends or pay elective stock dividends. These results imply that mitigating going-concern risk is an important motive for REITs adjusting dividend policies during the crisis and support dividend catering theory where investor demand for dividends impacts corporate dividend policies. Moreover, REITs that cut or suspend dividends experience positive cumulative abnormal returns during the post-announcement period after controlling for the potential influence from simultaneous funds from operation announcements. The positive market response over the post-announcement period supports the notion that dividend decisions convey information to investors and is also consistent with the broad catering theory of dividend policy.Research on dividend policy and market reactions to dividend announcements has been conducted in a variety of market environments over the past half century (see, e.g., Lintner 1956, Brav et al. 2005. Little research, however, has focused on these issues in an environment such as the 2008-2009 liquidity crisis. Moreover, although researchers have embraced a wide set of hypotheses and theories including signaling, information asymmetry, agency costs, tax clienteles, firm life cycle and dividend catering to explain dividend behavior, these explanations remain open to debate. 1 During the 2008-2009 crisis, dysfunctional capital markets created an exogenous shock to firms dependent on external capital flows. The broad stock market * National Association of Real Estate Investment Trusts (NAREIT), Washington, DC 20006 or bcase@nareit.com.

This article examines the evolution of real estate investment trust (REIT) capital structure in the new REIT era with a focus on the effects of banking relationships on REIT capital structure. Using a unique sample of REITs from 1992 to 2003, we find that, after controlling for firm characteristics, REITs with banking relationships are more likely to obtain long-term debt ratings and subsequently issue public debt. Moreover, REITs with banking relationships tend to use less secured debt and have lower leverage. These findings support the notion that banking relationships facilitate REITs' access to the public debt markets and help explain why REITs shift from traditional mortgage financing to bank debt and public capital market financing. The results also support the proposition that firm leverage should be positively related to the amount of a firm's secured debt. Copyright (c) 2010 American Real Estate and Urban Economics Association.

The gradient of standard full-waveform inversion (FWI) attempts to map the residuals in the data to perturbations in the model. Such perturbations may include smooth background updates from the transmission components and high wavenumber updates from the reflection components. However, if we fix the reflection components using imaging, the gradient of what is referred to as reflected-waveform inversion (RWI) admits mainly transmission background-type updates. The drawback of existing RWI methods is that they lack an optimal image capable of producing reflections within the convex region of the optimization. Because the influence of velocity on the data was given mainly by its background (propagator) and perturbed (reflectivity) components, we have optimized both components simultaneously using a modified objective function. Specifically, we used an objective function that combined the data generated from a source using the background velocity, and that by the perturbed velocity through Born modeling, to fit the observed data. When the initial velocity was smooth, the data modeled from the source using the background velocity will mainly be reflection free, and most of the reflections were obtained from the image (perturbed velocity). As the background velocity becomes more accurate and can produce reflections, the role of the image will slowly diminish, and the update will be dominated by the standard FWI gradient to obtain high resolution. Because the objective function was quadratic with respect to the image, the inversion for the image was fast. To update the background velocity smoothly, we have combined different components of the gradient linearly through solving a small optimization problem. Application to the Marmousi model found that this method converged starting with a linearly increasing velocity, and with data free of frequencies below 4 Hz. Application to the 2014 Chevron Gulf of Mexico imaging challenge data set demonstrated the potential of the proposed method.

A successful full-waveform inversion implementation updates the low-wavenumber model components first for a proper description of the wavefield propagation and slowly adds the high wavenumber potentially scattering parts of the model. The low-wavenumber components can be extracted from the transmission parts of the recorded wavefield emanating directly from the source or the transmission parts from the single- or double-scattered wavefield computed from a predicted scatter field acting as secondary sources. We use a combined inversion of data modeled from the source and those corresponding to single and double scattering to update the velocity model and the component of the velocity (perturbation) responsible for the single and double scattering. The combined inversion helps us access most of the potential model wavenumber information that may be embedded in the data. A scattering-angle filter is used to divide the gradient of the combined inversion, so initially the high-wavenumber (low-scattering-angle) components of the gradient are directed to the perturbation model and the low-wavenumber (high-scattering-angle) components are directed to the velocity model. As our background velocity matures, the scattering-angle divide is slowly lowered to allow for more of the higher wavenumbers to contribute the velocity model. Synthetic examples including the Marmousi model are used to demonstrate the additional illumination and improved velocity inversion obtained when including multiscattered energy.

The gradient of standard full-waveform inversion (FWI) attempts to map the residuals in the data to perturbations in the model. Such perturbations may include smooth background updates from the transmission components and high wavenumber updates from the reflection components. However, if we fix the reflection components using imaging, the gradient of what is referred to as reflected-waveform inversion (RWI) admits mainly transmission background-type updates. The drawback of existing RWI methods is that they lack an optimal image capable of producing reflections within the convex region of the optimization. Because the influence of velocity on the data was given mainly by its background (propagator) and perturbed (reflectivity) components, we have optimized both components simultaneously using a modified objective function. Specifically, we used an objective function that combined the data generated from a source using the background velocity, and that by the perturbed velocity through Born modeling, to fit the observed data. When the initial velocity was smooth, the data modeled from the source using the background velocity will mainly be reflection free, and most of the reflections were obtained from the image (perturbed velocity). As the background velocity becomes more accurate and can produce reflections, the role of the image will slowly diminish, and the update will be dominated by the standard FWI gradient to obtain high resolution. Because the objective function was quadratic with respect to the image, the inversion for the image was fast. To update the background velocity smoothly, we have combined different components of the gradient linearly through solving a small optimization problem. Application to the Marmousi model found that this method converged starting with a linearly increasing velocity, and with data free of frequencies below 4 Hz. Application to the 2014 Chevron Gulf of Mexico imaging challenge data set demonstrated the potential of the proposed method.

Spectral methods are fast becoming an indispensable tool for wavefield extrapolation, especially in anisotropic media because it tends to be dispersion and artifact free as well as highly accurate when solving the wave equation. However, for inhomogeneous media, we face difficulties in dealing with the mixed space-wavenumber domain extrapolation operator efficiently. To solve this problem, we evaluated an optimized expansion method that can approximate this operator with a low-rank variable separation representation. The rank defines the number of inverse Fourier transforms for each time extrapolation step, and thus, the lower the rank, the faster the extrapolation. The method uses optimization instead of matrix decomposition to find the optimal wavenumbers and velocities needed to approximate the full operator with its explicit low-rank representation. As a result, we obtain lower rank representations compared with the standard low-rank method within reasonable accuracy and thus cheaper extrapolations. Additional bounds set on the range of propagated wavenumbers to adhere to the physical wave limits yield unconditionally stable extrapolations regardless of the time step. An application on the BP model provided superior results compared to those obtained using the decomposition approach. For transversely isotopic media, because we used the pure P-wave dispersion relation, we obtained solutions that were free of the shear wave artifacts, and the algorithm does not require that η > 0. In addition, the required rank for the optimization approach to obtain high accuracy in anisotropic media was lower than that obtained by the decomposition approach, and thus, it was more efficient. A reverse time migration result for the BP tilted transverse isotropy model using this method as a wave propagator demonstrated the ability of the algorithm.