on the statistical identification of dsge models abstract dynamic stochastic general equilibrium dsge models are now considered attractive by the profession not only from the theoretical perspective but also from an empirical standpoint as a consequence of this development methods for diagnosing the fit of these models are being proposed and implemented in this article we illustrate how the concept of statistical identification that was introduced and used by spanos spanos aris 1990 the simultaneous equations model revisited statistical adequacy and identification journal of econometrics 44 87 105 to criticize traditional evaluation methods of cowles commission models could be relevant for dsge models we conclude that the recently proposed model evaluation method based on the dsge var might not satisfy the condition for statistical identification however our application also shows that the adoption of a favar as a statistically identified benchmark leaves unaltered the support of the data for the dsge model and that a dsge favar can be an optimal forecasting model

determinacy indeterminacy and dynamic misspecification in linear rational expectations models abstract this paper proposes a testing strategy for the null hypothesis that a multivariate linear rational expectations lre model may have a unique stable solution determinacy against the alternative of multiple stable solutions indeterminacy the testing problem is addressed by a misspecification type approach in which the overidentifying restrictions test obtained from the estimation of the system of euler equations of the lre model through the generalized method of moments is combined with a likelihood based test for the cross equation restrictions that the model places on its reduced form solution under determinacy the resulting test has no power against a particular class of indeterminate equilibria hence the non rejection of the null hypothesis can not be interpreted conclusively as evidence of determinacy on the other hand this test i circumvents the nonstandard inferential problem generated by the presence of the auxiliary parameters that appear under indeterminacy and that are not identifiable under determinacy ii does not involve inequality parametric restrictions and hence the use of nonstandard inference iii is consistent against the dynamic misspecification of the lre model and iv is computationally simple monte carlo simulations show that the suggested testing strategy delivers reasonable size coverage and power against dynamic misspecification in finite samples an empirical illustration focuses on the determinacy indeterminacy of a new keynesian monetary business cycle model of the us economy

surface shape from the deformation of apparent contours the spatiotemporal analysis of deforming silhouettes apparent contours is here extended using the mathematics of perspective projections and tools from differential geometry analysis of the image motion of a silhouette or apparent contour enables computation of local surface curvature along the corresponding contour generator on the surface assuming viewer motion is known to perform the analysis a spatiotemporal parameterization of image curve motion is needed but is underconstrained a manifestation of the well known aperture problem it is shown that an epipolar parameterization is most naturally matched to the recovery of surface curvature

identification theory for high dimensional static and dynamic factor models abstract high dimensional factor models can involve thousands of parameters the jacobian matrix for identification is of a large dimension it can be difficult and numerically inaccurate to evaluate the rank of such a jacobian matrix we reduce the identification problem to a small rank problem which is easy to check the identification conditions allow both linear and nonlinear restrictions under reasonable assumptions for high dimensional factor models the small rank conditions are shown to be necessary and sufficient for local identification

forecasting using principal components from a large number of predictors this article considers forecasting a single time series when there are many predictors n and time series observations t when the data follow an approximate factor model the predictors can be summarized by a small number of indexes which we estimate using principal components feasible forecasts are shown to be asymptotically efficient in the sense that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both n and t grow large the estimated factors are shown to be consistent even in the presence of time variation in the factor model

on l envelopes of banach spaces abstract let a denote any of the following classes of l spaces c k spaces lindenstrauss spaces separably injective spaces universally separably injective spaces lindenstrauss pelczynski spaces or l spaces we show that every banach space x can be isometrically embedded into a space a x a so that every operator x a with a a can be extended to an operator a x a with the same norm

what s next an interactive next best view approach shape from silhouettes algorithms use either a surface or a volumetric approach a problem with the volumetric approach is that in general we know neither the accuracy of the reconstruction nor where to locate new viewpoints for improving the accuracy in this paper we present a general approach to interactive object specific volumetric algorithms based on a necessary condition for the best possible reconstruction to have been performed the outlined approach can be applied to any class of objects as an example of this approach an interactive algorithm has been implemented for convex polyhedra

improving gdp measurement a measurement error perspective we provide a new measure of historical u s gdp growth obtained by applying optimal signal extraction techniques to the noisy expenditure side and income side gdp estimates the quarter by quarter values of our new measure often differ noticeably from those of the traditional measures its dynamic properties differ as well indicating that the persistence of aggregate output dynamics is stronger than previously thought

bayesian estimation of sparse dynamic factor models with order independent and ex post mode identification abstract common variation in n series is extracted into k n dynamic factors we induce sparsity by using a zero point mass normal mixture prior distribution on the loadings estimation and rotational identification are independent of variable ordering sparsity helps identifying the factor space and the factors rotational identification including factor order and sign is obtained by processing the posterior output and based on factor draws rather than factor loading draws simulating data we document sampler and estimation efficiency to illustrate we estimate the model for a large panel of swiss macroeconomic and detailed price data we identify 16 factors with a clear economic interpretation

time domain galerkin method for dynamic load identification summary this paper proposes a new method called time domain galerkin method tdgm for investigating the structural dynamic load identification problems firstly the shape functions are adopted to approximate three parameters such as the dynamic load kernel function response and measured structural response secondly defining a residual function could be expressed as the difference of the measured response and the computational response thirdly select an appropriate weighting function to multiply the defined residual function and make integral operation with respect to time to be zero finally when the shape functions are chosen as the weighting function it establishes the forward model called tdgm furthermore the regularization method could have effectiveness in solving the ill posed matrix of load reconstruction and obtaining the accurate identified results of the dynamic load compared with the traditional green kernel function method gkfm tdgm can effectively overcome the influences of noise and improve the accuracy of the dynamic load identification three numerical examples are provided to demonstrate the correctness and advantages of tdgm copyright 2015 john wiley sons ltd

identification of linear stochastic models with covariance restrictions abstract the purpose of this paper is to provide a systematic treatment of the problem of identification in systems of linear structural equations where some of the disturbances are uncorrelated

determining the number of factors in approximate factor models in this paper we develop some econometric theory for factor models of large dimensions the focus is the determination of the number of factors r which is an unresolved issue in the rapidly growing literature on multifactor models we first establish the convergence rate for the factor estimates that will allow for consistent estimation of r we then propose some panel criteria and show that the number of factors can be consistently estimated using the criteria the theory is developed under the framework of large cross sections n and large time dimensions t no restriction is imposed on the relation between n and t simulations show that the proposed criteria have good finite sample properties in many configurations of the panel data encountered in practice

a computational approach for corner and vertex detection corners and vertexes are strong and useful features in computer vision for scene analysis stereo matching and motion analysis here we deal with the development of a computational approach to these important features we consider first a corner model and study analytically its behavior once it has been smoothed using the well known gaussian filter this allows us to clarify the behavior of some well knowncornerness measure based approaches used to detect these points of interest most of these classical approaches appear to detect points that do not correspond to the exact position of the corner a new scale space based approach that combines useful properties from the laplacian and beaudet s measure beaudet 1978 is then proposed in order to correct and detect exactly the corner position an extension of this approach is then developed to solve the problem of trihedral vertex characterization and detection in particular it is shown that a trihedral vertex has two elliptic maxima on extremal contrast surfaces if the contrast is sufficient and this allows us to classify trihedral vertexes in 2 classes vertex and vertex as corner the corner detection approach developed is applied to accurately detect trihedral vertexes using an additional test in order to make a distinction between trihedral vertexes and corners many experiments have been carried out using noisy synthetic data and real images containing corners and vertexes most of the promising results obtained are used to illustrate the experimental section of this paper

computing exact aspect graphs of curved objects algebraic surfaces this article presents an algorithm for computing the exact aspect graph of an opaque solid bounded by a smooth algebraic surface orthographic projection is assumed the algorithm is based on a catalog of visual events available from singularity theory it uses curve tracing cell decomposition homotopy continuation and ray tracing to construct the regions of the view sphere delineated by visual event curves the algorithm has been fully implemented and examples are presented

affine reconstruction of curved surfaces from uncalibrated views of apparent contours in this paper we consider uncalibrated reconstruction of curved surfaces from apparent contours since apparent contours are not fixed features viewpoint independent we cannot directly apply the recent results of the uncalibrated reconstruction from fixed features we show that nonetheless curved surfaces can be reconstructed up to an affine ambiguity from their apparent contours viewed from uncalibrated cameras with unknown linear translations furthermore we show that even if the reconstruction is nonmetric non euclidean we can still extract useful information for many computer vision applications just from the apparent contours we first show that if the camera motion is linear translation but arbitrary direction and magnitude the epipolar geometry can be recovered from the apparent contours without using any optimization process the extracted epipolar geometry is next used for reconstructing curved surfaces from the deformations of the apparent contours viewed from uncalibrated cameras the result is applied to distinguishing curved surfaces from fixed features in images it is also shown that the time to contact to the curved surfaces can be computed from simple measurements of the apparent contours

a theory of shape by space carving in this paper we consider the problem of computing the 3d shape of an unknown arbitrarily shaped scene from multiple photographs taken at known but arbitrarily distributed viewpoints by studying the equivalence class of all 3d shapes that reproduce the input photographs we prove the existence of a special member of this class the photo hull that 1 can be computed directly from photographs of the scene and 2 subsumes all other members of this class we then give a provably correct algorithm called space carving for computing this shape and present experimental results on complex real world scenes the approach is designed to 1 capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints and 2 account for the complex interactions between occlusion parallax shading and their view dependent effects on scene appearance

using extremal boundaries for 3 d object modeling the extremal boundaries of 3 d curved objects are the images of special curves drawn on the object and are called rims they are viewpoint dependent and characterized by the fact that the optical rays of their points are tangential to the surface of the object the mathematics of the relationship between the extremal boundaries and the surface of the object is studied this study makes it possible to design an algorithm for detecting those boundaries in the images that are likely to be extremal once this has been done one can reconstruct the rims and compute the differential properties of the surface of the object along them up to the second order if a qualitative description is sufficient the sign of the gaussian curvature of the surface along the rim can be computed in a much simpler way experimental results are presented on synthetic and real images the work provides a better understanding of the relationship between the apparent and real shape of a 3 d object as well as algorithms for reconstructing the local shape of such an object along the rims

identification in linear simultaneous equations models with covariance restrictions an instrumental variables interpretation necessary and sufficient conditions for identification with linear coefficient and covari ance restrictions are developed in a limited information context for the limited informa tion case covariance restrictions aid identification if and only if they imply that a set of endogenous variables is predetermined in the equation of interest generalizing the idea of recursiveness under full information covariance restrictions imply that residuals from other equations are predetermined in a particular equation and under certain conditions can aid in identification sufficient conditions for identification are obtained for the hierarchical system in which the identification of a particular equation does not depend upon the identifiability of higher numbered equations in the general case the fiml first order conditions show that if the system of equations is identifiable as a whole covariance restrictions cause residuals to behave as instruments in both limited and full information settings the link between identification and estimation is worked out restrictions useful for identification yield instruments required for estimation

likelihood ratio testing in linear state space models an application to dynamic stochastic general equilibrium models abstract this paper considers the problem of hypothesis testing in linear gaussian state space models we consider two hypotheses of interest a simple null and a hypothesis of explicit parameter restrictions we derive the asymptotic distributions of the corresponding likelihood ratio test statistics and compute the bartlett adjustments the results are non trivial because the unrestricted state space model is not even locally identified we apply our analysis to test the validity of the dynamic stochastic general equilibrium dsge models a monte carlo exercise illustrates our findings and confirms the importance of bartlett corrections at sample sizes typically encountered in macroeconomics

stationary bubble equilibria in rational expectation models abstract a linear rational expectation model with current expectations admits a unique linear stationary dynamic equilibrium only under specific restrictions on the parameter values this paper shows that in general there is a multiplicity of stationary dynamic equilibria due to the existence of nonlinear stationary equilibria these nonlinear stationary equilibria are consistent with the self fulfilling prophecies that characterize the rational expectation equilibria and can display speculative bubbles volatility induced mean reversion and or stochastic autoregressive patterns they are also compatible with the transversality conditions when the model involves intertemporal optimization the stationary nonlinear dynamic equilibria are economically relevant their analysis requires revised methods of identification for the stationary equilibrium impulse response analysis and estimation techniques which are presented in this paper standard econometric and economic methods which ignore the nonlinear stationary solutions provide misleading outcomes which may affect the validity of an economic policy or portfolio strategy

from uncertainty to visual exploration the authors attempt to determine what can be inferred from ambiguity in processes of visual interpretation they discuss this question in a specific context the interpretation of scene geometry in the form of parametrized volumetric models ambiguity is described as a local probabilistic property of the misfit error surface in the parameter space of superellipsoid models namely as an ellipsoid of confidence in which there is a given probability that the true parameters can be found the authors show how to project the ellipsoid of confidence back into 3d space to obtain the shell in which the true 3d surface most probably lies and introduce what they call the uncertainty as a local property of the fitted model s surface they propose a technique that can use this information to plan a new direction of view that minimizes the ambiguity of subsequent interpretation

global surface reconstruction by purposive control of observer motion abstract what viewpoint control strategies are important for performing global visual exploration tasks such as searching for specific surface markings building a global model of an arbitrary object or recognizing an object in this paper we consider the task of purposefully controlling the motion of an active monocular observer in order to recover a global description of a smooth arbitrarily shaped object we formulate global surface reconstruction as the task of controlling the motion of the observer so that the visible rim slides over the maximal connected reconstructible surface regions intersecting the visible rim at the initial viewpoint we show that these regions are bounded by a subset of the visual event curves defined on the surface by studying the epipolar parameterization we develop two basic strategies that allow reconstruction of a surface region around any point in a reconstructible surface region these strategies control viewpoint to achieve and maintain a well defined geometric relationship with the object s surface rely only on information extracted directly from images e g tangents to the occluding contour and are simple enough to be performed in real time we then show how global surface reconstruction can be provably achieved by 1 appropriately integrating these strategies to iteratively grow the reconstructed regions and 2 obeying four simple rules

principal components estimation and identification of static factors a b s t r a c t it is known that the principal component estimates of the factors and the loadings are rotations of the un derlying latent factors and loadings we study conditions under which the latent factors can be estimated asymptotically without rotation we derive the limiting distributions for the estimated factors and factor loadings when n and t are large and make precise how identification of the factors affects inference based on factor augmented regressions we also consider factor models with additive individual and time effects the asymptotic analysis can be modified to analyze identification schemes not considered in this analysis

extended yule walker identification of varma models with single or mixed frequency data chen and zadrozny 1998 developed the linear extended yule walker xyw method for determining the parameters of a vector autoregressive var model with available covariances of mixed frequency observations on the variables of the model if the parameters are determined uniquely for available population covariances then the var model is identified the present paper extends the original xyw method to an extended xyw method for determining all arma parameters of a vector autoregressive moving average varma model with available covariances of single or mixed frequency observations on the variables of the model the paper proves that under conditions of stationarity regularity miniphaseness controllability observability and diagonalizability on the parameters of the model the parameters are determined uniquely with available population covariances of single or mixed frequency observations on the variables of the model so that the varma model is identified with the single or mixed frequency covariances

a note on the identification of restricted factor loading matrices it is shown that problems of rotational equivalence of restricted factor loading matrices in orthogonal factor analysis are equivalent to problems of identification in simultaneous equations systems with covariance restrictions a necessary under a regularity assumption and sufficient condition for local uniqueness is given and a counterexample is provided to a theorem by j algina concerning necessary and sufficient conditions for global uniqueness

estimation and inference of dynamic structural factor models with over identifying restrictions abstract this paper develops a new estimator for the impulse response functions in structural factor models with a fixed number of over identifying restrictions the proposed identification scheme nests the conventional just identified recursive scheme as a special case we establish the asymptotic distributions of the new estimator and develop test statistics for the over identifying restrictions simulation results show that adding a few more over identifying restrictions can lead to a substantial improvement in estimation accuracy for impulse response functions at both zero and nonzero horizons we estimate the effects of a monetary policy shock using a u s data set the results show that our over identified scheme can help to detect incorrect specifications that lead to spurious impulse responses

var analysis nonfundamental representations blaschke matrices abstract macroeconomic models may produce arma structures where the determinant of the ma matrix polynomial has some roots inside the unit circle this implies that the impulse response functions are no longer identified and may vary in an infinite dimensional space this paper deals with this problem in the var or structural var framework we provide a method to strongly limit the research for economically interesting nonfundamental impulse response functions and show how to construct such representations from estimated var coefficients we also give two empirical applications gnp unemployment usa data and interest rate inflation french data

inferential theory for factor models of large dimensions this paper develops an inferential theory for factor models of large dimensions the principal components estimator is considered because it is easy to compute and is asymptotically equivalent to the maximum likelihood estimator if normality is assumed we derive the rate of convergence and the limiting distributions of the estimated factors factor loadings and common components the theory is developed within the framework of large cross sections n and a large time dimension t to which classical factor analysis does not apply we show that the estimated common components are asymptotically normal with a convergence rate equal to the minimum of the square roots of n and t the estimated factors and their loadings are generally normal although not always so the convergence rate of the estimated factors and factor loadings can be faster than that of the estimated common components these results are obtained under general conditions that allow for correlations and heteroskedasticities in both dimensions stronger results are obtained when the idiosyncratic errors are serially uncorrelated and homoskedastic a necessary and sufficient condition for consistency is derived for large n but fixed t

dynamic identification of dynamic stochastic general equilibrium models this paper studies dynamic identification of parameters of a dynamic stochastic general equilibrium model from the first and second moments of the data classical results for dynamic simultaneous equations do not apply because the state space solution of the model does not constitute a standard reduced form full rank of the jacobian matrix of derivatives of the solution parameters with respect to the parameters of interest is necessary but not sufficient for identification we use restrictions implied by observational equivalence to obtain two sets of rank and order conditions one for stochastically singular models and another for nonsingular models measurement errors mean long run and a priori restrictions can be accommodated an example is considered to illustrate the results

arbitrage factor structure and mean variance analysis on large asset markets we examine the implications of arbitrage in a market with many assets the absence of arbitrage opportunities implies that the linear functionals that give the mean and cost of a portfolio are continuous hence there exist unique portfolios that represent these functionals these portfolios span the mean variance efficient set we resolve the question of when a market with many assets permits so much diversification that risk free investment opportunities are available ross 12 14 showed that if there is a factor structure then the mean returns are approximately linear functions of factor loadings we define an approximate factor structure and show that this weaker restriction is sufficient for ross result if the covariance matrix of the asset returns has only k unbounded eigenvalues then there is an approximate factor structure and it is unique the corresponding k eigenvectors converge and play the role of factor loadings hence only a principal component analysis is needed in empirical work