计量经济分析 = Econometric analysis

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

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Table Of Integrals Series And Products

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

Combining forecasts: A review and annotated bibliography

Motivated by specific problems involving radar-range profiles, we suggest techniques for real-time discrimination in the context of signal analysis The key to our approach is to regard the signals as curves in the continuum and employ a functional data-analytic (FDA) method for dimension reduction, based on the FDA technique for principal coordinates analysis This has the advantage, relative to competing methods such as canonical variates analysis, of providing a signal approximation that is best possible, in an L2 sense, for a given dimension As a result, it produces particularly good discrimination We explore the use of both nonparametric and Gaussian-based discriminators applied to the dimensionreduced data

A Functional Data—Analytic Approach to Signal Discrimination

The echelon form of a VARMA (vector autoregressive moving average) model is considered. Its advantages over other identified VARMA representations are discussed. Furthermore, a general strategy for specifying echelon-form VARMA models from data is presented. Specifically procedures for choosing the Kronecker indices that characterize an echelon form are reviewed. The feasibility of the method is demonstrated by analyzing a well-known set of flour-price time series and the term structure of German interest rates.

https://www.jstor.org/stable/info/1392100

Specification of Echelon-Form VARMA Models

In this paper we will investigate the consequences of applying the sieve bootstrap
under regularity conditions that are sufficiently general to encompass both fractionally
integrated and non-invertible processes. The sieve bootstrap is obtained by approximating
the data generating process by an autoregression whose order h increases with the
sample size T. The sieve bootstrap may be particularly useful in the analysis of fractionally
integrated processes since the statistics of interest can often be non-pivotal with
distributions that depend on the fractional index d. The validity of the sieve bootstrap
is established and it is shown that when the sieve bootstrap is used to approximate the
distribution of a general class of statistics admitting an Edgeworth expansion then the
error rate achieved is of order O(Tβ+d−1), for any β > 0. Practical implementation of the
sieve bootstrap is considered and the results are illustrated using a canonical example.

/pdf/properties-of-the-sieve-bootstrap-for-fractionally-15vb50cldc.pdf

Properties of the Sieve Bootstrap for Fractionally Integrated and Non‐Invertible Processes

Autoregressive models are commonly employed to analyze empirical time series. In practice, however, any autoregressive model will only be an approximation to reality and in order to achieve a reasonable approximation and allow for full generality the order of the autoregression, h say, must be allowed to go to infinity with T, the sample size. Although results are available on the estimation of autoregressive models when h increases indefinitely with T such results are usually predicated on assumptions that exclude (1) non-invertible processes and (2) fractionally integrated processes. In this paper we will investigate the consequences of fitting long autoregressions under regularity conditions that allow for these two situations and where an infinite autoregressive representation of the process need not exist. Uniform convergence rates for the sample autocovariances are derived and corresponding convergence rates for the estimates of AR(h) approximations are established. A central limit theorem for the coefficient estimates is also obtained. An extension of a result on the predictive optimality of AIC to fractional and non-invertible processes is obtained.

https://www.ism.ac.jp/editsec/aism/pdf/059_4_0697.pdf

Autoregressive approximation in nonstandard situations: the fractionally integrated and non-invertible cases

Tests for Granger-causality have been performed in numerous empirical studies. These tests are usually based on finite order vector autoregressive (VAR) processes, and the assumption is made that the model fitted to the available data corresponds to the true data generating mechanism. In the present study, the more general assumption is made that a finite order VAR model is fitted to a potentially infinite order process. The order is assumed to increase with the sample size. Asymptotic properties of tests for Granger-causality as well as other types of causality concepts are derived. Some limited small sample results are obtained using simulation methods. In the context of multiple time series analysis and dynamic econometric models, different types of causation have been defined and analyzed. Typically, causality analyses have been performed in the framework of vector autoregressive (VAR) or vector autoregressive moving average (VARMA) models. In other words, the restrictions for the parameters of these models characterizing the specific causality concept are derived and then tests of these parameter restrictions are performed. Analyses of Granger's causality concept represent popular examples of this research strategy. Most of these analyses have been based on finite order VAR processes. A main reason for the emphasis on finite order, pure VAR models is that the parameter restrictions implied by different causality concepts are easy to treat under common stationarity assumptions. In contrast, tests for Grangercausality are relatively difficult to execute, for instance, in VARMA models (see, e.g., Eberts and Steece, 1984; Boudjellaba, Dufour, and Roy, 1992;

Donald Poskitt

Papers

A Functional Data—Analytic Approach to Signal Discrimination

Specification of Echelon-Form VARMA Models

Properties of the Sieve Bootstrap for Fractionally Integrated and Non‐Invertible Processes

Autoregressive approximation in nonstandard situations: the fractionally integrated and non-invertible cases

Testing for Causation Using Infinite Order Vector Autoregressive Processes