Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.     

Estimating dynamic local interactions models

This paper presents empirical methods for studying a class of local interactions models in which agents’ transitions are affected by their neighbors’ states. We consider an application to urban unemployment and social networks in job search using publicly available cross-section and retrospective data. Most links in our model are local, but some span an entire metropolitan area. Our methods are designed to accommodate the presence of strong cross-sectional dependence arising from these few cross-metro-area links. We also present simple methods to compare data and model spell distributions and to illustrate the model's dynamic properties.     

CHILD CARE PRICES AND MATERNAL EMPLOYMENT: A META‐ANALYSIS

The literature estimates for labor force participation elasticity with regard to child care prices are extensive and varying. While some estimates imply substantial gains from child care subsidies, others find insignificant effects. To determine the causes of the variance, this paper reviews and analyzes the elasticity sizes using estimates from 36 peer‐reviewed articles and working papers in the literature. We start by reviewing the theoretical and empirical aspects related to participation elasticity with regard to child care costs, paying special attention to sample characteristics, methodological aspects, and macro level factors. We conclude by providing a meta‐regression using control variables based on our review of the literature to explain some of the differences between the estimates. As research builds on and improves the methods and assumptions in prior works, elasticity estimates have become smaller over time. This decline might also be partially explained by changes in labor market characteristics. In countries with high rates of part‐time work and very high or very low rates of female labor force participation, we find elasticity rates to be smaller.     

Semantic validity Testing the operationalization of ideological frames of reference

In this article, we draw attention to some problems involved in testing the semantic validity of operationalizations of abstract theoretical concepts. Particularly, we are dealing with concepts used in the field of text analysis. We shall demonstrate a procedure for carrying out a validity test, using a method of scaling of which the underlying theoretical model is generally known as the “law of categorical judgment”. This procedure may be seen as a contribution to traversing the time-honoured deadlock between reliable but irrelevant versus relevant but unreliable methods of content analysis.     

Teamworking and Lean revisited: a reply to Carter et al.

This paper is a reply to Carter et al.’s response to an earlier paper of ours in this journal on the subject of teamworking under Lean in the UK public services . Our reply covers the following issues which Carter et al. have raised: the literature we used to structure our findings; the way in which we used concepts such as autonomy and teamworking; our research methods and approach; how Carter et al.’s newly available data on teamworking might be interpreted; and how data drawn from an official employee attitude survey might best be understood. On the basis of this, we conclude that Carter et al.’s paper fails to meet its objectives. On some things, the authors are simply wrong; on others, they grossly misrepresent our position; on still others, their interpretations are, at best, highly questionable.     

A tale of two yield curves: Modeling the joint term structure of dollar and euro interest rates

Modeling the joint term structure of interest rates in the United States and the European Union, the two largest economies in the world, is extremely important in international finance. In this article, we provide both theoretical and empirical analysis of multi-factor joint affine term structure models (ATSM) for dollar and euro interest rates. In particular, we provide a systematic classification of multi-factor joint ATSM similar to that of Dai and Singleton (2000). A principal component analysis of daily dollar and euro interest rates reveals four factors in the data. We estimate four-factor joint ATSM using the approximate maximum likelihood method of [A?t-Sahalia, 2002] and [A?t-Sahalia, forthcoming] and compare the in-sample and out-of-sample performances of these models using some of the latest nonparametric methods. We find that a new four-factor model with two common and two local factors captures the joint term structure dynamics in the US and the EU reasonably well.     

Null models of economic networks: the case of the world trade web

In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only under assumptions (e.g., sparseness of the network) that are unrealistic for most economic networks like the world trade web (WTW). In this paper we study the evolution of the WTW using a recently-proposed family of null network models. The method allows to analytically obtain the expected value of any network statistic across the ensemble of networks that preserve on average some local properties, and are otherwise fully random. We compare expected and observed properties of the WTW in the period 1950–2000, when either the expected number of trade partners or total country trade is kept fixed and equal to observed quantities. We show that, in the binary WTW, node-degree sequences are sufficient to explain higher-order network properties such as disassortativity and clustering-degree correlation, especially in the last part of the sample. Conversely, in the weighted WTW, the observed sequence of total country imports and exports are not sufficient to predict higher-order patterns of the WTW. We discuss some important implications of these findings for international-trade models.     

Ecological-Inference-Based Latent Growth Models: Modeling Changes of Alienation

Ecological-inference-based statistical methods employ aggregated (ecological) data to approximately infer individual-level structures of interests when individual-level data were not available. Under the same conceptual frames, we introduce the ecological-inference-based latent growth model (EI-LGM) to analyze cross-years latent trends of a general population when longitudinally collected data were not available. We showed both the substantive values and methodological feasibilities of EI-LGMs. Substantively, we analyze results from several Taiwan Social Change Surveys (TSCS) to show the cross-years latent trends using a subscale of alienation psychological characteristics. Not only the cross-years movements of measurement constructs of the scale were shown, the trends of latent factors were revealed as well. More importantly, these trends can be formally tested under the frameworks of EI-LGMs. Statistically, EI-LGMs were implemented under the weighted least square (WLS) approaches because of the dichotomous outcomes of the subscale. We demonstrate some of the estimation methods as well as some cautions of interpreting EI-LGMs using the estimated results.Part of this paper was presented at the Fourth Survey Research Conference held at the Academia Sinica, Taipei, August 29–August 30, 2002.     

Markov-switching dynamic factor models in real time

We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as mixed sampling frequencies and ragged-edge data. First, we evaluate the theoretical gains of using data that are available promptly for computing probabilities of recession in real time. Second, we show how to estimate the model that deals with unbalanced panels of data and mixed frequencies, and examine the benefits of this extension through several Monte Carlo simulations. Finally, we assess its empirical reliability for the computation of real-time inferences of the US business cycle, and compare it with the alternative method of forecasting the probabilities of recession from balanced panels.     

Local block bootstrap inference for trending time series

Resampling for stationary sequences has been well studied in the last couple of decades. In the paper at hand, we focus on nonstationary time series data where the nonstationarity is due to a slowly-changing deterministic trend. We show that the local block bootstrap methodology is appropriate for inference under this locally stationary setting without the need of detrending the data. We prove the asymptotic consistency of the local block bootstrap in the smooth trend model, and complement the theoretical results by a finite-sample simulation.     

Constructing smooth tests without estimating the eigenpairs of the limiting process

Based on the well known Karhunen–Loève expansion, it can be shown that many omnibus tests lack power against “high frequency” alternatives. The smooth tests of  Neyman (1937) may be employed to circumvent this power deficiency problem. Yet, such tests may be difficult to compute in many applications. In this paper, we propose a more operational approach to constructing smooth tests. This approach hinges on a Fourier representation of the postulated empirical process with known Fourier coefficients, and the proposed test is based on the normalized principal components associated with the covariance matrix of finitely many Fourier coefficients. The proposed test thus needs only standard principal component analysis that can be carried out using most econometric packages. We establish the asymptotic properties of the proposed test and consider two data-driven methods for determining the number of Fourier coefficients in the test statistic. Our simulations show that the proposed tests compare favorably with the conventional smooth tests in finite samples.     

ASSESSING THE REGIONAL ECONOMIC IMPACTS OF DEFENSE ACTIVITIES: A SURVEY OF METHODS

Defense activities exercised in a specific region may alter the region's economic performance. An accurate assessment of the potential economic impacts of defense activities is a valuable undertaking to enable regional planners to prepare for changes. The variety in the methods (among others, input–output models, economic base models, Keynesian regional multipliers, fixed‐effects estimators, and case‐study approaches) inspired by geography, sociology, and political science can pose a dilemma. We detail the historical and theoretical background of each method, as well as select exemplary cases where these methods were applied. By examining old and “new” methods, we aim to construct a typology that could be valuable to all stakeholders. In this sense, defense economics can also contribute to the allied social sciences by outlining evaluation methods that may be applicable to other fields.     

Some Recent Developments in Futures Hedging

The use of futures contracts as a hedging instrument has been the focus of much research. At the theoretical level, an optimal hedge strategy is traditionally based on the expected–utility maximization paradigm. A simplification of this paradigm leads to the minimum–variance criterion. Although this paradigm is quite well accepted, alternative approaches have been sought. At the empirical level, research on futures hedging has benefited from the recent developments in the econometrics literature. Much research has been done on improving the estimation of the optimal hedge ratio. As more is known about the statistical properties of financial time series, more sophisticated estimation methods are proposed. In this survey we review some recent developments in futures hedging. We delineate the theoretical underpinning of various methods and discuss the econometric implementation of the methods.     

Large time-varying parameter VARs

In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVP-VAR so that its dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a smaller TVP-VAR at others. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach.     

A robust support vector regression model for electric load forecasting

Electric load forecasting is a crucial part of business operations in the energy industry. Various load forecasting methods and techniques have been proposed and tested. With growing concerns about cybersecurity and malicious data manipulations, an emerging topic is to develop robust load forecasting models. In this paper, we propose a robust support vector regression (SVR) model to forecast the electricity demand under data integrity attacks. We first introduce a weight function to calculate the relative importance of each observation in the load history. We then construct a weighted quadratic surface SVR model. Some theoretical properties of the proposed model are derived. Extensive computational experiments are based on the publicly available data from Global Energy Forecasting Competition 2012 and ISO New England. To imitate data integrity attacks, we have deliberately increased or decreased the historical load data. Finally, the computational results demonstrate better accuracy of the proposed robust model over other recently proposed robust models in the load forecasting literature.     

Choice Modelling Approaches: A Superior Alternative for Environmental Valuatioin?

In this paper, we examine some popular 'choice modelling' approaches to environmental valuation, which can be considered as alternatives to more familiar valuation techniques based on stated preferences such as the contingent valuation method. A number of choice modelling methods are consistent with consumer theory, and its focus on an attribute‐based theory of value permits a superior representation of many environmental management contexts. However, choice modelling surveys can place a severe cognitive burden upon respondents and induce satisficing rather than maximising behavioural patterns. In this framework, we seek to identify the best available choice modelling alternative and investigate its potential to 'solve' some of the major biases associated with standard contingent valuation. We then discuss its use in the light of policy appraisal needs within the EU. An application to the demand for rock climbing in Scotland is provided as an illustration.     

Variance Decomposition Analysis for Nonlinear Economic Models1

In this paper, we propose a new method called the total variance method and algorithms to compute and analyse variance decomposition for nonlinear economic models. We provide theoretical and empirical examples to compare our method with the only existing method called generalized forecast error variance decomposition (GFEVD). We find that the results from the two methods are different when shocks are multiplicative or interacted in nonlinear models. We recommend that when working with nonlinear models researchers should use the total variance method in order to see the importance of indirect variance contributions and to quantify correctly the relative variance contribution of each structural shock.     

Multiple Imputation: Theory and Method

In this review paper, we discuss the theoretical background of multiple imputation, describe how to build an imputation model and how to create proper imputations. We also present the rules for making repeated imputation inferences. Three widely used multiple imputation methods, the propensity score method, the predictive model method and the Markov chain Monte Carlo (MCMC) method, are presented and discussed.     

Variance Decomposition Analysis for Nonlinear Economic Models1

In this paper, we propose a new method called the total variance method and algorithms to compute and analyse variance decomposition for nonlinear economic models. We provide theoretical and empirical examples to compare our method with the only existing method called generalized forecast error variance decomposition (GFEVD). We find that the results from the two methods are different when shocks are multiplicative or interacted in nonlinear models. We recommend that when working with nonlinear models researchers should use the total variance method in order to see the importance of indirect variance contributions and to quantify correctly the relative variance contribution of each structural shock.     

Methods for Default and Robust Bayesian Model Comparison: the Fractional Bayes Factor Approach

In the Bayesian approach to model selection and hypothesis testing, the Bayes factor plays a central role. However, the Bayes factor is very sensitive to prior distributions of parameters. This is a problem especially in the presence of weak prior information on the parameters of the models. The most radical consequence of this fact is that the Bayes factor is undetermined when improper priors are used. Nonetheless, extending the non-informative approach of Bayesian analysis to model selection/testing procedures is important both from a theoretical and an applied viewpoint. The need to develop automatic and robust methods for model comparison has led to the introduction of several alternative Bayes factors. In this paper we review one of these methods: the fractional Bayes factor (O'Hagan, 1995). We discuss general properties of the method, such as consistency and coherence. Furthermore, in addition to the original, essentially asymptotic justifications of the fractional Bayes factor, we provide further finite-sample motivations for its use. Connections and comparisons to other automatic methods are discussed and several issues of robustness with respect to priors and data are considered. Finally, we focus on some open problems in the fractional Bayes factor approach, and outline some possible answers and directions for future research.