bluuebunny/crossref_metadata_2025_split huggingface dataset:
Introduction
Authors: Brown L Neville , Bell John S , Galabert Jean-Michel 
Published: October 2023

Abstract
Why should a common lawyer study French administrative law? This is a question that the student of English law, to whom this book is primarily addressed, may fairly ask. The answer lies in the value of a comparative approach to a study of one’s own law. In order that the solutions given to problems arising in our administrative law may be better understood, it is instructive and valuable to appreciate how those same or similar problems have been or are being resolved by the corresponding institutions of another highly developed legal system. Great benefit is to be derived from a study of other common law jurisdictions, but it is sometimes even more valuable to go outside the common law world and make comparisons with a legal system having a quite different history and tradition.
The Constitutional and Administrative Background
Authors: Brown L Neville , Bell John S , Galabert Jean-Michel 
Published: October 2023

Abstract
The ‘duality’ of the droit civil and the droit administratif in France, and more particularly the dual system of courts, cannot be under stood without some appreciation of French constitutional history and the present Constitution of the Fifth Republic. This is particularly important because the Conseil d’Etat was the child of the Revolution of 1789 and the period of the Consulate (1798-1802), although droit administratif itselfwas, as we shall see in a subsequent chapter, a later development. The course of French political history since the Revolution has been charted by repeated shifts of power between the executive and the representative assembly. On the one hand, there has been the authoritarian or Bonapartist tradition (inherited from the Ancien Regime) of autocratic rule based upon a powerful and centralized bureaucracy and acting more or less independently of parliament. On the other hand, there is the parliamentary tradition whereby the elected assembly imposes its will upon the executive, although still relying upon a strong bureaucracy.
Extensions and Generalizations
Authors: Dixit Avinash K 
Published: October 2023

Abstract
where the functions are identified by superscripts to avoid confusion with partial derivatives, which are being denoted by subscripts. For the moment, continue to ignore any other restrictions such as non-negativity on the variables. We need m &lt; n, for n constrains on n variables will generally reduce the choice to a discrete set of points, while more constraints will, in general, be mutually inconsistent.
Convex Sets and Their Separation
Authors: Dixit Avinash K 
Published: October 2023

Abstract
Lagrange’s Theorem and the Kuhn-Tucker Theorem give us first order necessary conditions for constrained maximization problems. The conditions are not in general sufficient to determine the optimum. The same necessary conditions would arise if we wanted to minimize the same objective function subject to the same constraints. When I discussed this point in Chapter 2, I mentioned that maxima and minima can be distinguished by examining the curvatures of the objective and constraint functions. That idea is developed in this chapter and Chapters 7 and 8.
Second-Order Conditions
Authors: Dixit Avinash K 
Published: October 2023

Abstract
The previous chapter developed sufficient conditions for optimality, using properties like concavity and quasi-concavity. These were defined globally, that is, over the full domain of definition of the functions. For example, a function is called concave if the tangent at any point lies on or above the graph of the function, or if the graph lies on or above the chord joining any two points of it. Correspondingly, the conditions are sufficient for a global maximum; the x satisfying them does at least as well as any other feasible x. In a sense the conditions are ideal; if they are met, we have no further worry that some distant point somewhere may do better than the one we are looking at.
Issues Involved with the Seasonal Adjustment of Economic Time Series
Authors: Bell* William R , Hillmer Steven C 
Published: October 2023

Abstract
In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modern time series analysis raise the question of whether seasonal adjustment should be performed at all. This in turn leads to a discussion in the second part of issues involved in seasonal adjustment. We state our opinions about the issues raised and review some of the work of other authors. First, we comment on reasons that have been given for doing seasonal adjustment and suggest a new possible justification. We then emphasize the need to define precisely the seasonal and nonseasonal components and offer our definitions. Finally, we discuss criteria for evaluating seasonal adjustments. We contend the proposed criteria based on empirical comparisons of estimated components are of little value and suggest that seasonal adjustment methods should be evaluated based on whether they are consistent with the information in the observed data. This idea is illustrated with an example.
Introduction
Authors: Hylleberg S 
Published: October 2023

Abstract
Although seasonal fluctuations account for a major part of the variation in many quarterly and monthly economic time series, the attitude of many economists has been and still is well expressed in the following citation from William Stanley Jevons (1862), p. 4: ‘Every kind of periodic fluctuation, whether daily, weekly, monthly, quarterly or yearly, must be detected and exhibited not only as a subject of study in itself, but because we must ascertain and eliminate such periodic variations before we can correctly exhibit those which are irregular or non-periodic, and probably of more interest and importance.’ Implicit in this attitude is the belief that seasonality is periodic to such a degree that it is relatively easy to eliminate, but also that economics has little to say about the seasonal movements and conversely that seasonal variation tells us very little of interest about economic theory. However, both views are incorrect but none the less persistent.
The X-11 Method
Authors: Hylleberg S 
Published: October 2023

Abstract
The Bureau of the Census method I was a seven-step program based on a refinement of the ratio-to-moving-average method developed by Macaulay (1931) at the NBER. The method was introduced in 1954 and used as a springboard for the method II of 1955 (see Shiskin and Eisenpress (1957) and Shiskin (1960, 1978)). During the following years several versions of this method were put forward. The versions were denoted X-1, X-2, etc. The last version, the X-11 method, was developed in 1965 (see Shiskin et al. (1967); Kallek (1978); Kuiper (1978); and Danmarks Statistik (1975)). The X-11 method has been in use since 1965, but the program has been improved by the introduction of several different options that allow the user to adhere to the special characteristics of the individual series.
Unobserved-Components Models for Seasonal Adjustment Filters
Authors: Burridge Peter , Wallis Kenneth F 
Published: October 2023

Abstract
Abstract. Time series models are presented, for which the seasonal-component estimates delivered by linear least squares signal extraction closely approximate those of the standard option of the widely-used Census X-11 program. Earlier work is extended by consideration of a broader class of models and by examination of asymmetric filters, in addition to the symmetric filter implicit in the adjustment of historical data. Various criteria that guide the specification of unobserved-components models are discussed, and a new preferred model is presented. Some nonstandard options in X-11 are considered in the Appendix.
Key words: Time series; seasonal adjustment; signal extraction; X-11 method; unobserved-components models; ARIMA models.
Estimating Structural Models of Seasonality
Authors: Engle* Robert F 
Published: October 2023

Abstract
The intention of this paper is to define and estimate several classes of models of seasonal behavior. A stochastic model will be formulated for each of several unobserved components that then add to give an observed series. The statistical problem is first to choose the appropriate model from the class, then to estimate the unknown parameters of the model, and finally to estimate the values of the unobserved components. When one of the components is a seasonal, the end result is a seasonally adjusted data series.
There are at least three advantages of this approach to seasonal adjustment. First, with an explicit statistical model of the seasonal process, it is possible to calculate the properties of different methods and the variances of individual component estimates. Second, the method of seasonal adjustment will be tailored to the characteristics of the series, and third, it is possible to incorporate additional information about economic trends and cycles, weather, strikes, and holidays which will help distinguish seasonal from nonseasonal behavior and will provide means for automatically correcting for phenomena that are normally treated as outliers.
Seasonal Integration and Cointegration *
Authors: Hylleberg S , Engle R F , Granger, C W J , Yoo B S 
Published: October 2023

Abstract
Abstract. This paper develops tests for roots in linear time series which have a modulus of one but which correspond to seasonal frequencies. Critical values for the tests are generated by Monte Carlo methods or are shown to be available from Dickey-Fuller or Dickey-Hasza-Fuller critical values. Representations for multivariate processes with combinations of seasonal and zero-frequency unit roots are developed leading to a variety of autoregressive and error-correction representations. The techniques are used to examine cointegration at different frequencies between consumption and income in the UK.
General Models and Tools for Analysis
Authors: Granger Clive W J , Teräsvirta Timo 
Published: October 2023

Abstract
This chapter considers two important topics–the form of some general models that have been proposed for nonlinear relationships and a discussion of some of the statistical tools that may be helpful in determining what model to use. The general models include a generalization of a linear moving average–called the Volterra representation–and a form of autoregressive model with time-varying coefficients, known as the ‘state-space model’. In a sense all of the other, specific models considered later are special cases of these general models. However, the general models are often too general for practical use. The chapter also discusses frequency domain analysis, which generalizes the spectrum of linear processes. The resulting functions are interesting theoretically but of limited practical use.
Linearity Testing
Authors: Granger Clive W J , Teräsvirta Timo 
Published: October 2023

Abstract
Many parametric nonlinear models discussed in this book have a linear model nested in the nonlinear structure. Before building a nonlinear model it is advisable to find out if indeed a linear model would adequately characterize the economic relationships under analysis. If this were the case, there would be more statistical theory available for building a reasonable model than if a nonlinear model were appropriate. Furthermore, obtaining optimal forecasts for more than one period ahead would be much simpler if the model were linear. It may happen, at least when the time-series are short, that the investigator successfully estimates a nonlinear model although the true relationship between the variables in question is linear. The danger of unnecessarily complicating the model-building is therefore real, but can be diminished by linearity testing. Sometimes linearity tests are also useful in specifying a nonlinear model, but this may be the case only if the set of conceivable nonlinear families of models can be assumed to be rather small. Examples of this will be discussed later. Another occasion in which linearity testing may be useful is after specifying and estimating a nonlinear model. The residuals of the model may be subjected to linearity tests to see if the nonlinear features in the original data have been adequately captured by the estimated nonlinear specification. If linearity is still rejected, the model obviously has to be respecified and re-estimated.
Building Nonlinear Models
Authors: Granger Clive W J , Teräsvirta Timo 
Published: October 2023

Abstract
If a test suggests that a series is nonlinear or is part of a nonlinear relationship, the question naturally arises-how can the relationship be modelled? This chapter surveys a variety of approaches that can be considered, both parametric and nonparametric. Particular attention is paid to the specification and estimation of smooth transition regression models, including how to decide which variety of these models to use. These models were shown in Chapter 3 to arise naturally in economic theory and can also be shown to provide adequate approximations of some of the alternative models in simple cases.
Strategies for Nonlinear Modelling
Authors: Granger Clive W J , Teräsvirta Timo 
Published: October 2023

Abstract
The large number of techniques and models available for investigating nonlinear relationships imply that a strategy is required to approach the modelling question. In this final, brief chapter we discuss the various problems that arise and make some suggestions about an appropriate strategy. However, as currently there is little experience with the use of the various methods and models, the proposed strategy is introduced with some hesitation, and it is believed that a better strategy will become available in the future, as more experience is accumulated. It is clear that the strategy to be discussed is not guaranteed to arrive at the best model or even a satisfactory one.
Bayes models and forecasts of Australian macroeconomic time series
Authors: Phillips Peter CB 
Published: October 2023

Abstract
This paper provides an empirical implementation of some recent work by the author and Werner Ploberger on the development of Bayes models for time series. The methods offer a new data-based approach to model selection, to hypothesis testing and to forecast evaluation in the analysis of time series. A particular advantage of the approach is that modelling issues such as lag order, parameter constancy, and the presence of deterministic and stochastic trends all come within the compass of the same statistical methodology, as do the evaluation of forecasts from competing models. The paper shows how to build parsimonious empirical Bayes models using the new approach and applies the methodology to some Australian macroeconomic data. Bayes models are constructed for thirteen quarterly Australian macroeconomic time series over the period 1959(3}-1987(4). These models are compared with certain fixed format models (like an AR(4) + linear trend) in terms of their forecasting performance over the period 1988(1}-1991(4). The Bayes models are found to be superior to these forecasting exercises for two of the thirteen series, while at the same time being more parsimonious in form.
Testing Exogeneity: An Introduction
Authors: Ericsson Neil R 
Published: October 2023

Abstract
The author is a staff economist in the Division of International Finance, Federal Reserve Board. The views expressed in this article are solely the responsibility of the author and should not be interpreted as reflecting those of the Board of Governors of the Federal Reserve System or other members of its staff. Helpful discussions with and comments from Lisa Barrow, Jim Boughton, Julia Campos, Jonathan Eaton, Jon Faust, David Hendry, SØren Johansen, Katarina Juselius, Ed Leamer, Jaime Marquez, Will Melick, George Tavlas, Hong-Anh Tran, and Ken Wallis are gratefully acknowledged. I am indebted to Lisa Barrow for invaluable resear.ch assistance.
Tests of Cointegrating Exogeneity for PPP and Uncovered Interest Rate Parity in the United Kingdom
Authors: hunter John 
Published: October 2023

Abstract
In this article we try to determine whether the purchasing power parity and uncovered interest rate arbitrage conditions are satisfied by data on the U.K. exchange rate. This study compares the notions of cointegrating exogeneity and weak exogeneity, and then tests these hypotheses.
Domestic and Foreign Effects on Prices in an Open Economy: The Case of Denmark
Authors: Juselius Katarina 
Published: October 2023

Abstract
Domestic price determination in Denmark is investigated using three kinds of macro-economic explanations: (1) internal labor market theories describing the relation between price and wage inflation, on the inflation rate, and (3) external theories describing the foreign transmission effects on a small open economy. The empirical analysis makes use of the multivariate cointegration model, which is based on the joint analysis of long- and short-run behavior. The deviations from derived underlying steady states in each sector were found to be the main determinants of the inflation rate. Among these, the domestic effects were small compared to the foreign effects. The empirical results strongly favored a backward-looking behavioral model in terms of structurally stable parameters as opposed to a forward looking expectations model. The results stand up as quite strong evidence against the Lucas critique.
An Econometric Analysis of TV Advertising Expenditure in the United Kingdom
Authors: Hendry David F 
Published: October 2023

Abstract
Previous econometric studies of the supply of and demand for TV advertising time in the United Kingdom using annual data found negative price elasticities of demand of less than unity in absolute value. Here, a dynamic, constant-parameter, econometric system for both the real price of TV advertising time and the number of commercial home minutes broadcast is estimated from quarterly data and finds a long-run price elasticity of about -2. The model explains the previous findings as due to the high degree of contemporaneous interdependence between price and quantity on annual data, which arises from within-year feedbacks.
Parameter Constancy, Mean Square Forecast Errors, and Measuring Forecast Performance: An Exposition, Extensions, and Illustration
Authors: Ericsson Neil R 
Published: October 2023

Abstract
Parameter constancy and a model’s mean square forecast error are two commonly used measures of forecast performance. By explicit consideration of the information sets involved, this paper clarifies the roles that each plays in analyzing a model’s forecast accuracy. Both criteria are necessary for “good” forecast performance, but neither (nor both) is sufficient. Further, these criteria fit into a general
Systems of Financial Markets
Authors: Eichberger Jürgen , Harper Ian R 
Published: October 2023

Abstract
The central theme of this chapter is the structure of financial markets. The first section reminds us why efficiency of trade fails if there are no financial markets. We then consider two extreme forms of financial assets: (i) financial assets where each asset specifies the delivery of one particular good under well-specified conditions; and (ii) financial assets where each asset specifies the delivery of a numeraire good (money) in just one spot market. Both systems of financial markets can provide trading opportunities that allow agents to achieve a Pareto-optimal allocation in competitive markets. An important insight of the analysis is the observation that a minimum number of sufficiently distinct securities is necessary for efficiency of trade.
Introduction
Authors: Franses Philip Hans 
Published: October 2023

Abstract
This monograph deals with econometric models for seasonally observed eco nomic time series. A typical approach to modelling seasonal time series is to remove the seasonal fluctuations using some seasonal adjustment method. Recently, however, there have appeared several econometric studies that indicate that it may be worthwhile to study seasonal fluctuations in their own right. The main reason for this is that such seasonal variation can also shed some light on the behaviour of economic agents. A further reason is that, in some how detrended time series, the seasonal flwtuations dominate the remaining variation in the series to a large extent.
An Introduction to Seasonal Time Series
Authors: Franses Philip Hans 
Published: October 2023

Abstract
In this chapter I give an introduction to seasonal time series by discussing typical aspects of such time series. This discussion centres around graphs of some quarterly observed time series which serve as running examples throughout the chapter, around several auxiliary regressions, and around the estimated auto correlation functions of (transformations) of the data. The sample series considered are the industrial production index for the USA, the number of unemployed in Canada, real GNP in Germany, and real total investment in the UK. To save space, I consider only quarterly observed time series. In forth coming chapters I include additional time series; they display roughly similar characteristics to those considered here. The sources of the data used in this chapter are given in the Data Appendix. The data analysed here are all season ally unadjusted. Furthermore, all are analysed after the log transformation has been applied, except for the unemployment in Canada series.
Are Seasons, Trends, and Cycles always Independent?
Authors: Franses Philip Hans 
Published: October 2023

Abstract
When an economic time series displays seasonal variation that seems to in crease with an increasing trend, we usually analyse the data after a natural log arithm transformation. An alternative strategy is to construct an explicit model for the increasing seasonal variation using, for example, seasonal dummy variables which interact with linear trends. (See Bowerman et al. (1990) for a comparison of the two approaches.) The use of logarithmic data seems particularly useful when an untransformed series y1can be decomposed There are however economic variables for which seasonal and nonseasonal variation may not be separated just by taking logs. (See Wisniewski (1934) for an early reference to the interdependence of cyclical and seasonal variation and Hylleberg (1994) for a recent discussion.) This may be caused by, say, eco nomic agents who can behave differently in recessions than in expansions, thereby establishing different seasonal patterns across these regimes. For ex ample, some retail sales variables may show peaks in the clearance sales sea sons, where the start and length of those seasons can depend on the current state of the economy. As another example, the growth in disposable income in several industrialized countries has created the possibility of people having more than one holiday per year, and this can have an effect on the seasonal pat tern in nondurables consumption. For some economic time series, therefore, it may not be easy (or even possible) strictly to isolate seasonal and nonseasonal components since the nonseasonal and seasonal fluctuations can interact.