Kapoor,Ria. Making Refugees in India. Oxford University Press. pp.272

Ria Kapoor's book, Making Refugees in India, explores the history of refugees in India, tracing the way the figure of ‘refugee’ makes its appearance in the subcontinent since colonial times. The motivation for the book stems from the Citizen Amendment Act passed in the Indian Parliament in 2019, and through the book, Kapoor explores the past histories and the struggles of refugees in independent India for last seventy-five years. Through the historicizing of the concept of refugee, Kapoor touches on various periods of history within the subcontinent.     

Asymptotics for REML estimation of spatial covariance parameters

In agricultural field trials, restricted maximum likelihood estimation (REML) of the spatial covariance parameters is often preferred to maximum likelihood. Although it has either been conjectured or assumed that REML estimators are asymptotically Gaussian, conditions under which such asymptotic results hold are clearly needed. This article gives checkable conditions for spatial regression when sampling locations are either on a rectangular grid or are irregularly spaced but satisfy certain growth conditions.     

A study of combustibility of wastes of two hospitals for determining disposal by incineration

Determination of quantity, quality and characteristics of the wastes of two hospitals located in the same sector of a class B city was undertaken to review the present dumping method of disposal. On an average 1,424.71 and 224.6 kgs. of five types of wastes as per the moisture content were found daily for seven days in these two hospitals of 1000 general beds, and 600 long stay beds for chest tuberculosis and other Cardiothoracic diseases respectively. The wastes of first hospital had higher combustibility due to its sprawling nature and activities for general patients than that of the second multistoreyed hospital treating special cases. A combustibility analysis of hospital wastes would help to decide whether to adopt incineration for disposal.     

More Flexible Use of Survey Data on Expectations in Macroeconomic Models

A flexible procedure is proposed whereby both the random and systematic errors in measurement of survey data on inflation expectations can be appropriately handled when these surveys are used in the estimation of macroeconomic models. During the period 1952–1980, Livingston's two expectations series and the SRC series systematically underestimated inflation expectations by nearly 25%. The ASA–NBER survey, however, does not exhibit such underestimation. Thus, the use of the Livingston or SRC data as an unbiased measure of the true expectations of inflation would result in overestimation of the Fisher effect and the ex ante real interest rate.     

Least squares variogram fitting by spatial subsampling

Summary. Least squares methods are popular for fitting valid variogram models to spatial data. The paper proposes a new least squares method based on spatial subsampling for variogram model fitting. We show that the method proposed is statistically efficient among a class of least squares methods, including the generalized least squares method. Further, it is computationally much simpler than the generalized least squares method. The method produces valid variogram estimators under very mild regularity conditions on the underlying random field and may be applied with different choices of the generic variogram estimator without analytical calculation. An extension of the method proposed to a class of spatial regression models is illustrated with a real data example. Results from a simulation study on finite sample properties of the method are also reported.     

A short-cut method for estimating death registration completeness in destabilized populations

"This paper proposes a simple method for evaluating death registration completeness during intercensal periods. It is easier to implement than alternative methods but its main advantage is making explicit the dependence of results on the quality of readily observed demographic variables. Applications are made to data from South Korea and Argentina."     

Online learning and forecast combination in unbalanced panels

This article evaluates the performance of a few newly proposed online forecast combination algorithms and compares them with some of the existing ones including the simple average and that of Bates and Granger (1969). We derive asymptotic results for the new algorithms that justify certain established approaches to forecast combination including trimming, clustering, weighting, and shrinkage. We also show that when implemented on unbalanced panels, different combination algorithms implicitly impute missing data differently, so that the performance of the resulting combined forecasts are not comparable. After explicitly imputing the missing observations in the U.S. Survey of Professional Forecasters (SPF) over 1968 IV-2013 I, we find that the equally weighted average continues to be hard to beat, but the new algorithms can potentially deliver superior performance at shorter horizons, especially during periods of volatility clustering and structural breaks.     

POTENTIAL PARETO‐IMPROVING MOVE TOWARD MOST FAVORED NATION TARIFFS

We use a multicountry asymmetric oligopolistic framework for segmented markets to study the welfare effects of reducing tariff discriminations—which we call a move toward Most Favored Nation tariffs. In our basic framework initial tariffs are arbitrary, and we consider unilateral reforms without and with reciprocity/retaliation. We extend the basic framework to allow for revenue constraints, unemployment, and the case where the initial tariffs are at Nash‐optimal levels. We also consider multilateral reforms in the last scenario. Conditions under which such harmonizations are potentially Pareto‐improving are derived and results compared across scenarios. Each scenario makes a significant difference to qualitative results. (JEL F10, F11, F13, H21, H3, H41)