Journal of Economic and Social Measurement

The sale price appraisal ratio (SPAR) method has been applied in a number of countries to construct house price indexes. This paper reviews the statistical and index number properties of the SPAR approach. Three types of SPAR indexes are distinguished: a weighted index, which aims at tracking the price change of the stock of owner-occupied houses, and two unweighted indexes, an arithmetic one and a geometric one. We also discuss stratified versions of these indexes and show how stratification can be used to estimate an expenditure-based Laspeyres-type price index. Empirical results for the Netherlands are given and compared with the repeat-sales index published by the Dutch land registry until January 2008.

'Poverty maps', that is graphic representations of spatially disaggregated estimates of welfare, are being increasingly used to geographically target scare resources. This paper outlines the development of a set of poverty maps for Azerbaijan which can be used by different users. The paper contrasts two alternative approaches to the measurement and mapping of welfare. First a map is derived using imputed household consumption. This involves combining information from the 2002 Household Budget Survey with 1999 Azerbaijan Census data using techniques developed by a team within the World Bank that are now becoming standard practice. Secondly an alternative map is constructed using an asset index based on data from the 1999 Census to produce estimates of welfare at the district level. This provides a unique opportunity to compare the welfare rankings obtained at the regional level under the two alternative approaches and to assess the different results that the two techniques provide.

Despite the frequent use of the Panel Study of Income Dynamics (PSID) in poverty research, no recent studies have examined how to accurately measure long-term trends in poverty with these data. In this paper, we describe how to accurately measure poverty over the course of the PSID panel. We benchmark our poverty rates with published rates from the US Census Bureau's March Current Population Survey (CPS). The PSID time series based on poverty thresholds we developed is more highly correlated with the CPS time series than are the rates based on the two thresholds currently available in the PSID data file for the entire 1967–2004 period. Another PSID poverty threshold, available only from 1989 onwards, yields poverty rates that have a correlation of 0.96 with the CPS series, about the same as the correlation when our methods for measuring poverty are used for these years.

The recent financial crisis and economic downturn has emphasized the importance of accurate sub-national forecasting models. To judge which models work best, researchers have emphasized the importance of looking at the true real-time performance of models and not simply an analysis of out-of-sample results. In this study we utilize real-time forecasts from the Western Blue Chip Economic Forecast to analyze and evaluate a host of different forecasters and models across time and 10 U.S. states to see if some models and forecasters consistently outperform others. We use the forecast accuracy criteria established by the Blue Chip publication. To evaluate accuracy we develop a scoring procedure based on the number of years that the forecaster/model was closest to actual relative to what we would expect just by random chance. We also utilize standard measures such as the Root Mean Square Error and Theil's inequality coefficient and test the statistical significance of the best forecasts. We then take a closer look at one model that has proven to be very accurate.

The received theory of aggregation of producer durables has been erected on certain fundamental hypotheses. One of them is that producer durables deteriorate exponentially, which implies that their replacement is proportional to the corresponding capital stocks. However the proportionality hypothesis conflicts with most of the available theoretical and empirical evidence. So an effort to relax it is long overdue. To this end the present paper investigates the conditions for aggregation in a two-sector vintage capital model with exogenous technological change and endogenous useful lives. In the model aggregation is achieved by adapting the procedure first suggested by Haavelmo [11]. From the simulations of the solution with data from the United States in the post-war period it is found that the conventional approach to aggregation may be responsible for significant biases in the measurement of capital stocks.

The Blinder-Oaxaca (B-O) decomposition has been widely used in continuous outcome comparisons between two groups of people; however, controversies and limitations with this method remain. In this paper, operating under the same-model structure, we suggest employing a multiple regression using a dummy variable to represent discrimination, and we compare this strategy with results obtained from a B-O decomposition and a Neumark decomposition, both in theory and using a simulation study. In addition, we extend these methods to cover binary outcomes.

DAD is designed to facilitate the analysis and the comparisons of social welfare, inequality, poverty and equity across distributions of living standards and using disaggregated data. It is made available at no charge. DAD's features include the estimation of a large number of indices and curves that are useful for distributive comparisons as well as the provision of various statistical tools. It is currently the only software that systematically takes into account the sampling design of commonly used surveys in calculating asymptotic, bootstrap and p-bootstrap statistics for carrying out statistical inference. Many of DAD's features are useful for estimating the impact of programs (and reforms to these programs) on poverty and equity.

We present a case study demonstrating that without data and code archives reproducibility is more the exception than the rule, especially if modern, complex algorithms are employed. Specifically, we show that stochastic extensions of OLS, as required in some combinatorial optimization problems arising in high-breakdown robust regression, can be difficult to replicate in the absence of detailed information on tuning parameters and further computational issues.

Replications merely check whether the results reported by authors are independently verifiable, not whether they are reliable, robust and stable. Statistical inference deals with specification and sampling errors whereas subject matter knowledge is needed to avoid errors in interpretation of the model. Vinod and Ullah [24] suggested perturbing the data beyond the available digits to evaluate the numerical stability of model results. This paper extends the idea into a simple algorithm to create random perturbations for checking perturbation sensitivity (=α $ _{p}$ ) of a model, its software and interpretations. We illustrate the proposed algorithm with archived replication examples from the Journal of Money Credit and Banking (JMCB) and Journal of Applied Econometrics. Some journals (against our sentiment) still allow some parts of the code to be hidden in a black box. Unfortunately, black boxes may also hide shortcuts and dishonesty, unless all black box users are required to post the α $_{p}$ developed here. There is a new requirement at Econometrica to report the sensitivity of empirical results, and other journals may soon follow. Ourα $ _{p}$ provides a tool for meeting such requirements.