The fiscal behavior of municipal governments: 1905–1930

This report discusses a new set of annual U. S. municipal expenditure estimates. These estimates deal with the 1905–1930 period. While these expenditures are seen to be influenced by urbanization and price inflation, it is clear that real per capita spending rose substantially during the period. The distribution of municipal spending between current and capital accounts is seen to be associated with relative price change. A model of the structure of budget decisions—emphasizing an incremental budgeting format—is developed. Estimates of the structural parameters suggest that this model adequately represents the data; and that municipal decision makers responded in a regular and rational fashion to shifts in relative prices and nominal sources of funds.     

On generalized progressive hybrid censoring in presence of competing risks

The progressive Type-II hybrid censoring scheme introduced by Kundu and Joarder (Comput Stat Data Anal 50:2509–2528, 2006), has received some attention in the last few years. One major drawback of this censoring scheme is that very few observations (even no observation at all) may be observed at the end of the experiment. To overcome this problem, Cho et al. (Stat Methodol 23:18–34, 2015) recently introduced generalized progressive censoring which ensures to get a pre specified number of failures. In this paper we analyze generalized progressive censored data in presence of competing risks. For brevity we have considered only two competing causes of failures, and it is assumed that the lifetime of the competing causes follow one parameter exponential distributions with different scale parameters. We obtain the maximum likelihood estimators of the unknown parameters and also provide their exact distributions. Based on the exact distributions of the maximum likelihood estimators exact confidence intervals can be obtained. Asymptotic and bootstrap confidence intervals are also provided for comparison purposes. We further consider the Bayesian analysis of the unknown parameters under a very flexible beta–gamma prior. We provide the Bayes estimates and the associated credible intervals of the unknown parameters based on the above priors. We present extensive simulation results to see the effectiveness of the proposed method and finally one real data set is analyzed for illustrative purpose.     

A bayesian approach to time-varying cross-sectional regression models

This paper presents a Bayesian approach to regression models with time-varying parameters, or state vector models. Unlike most previous research in this field the model allows for multiple observations for each time period. Bayesian estimators and their properties are developed for the general case where the regression parameters follow an ARMA(s,q) process over time. This methodology is applied to the estimation of time-varying price elasticity for a consumer product, using biweekly sales data for eleven domestic markets. The parameter estimates and forecasting performance of the model are compared with various alternative approaches.     

Shrinkage estimation of semiparametric multiplicative error models

Within models for nonnegative time series, it is common to encounter deterministic components (trends, seasonalities) which can be specified in a flexible form. This work proposes the use of shrinkage type estimation for the parameters of such components. The amount of smoothing to be imposed on the estimates can be chosen using different methodologies: Cross-Validation for dependent data or the recently proposed Focused Information Criterion. We illustrate such a methodology using a semiparametric autoregressive conditional duration model that decomposes the conditional expectations of durations into their dynamic (parametric) and diurnal (flexible) components. We use a shrinkage estimator that jointly estimates the parameters of the two components and controls the smoothness of the estimated flexible component. The results show that, from the forecasting perspective, an appropriate shrinkage strategy can significantly improve on the baseline maximum likelihood estimation.     

Mortality Forecasting and Trend Shifts: an Application of the Lee–Carter Model to Swedish Mortality Data*

In this paper we examine how the Lee–Carter model fares with Swedish data for the period 1901–2001 and for segments of this period. We have choosen to censor ages less than age 40 as those ages only are of marginal interest to the forecast. At age 40 some 98 to 99 percent of the birth cohorts are survivors. In the study we only consider the unweighted K¹ estimates. The Lee–Carter model provides very good fits to the data. When splitting up the base period there seems to be an interaction beween the age and time components of the model. In order to deal with the different phases of falling mortality for males and females possibly one should choose the past 25 years as a base in the model. Selecting the base period is however a judgmental issue depending on the main focus of the forecast. Is it long‐term, short‐term or, as in Sweden, a combination of both.?     

Separating Technical Change from Time-Varying Technical Inefficiency in the Absence of Distributional Assumptions

This paper proposes an alternative model for separating technical change from time-varying technical inefficiency. The proposed formulation uses the general index, developed by Baltagi and Griffin (1988), to model technical change in the production frontier function and a quadratic function of time, as in Cornwell, Schmidt and Sickles (1990), to capture the temporal pattern of technical inefficiency. In such a setting, all parameters associated with the rate of technical change and the temporal pattern of technical inefficiency are identified separately. Moreover, the proposed formulation is independent of any distributional assumption concerning the one-sided error term associated with technical inefficiency, and it can be estimated in a single stage with non-linear FGLS. Empirical results based on a translog production frontier, and estimates of technical inefficiency and technical change are presented for the UK dairy sector over the period 1982–1992.     

A new class of inverse Gaussian type distributions

The elliptical laws are a class of symmetrical probability models that include both lighter and heavier tailed distributions. These models may adapt well to the data, even when outliers exist and have other good theoretical properties and application perspectives. In this article, we present a new class of models, which is generated from symmetrical distributions in and generalize the well known inverse Gaussian distribution. Specifically, the density, distribution function, properties, transformations and moments of this new model are obtained. Also, a graphical analysis of the density is provided. Furthermore, we estimate parameters, propose asymptotic inference and discuss influence diagnostics by using likelihood methods for the new distribution. In particular, we show that the maximum likelihood estimates parameters of the new model under the t kernel are down-weighted for the outliers. Thus, smaller weights are attributed to outlying observations, which produce robust parameter estimates. Finally, an illustrative example with real data shows that the new distribution fits better to the data than some other well known probabilistic models.     

Data aggregation in stochastic frontier models: the closed skew normal distribution

The effect of aggregation on estimates of stochastic frontier functions is considered. Inefficiency is assumed associated with the individual units being aggregated. In this case, the aggregated data have a closed skew normal distribution. Estimating the parameters of a closed skew normal distribution is difficult and so we focus mostly on the biases created by ignoring the fact that the data are aggregated. The conclusions are based on both analytical and Monte Carlo results. When data for firms are aggregates over smaller units and the inefficiency is associated with the units and not the firm, empirical work that does not consider the effect of aggregation will attribute the inefficiency of large firms to diseconomies of scale.     

The sampling distribution of forecasts from a first-order autoregression

Previous work on characterising the distribution of forecast errors in time series models by statistics such as the asymptotic mean square error has assumed that observations used in estimating parameters are statistically independent of those used to construct the forecasts themselves. This assumption is quite unrealistic in practical situations and the present paper is intended to tackle the question of how the statistical dependence between the parameter estimates and the final period observations used to generate forecasts affects the sampling distribution of the forecast errors. We concentrate on the first-order autoregression and, for this model, show that the conditional distribution of forecast errors given the final period observation is skewed towards the origin and that this skewness is accentuated in the majority of cases by the statistical dependence between the parameter estimates and the final period observation.     

A Gaussian approximation scheme for computation of option prices in stochastic volatility models

We consider European options on a price process that follows the log-linear stochastic volatility model. Two stochastic integrals in the option pricing formula are costly to compute. We derive a central limit theorem to approximate them. At parameter settings appropriate to foreign exchange data our formulas improve computation speed by a factor of 1000 over brute force Monte Carlo making MCMC statistical methods practicable. We provide estimates of model parameters from daily data on the Swiss Franc to Euro and Japanese Yen to Euro over the period 1999–2002.     

Air service and urban growth: Evidence from a quasi-natural policy experiment

While significant work has been done to examine the determinants of regional development, there is little evidence on the role of air services. This paper exploits the large and swift changes to air traffic induced by the 1978 Airline Deregulation Act to identify the link between air traffic and local economic growth. Using data for 263 Metropolitan Statistical Areas (MSAs) over a two-decade time period, we estimate the effects of airline traffic on local population, income, and employment growth. Our most conservative estimates suggest that a 50-percent increase in an average city’s air traffic growth rate generates an additional stream of income over a 20-year period equal to 7.4 percent of real GDP, the equivalent of $523.3 million in 1978 dollars.     

Increase in Precision when Estimating Parameters in Computer Assisted Testing Using Response Time

A model is given in this article that associates response time to each item with its relative difficulty for the person examined. The properties of the model are presented with respect to the quantity of information, while deducing the parameter estimators. Likewise, an empirical study to prove the feasibility and plausibility of the model is carried out, feasibility being understood as the existence of adequate estimators for the parameters of the model and plausibility the adjustment of the model proposed to the items in a spatial aptitude test used to demonstrate this (Rotation of solid figures). For this, the model is tested with simulated data, comparing the behaviour of the estimates carried out on the model proposed with the estimates made by a programme such as BILOG-MG. It is shown that, with the exception of parameter c (pseudo-chance) which does not benefit from the inclusion of the response time, the estimates for the model are always higher for parameters a (discrimination) and b (difficulty) of the items, always giving rise to a higher correlation with the original data, in spite of the inefficiency of the computation algorithm employed. The results suggest that the use of response time supplies as much information or more than the use of very informative a priori distributions.     

The effect of labor supply changes on output: empirical evidence from US industries

In this paper we examine the relationship between labor supply and industry-level output in the context of the specific factors model. Jones (Trade, balance of payment and growth: essays in honor of Charles P. Kindleberger, Amsterdam, pp 3–21, 1971) shows that a rise in the amount of labor in the economy will increase the output in all industries. We empirically show which industry output is predicted to expand more when the size of labor force grows. Unlike the commonly used Rybczynski Theorem (Economica 22:336–341, 1955) of the Heckscher-Ohlin model, the specific factors model shows that a comparison both of labor intensities and labor demand elasticities plays an important role in determining which output expands relatively more when the size of labor force grows. For this purpose, we illustrate the importance of the parameters of the model in determining how changes in the labor supply affect the output change, with special reference to elasticities of substitution in production. We estimate the elasticity of substitution by using CES production function and show how these estimates describe the general equilibrium of production with one mobile factor (labor) and 25 industries of the US economy using data for 1979–2001. We show that the increase in the supply of labor raise output in all industries, but the magnitudes of the increases in some industries are more than others depending on the value of the elasticity of substitution along with factor intensities between industries. The largest output effect occurs for educational, health care and social service, where a 1 % supply of labor increase would raise output 10.5 %. However, the growth in the labor supply has a small impact on output growth in the range of 0.1–0.6 % in agriculture, petroleum, coal product and finance and insurance industries.     

Inferring monetary policy objectives with a partially observed state

Accounting for the uncertainty in real-time perceptions of the state of the economy is believed to be critical for monetary policy analysis. We investigate this claim through the lens of a New Keynesian model with optimal discretionary policy and partial information. Structural parameters are estimated using a data set that includes real-time and ex post revised observations spanning 1965–2010. In comparison to a standard complete information model, our estimates reveal that under partial information: (i) the Federal Reserve demonstrates a significant concern for stabilizing the output gap after 1979, (ii) the model׳s fit with revised data improves, and (iii) the tension between optimal and observed policy is smaller.     

Estimating structural equation models with non-normal variables by using transformations

We discuss structural equation models for non-normal variables. In this situation the maximum likelihood and the generalized least-squares estimates of the model parameters can give incorrect estimates of the standard errors and the associated goodness-of-fit chi-squared statistics. If the sample size is not large, for instance smaller than about 1000, asymptotic distribution-free estimation methods are also not applicable. This paper assumes that the observed variables are transformed to normally distributed variables. The non-normally distributed variables are transformed with a Box–Cox function. Estimation of the model parameters and the transformation parameters is done by the maximum likelihood method. Furthermore, the test statistics (i.e. standard deviations) of these parameters are derived. This makes it possible to show the importance of the transformations. Finally, an empirical example is presented.     

Euromind‐ : A Density Estimate of Monthly Gross Domestic Product for the Euro Area

EuroMInd‐ is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom‐up approach, pooling the density estimates of 11 GDP components, by output and expenditure type. The components' density estimates are obtained from a medium‐size dynamic factor model handling mixed frequencies of observation and ragged‐edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process the data sequentially as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules. Copyright © 2016 John Wiley & Sons, Ltd.     

Credit market disequilibrium in Poland: Can we find what we expect?: Non-stationarity and the short-side rule

This paper presents an empirical investigation of the disequilibrium hypothesis on the Polish loan market in the 1990s. Using data over this period of rapid and sustained transition, we estimate a disequilibrium model with a standard maximum likelihood method. However, the estimates are highly counter-intuitive as regards the timing of the identified regimes. We show that the gap between the econometric evidence and the expected results may stem from the phenomenon of stochastic non-stationarity in a disequilibrium setting based on the short-side rule. We find that the omission of one non-stationary variable of the cointegrating space or the absence of a “structural” cointegrating relationship in one or both regimes lead to a spurious configuration. In such a case, the wrong use of the standard likelihood function, derived under the stationarity assumption, may lead to non-convergent estimates of structural parameters and, hence, to an erroneous identification of the regimes. Therefore, as a first approach to this problem, we estimate a disequilibrium model with stationary data, and identify the disequilibrium as an excess of quantities of new loans supplied (or demanded) on the market at time t. The empirical results are then robust and economically founded and correspond to the set and the timing of expected regimes.     

Cooperation and conflict among nations: An application of multi-sample confirmatory factor analysis

The measurement of cooperation and conflict among nations differs greatly among several empirical studies of international relations. In the multidimensional approach to the measurement of both concepts, cooperation and conflict are considered to be unmeasured traits of various indicators in different areas of interest. Insight into the validity of the measurement theory can be gained from the fit of the estimated measurement model and from the estimates of the unknown parameters. These estimates are obtained through confirmatory factor analysis of data on observable policy Indicators of both concepts (see e.g., Jöreskog, 1969). The reliability of the estimated model can be investigated by means of multi-sample confirmatory factor analysis, which allows one to fit a specified model to several data sets simultaneously. Maximum likelihood estimates of the unknown parameters of one-sample and multi-sample confirmatory factor analysis models, based on data available in COPDAB, can be obtained by using the LISREL-VI program (see Jöreskog and Sörbom (1986). The results indicate that cooperation and conflict in foreign policies of nations have de-escalating effects upon each other.     

Bayesian Estimation of Willingness‐to‐pay Where Respondents Mis‐report Their Preferences*

We introduce a modified conditional logit model that takes account of uncertainty associated with mis‐reporting in revealed preference experiments estimating willingness‐to‐pay (WTP). Like Hausman et al. [Journal of Econometrics (1988) Vol. 87, pp. 239–269], our model captures the extent and direction of uncertainty by respondents. Using a Bayesian methodology, we apply our model to a choice modelling (CM) data set examining UK consumer preferences for non‐pesticide food. We compare the results of our model with the Hausman model. WTP estimates are produced for different groups of consumers and we find that modified estimates of WTP, that take account of mis‐reporting, are substantially revised downwards. We find a significant proportion of respondents mis‐reporting in favour of the non‐pesticide option. Finally, with this data set, Bayes factors suggest that our model is preferred to the Hausman model.     

US TFP growth and the contribution of changes in export and import prices to real income growth

Using recent data from the Bureau of Economic Analysis, the Bureau of Labour Statistics (BLS), the Board of Governors of the Federal Reserve System and the US Department of Agriculture, the paper calculates the multifactor productivity (MFP) Growth for an Extended Business Sector of the US economy for the years 1987–2011. The Extended Business Sector (EBS) consists of the entire US economy less the inputs used and outputs produced by the Public Administration sector and less the US housing sector. The study found that MFP growth in the EBS averaged 1.157 % per year, somewhat higher than BLS MFP growth in the Private Sector which averaged 0.962 % per year. The study also produced estimates of MFP where a net output concept was used. Finally, the effects of changes in the prices of US exports and imports on real income growth was calculated using a translog model based on the work of Diewert and Morrison and Kohli.