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The Bureau of Economic Analysis provides data from 1969 to 2016 regarding state-level and county-level unemployment costs. These data are used to construct least-squares estimations including linear growth, the persistence of business cycles, and the unique anomaly of the Great Recession. Each of these models is constructed for North Carolina data, including the state as a whole and each individual county in the state. The state and county models are compared for differences and insights.

In this chapter we explore different ways to obtain decompositions of rank-dependent indices of socioeconomic inequality of health, such as the Concentration Index. Our focus is on the regression-based type of decomposition. Depending on whether the regression explains the health variable, or the socioeconomic variable, or both, a different decomposition formula is generated. We illustrate the differences using data from the Ethiopia 2011 Demographic and Health Survey (DHS).

Long-run changes in living standards occupy an important place in development and growth economics, as well as in economic history. An extensive literature uses heights to study historical living standards. Most historical heights data, however, come from selected subpopulations such as volunteer soldiers, raising concerns about the role of selection bias in these results. Variations in sample mean heights can reflect selection rather than changes in population heights. A Roy-style model of the decision to join the military formalizes the selection problem. Simulations show that even modest differential rewards to the civilian sector produce a military heights sample that is significantly shorter than the cohort from which it is drawn. Monte Carlos show that diagnostics based on departure from the normal distribution have little power to detect selection. To detect height-related selection, we develop a simple, robust diagnostic based on differential selection by age at recruitment. A companion paper (H. Bodenhorn, T. Guinnane, and T. Mroz, 2017) uses this diagnostic to show that the selection problems affect important results in the historical heights literature.

This chapter investigates the predictability of the European monetary policy through the eyes of the professional forecasters from a large investment bank. The analysis is based on forward-looking Actual and Perceived Taylor Rules for the European Central Bank which are estimated in real-time using a newly constructed database for the period April 2000–November 2009. The former policy rule is based on the actual refi rate set by the Governing Council, while the latter is estimated for the bank’s economists using their main point forecast for the upcoming refi rate decision as a dependent variable. The empirical evidence shows that the pattern of the refi rate is broadly well predicted by the professional forecasters even though the latter have foreseen more accurately the increases rather than the policy rate cuts. Second, the results point to an increasing responsiveness of the ECB to macroeconomic fundamentals along the forecast horizon. Third, the rolling window regressions suggest that the estimated coefficients have changed after the bankruptcy of Lehman Brothers in October 2008; the ECB has responded less strongly to macroeconomic fundamentals and the degree of policy inertia has decreased. A sensitivity analysis shows that the baseline results are robust to applying a recursive window methodology and some of the findings are qualitatively unaltered from using Consensus Economics forecasts in the regressions.

This paper presents a decomposition forecast of stock prices using time series of weekly stock price data as implemented in Excel. The following decomposition components are presented, analyzed, and interpreted including a moving average, a trend, a periodic function, and two shock variables including a triangular shock variable and a level change. The results of the individual components are compared and a discussion of each component’s efficiency is provided. The trend component is statistically significant over the forecast time. The moving average component displays a bi-modal error distribution over varying spans of the moving average and forecast periods. The first mode coincides with random walk behavior with an optimal span and forecast period of one. The second mode is more interesting and applicable for investing beyond the short-term with an optimal spans and forecast periods beyond 75 weeks. The periodic sine function well captures the typical U.S. business cycle of 4–5 years and significantly improves model performance. Finally, the significant outliers remaining from the decomposition are diagnosed and modeled with a triangular shock variable for the bust and recovery associated with the 2008 financial crisis. The model presented does a good job of decomposing the analytical components in forecasting stock prices and provides a useful illustration of Excel methods.

A common feature of certain kinds of data is a high level of statistical dependence across space and time. This spatial and temporal dependence contains useful information that can be exploited to significantly reduce the uncertainty surrounding local distributions. This chapter develops a methodology for inferring local distributions that incorporates these dependencies. The approach accommodates active learning over space and time, and from aggregate data and distributions to disaggregate individual data and distributions. We combine data sets on Kansas winter wheat yields – annual county-level yields over the period from 1947 through 2000 for all 105 counties in the state of Kansas, and 20,720 individual farm-level sample moments, based on ten years of the reported actual production histories for the winter wheat yields of farmers participating in the United States Department of Agriculture Federal Crop Insurance Corporation Multiple Peril Crop Insurance Program in each of the years 1991–2000. We derive a learning rule that combines statewide, county, and local farm-level data using Bayes’ rule to estimate the moments of individual farm-level crop yield distributions. Information theory and the maximum entropy criterion are used to estimate farm-level crop yield densities from these moments. These posterior densities are found to substantially reduce the bias and volatility of crop insurance premium rates.

Many developing and transition countries, and even some in the industrialized West, experience periods in which a substantial proportion of the workforce suffer wage arrears. We examine the implications for estimates of wage gaps and inequality using the Russian labor market as a test case. Wage inequality grew rapidly as did the incidence of wage arrears in Russia in the 1990s. Given data on wages and the incidence of wage arrears we construct counterfactual wage distributions, which give the distribution of pay were arrears not present. The results suggest that wage inequality could be some 30 percent lower in the absence of arrears. If individuals in arrears are distributed across the underlying wage distribution, as appears to be the case in Russia, we show that it may be feasible to use the wage distribution for the subset of those not in arrears to estimate the underlying population wage distribution parameters.

In this chapter, we investigate the effect of long memory in volatility on the accuracy of emerging stock markets risk estimation during the period of the recent global financial crisis. For this purpose, we use a short (GJR-GARCH) and long (FIAPARCH) memory volatility models to compute in-sample and out-of-sample one-day-ahead VaR. Using six emerging stock markets index, we show that taking into account the long memory property in volatility modelling generally provides a more accurate VaR estimation and prediction. Therefore, conservative risk managers may adopt long memory models using GARCH-type models to assess the emerging market risks, especially when incorporating crisis periods.