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This chapter proposes a vector autoregressive VAR model with structural shocks (SVAR) that are identified using sign restrictions, and whose distribution is subject to time varying skewness. The authors also present an efficient Bayesian algorithm to estimate the model. The model allows tracking joint asymmetric risks to macroeconomic variables included in the SVAR, and provides a structural narrative to the evolution of those risks. When faced with euro area data, our estimation suggests that there has been a significant variation in the skewness of demand, supply and monetary policy shocks. Such variation can explain a significant proportion of the joint dynamics of real GDP growth and inflation, and also generates important asymmetric tail risks in those macroeconomic variables. Finally, compared to the literature on growth- and inflation-at-risk, the authors find that financial stress indicators are not enough to explain all the macroeconomic tail risks.

A majority of products for manufacturing or consumers have multiple characteristics that must meet the requirements of the customer. For example, a steel beam any have dimensional tolerances on its length, width, or height and functional tolerances on its strength. The characteristics are influenced by different processes that create the product. For an individual characteristic, process capability measures exist that convey the degree to which the characteristic meets the specification requirements. Such measures may indicate the proportion of nonconforming product related to the particular characteristic, under some distributional assumptions of the characteristic. For products with multiple characteristics, the unit costs of rectification may be different, making the satisfaction of some characteristics meeting customer requirements more important than others. In this paper, an aggregate process capability performance measure is developed that considers the relative importance of the characteristic based on unit costs of nonconformance. Based on the aggregate measure, appropriate process capability measures for the individual measures are also derived. Bounds on the aggregate capability measures are also established.

Application of financial risk models in the emerging markets poses special challenges. A fundamental challenge is to accurately model the return distributions which are particularly fat tailed and skewed. Value-at-Risk (VaR) measures based on the Extreme Value Theory (EVT) have been suggested, but typically data histories are limited, making it hard to test and apply EVT. The chapter addresses issues in (i) modeling the VaR measure in the presence of structural breaks in an economy, (ii) the choice of stable innovation distribution with volatility clustering effects, (iii) modeling the tails of the empirical distribution, and (iv) fixing the cut-off point for isolating extreme observations. Pakistan offers an instructive case since its equity market exhibits high volatility and incidence of extreme returns. The recent Global Financial Crisis has been another source of extreme returns. The confluence of the two sources of volatility provides us with a rich data set to test the VaR/EVT model rigorously and examine practical challenges in its application in an emerging market.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

Bertil Ohlin was a most active commentator on current economic events in the interwar period, combining his academic work with a journalistic output of an impressive scale. He published more than a thousand newspaper articles in the 1920s and 1930s, more than any other professor in economics in Sweden.

Here we have collected 10 articles by Ohlin, translated from Swedish and originally published in Stockholms-Tidningen, to trace the evolution of his thinking during the Great Depression of the 1930s. These articles, spanning roughly half a decade, bring out his response to the stock market crisis in New York in 1929, his views on monetary policy in 1931, on fiscal policy and public works in 1932, his reaction to Keynes’ ideas in 1932 and 1933 and to Roosevelt’s New Deal in 1933, and, finally, his stand against state socialism in 1935.

At the beginning of the depression, Ohlin was quite optimistic in his outlook. But as the downturn in the world economy deepened, his optimism waned. He dealt with proposals for bringing the Swedish economy out of the depression, and reported positively on the policy views of Keynes. At an early stage, he recommended expansionary fiscal and monetary policies including public works. This approach permeated the contributions of the young generation of Swedish economists arising in the 1930s, eventually forming the Stockholm School of Economics. He was critical of passive Manchester liberalism, ‘folded-arms evangelism’ as well of socialism while promoting his own brand of ‘active social liberalism’.

The efficient distribution of bank notes is a first-order responsibility of central banks. The authors study the distribution patterns of bank notes with an administrative dataset from the Bank of Canada’s Currency Inventory Management Strategy. The single note inspection procedure generates a sample of 900 million bank notes in which the authors can trace the length of the stay of a bank note in the market. The authors define the duration of the bank note circulation cycle as beginning on the date the bank note is first shipped by the Bank of Canada to a financial institution and ending when it is returned to the Bank of Canada. In addition, the authors provide information regarding where the bank note is shipped and later received, as well as the physical fitness of the bank note upon return to the Bank of Canada’s distribution centers. K–prototype clustering classifies bank notes into types. A hazard model estimates the duration of bank note circulation cycles based on their clusters and characteristics. An adaptive elastic net provides an algorithm for dimension reduction. It is found that while the distribution of the duration is affected by fitness measures, their effects are negligible when compared with the influence exerted by the clusters related to bank note denominations.

This chapter proposes a nonparametric estimator of the risk neutral density (RND) based on cross-sectional European option prices. The authors recast the arbitrage-free equation for option pricing as a functional linear regression model where the regressor is a curve and the independent variable is a scalar corresponding to the option price. Then, the authors show that the RND can be viewed as the solution of an ill-posed integral equation. To estimate the RND, the authors use an iterative method called Landweber-Fridman (LF). Then, the authors establish the consistency and asymptotic normality of the estimated RND. These results can be used to construct a confidence interval around the curve. Finally, some Monte Carlo simulations and application to the S&P 500 options show that this method performs well compared to alternative methods.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).