C14 - Semiparametric and Nonparametric Methods: General

• Non-Parametric Identification and Testing of Quantal Response Equilibrium

  Staff Working Paper 2024-24 Johannes Hoelzemann, Ryan Webb, Erhao Xie

  We show that the utility function and the error distribution are non-parametrically over-identified under Quantal Response Equilibrium (QRE). This leads to a simple test for QRE. We illustrate our method in a Monte Carlo exercise and a laboratory experiment.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Economic models JEL Code(s): C, C1, C14, C5, C57, C9, C92

• Comparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response Models

  Staff Working Paper 2022-31 Xiangjin Shen, Iskander Karibzhanov, Hiroki Tsurumi, Shiliang Li

  We use graphic processing unit computing to compare Bayesian and sample theory semiparametric binary response models. Our findings show that optimal bandwidth does not outperform regular bandwidth in binary semiparametric models.

  Content Type(s): Staff research, Staff working papers Topic(s): Credit risk management, Econometric and statistical methods JEL Code(s): C, C1, C14, C3, C35, C5, C51, C6, C63, D, D1

• Covariates Hiding in the Tails

  Staff Working Paper 2021-45 Milian Bachem, Lerby Ergun, Casper G. de Vries

  We characterize the bias in cross-sectional Hill estimates caused by common underlying factors and propose two simple-to-implement remedies. To test for the presence, direction and size of the bias, we use monthly US stock returns and annual US Census county population data.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C0, C01, C1, C14, C5, C58

• Maturity Composition and the Demand for Government Debt

  Staff Working Paper 2020-29 Jason Allen, Jakub Kastl, Milena Wittwer

  The main objectives of debt management are to raise stable and low-cost funding to meet the government’s financial needs and to maintain a well-functioning market for government securities.

  Content Type(s): Staff research, Staff working papers Topic(s): Debt management, Financial markets JEL Code(s): C, C1, C14, D, D4, D44, E, E5, E58, G, G1, G12

• Identifying Consumer-Welfare Changes when Online Search Platforms Change Their List of Search Results

  Staff Working Paper 2020-5 Ryan Martin

  Online shopping is often guided by search platforms. Consumers type keywords into query boxes, and search platforms deliver a list of products. Consumers' attention is limited, and exhaustive searches are often impractical.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Market structure and pricing JEL Code(s): C, C1, C14, D, D1, D11, D12, D6, D8, D83, L, L4, L40

• Extreme Downside Risk in Asset Returns

  Staff Working Paper 2019-46 Lerby Ergun

  Financial markets can experience sudden and extreme downward movements. Investors are highly concerned about the performance of their assets in such scenarios. Some assets perform badly in a downturn in the market; others have milder reactions.

  Content Type(s): Staff research, Staff working papers Topic(s): Asset pricing, Econometric and statistical methods JEL Code(s): C, C1, C14, G, G1, G11, G12

• Tail Index Estimation: Quantile-Driven Threshold Selection

  Staff Working Paper 2019-28 Jon Danielsson, Lerby Ergun, Casper G. de Vries, Laurens de Haan

  The most extreme events, such as economic crises, are rare but often have a great impact. It is difficult to precisely determine the likelihood of such events because the sample is small.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Financial stability JEL Code(s): C, C0, C01, C1, C14, C5, C58

• Characterizing the Canadian Financial Cycle with Frequency Filtering Approaches

  Staff Analytical Note 2018-34 Andrew Lee-Poy

  In this note, I use two multivariate frequency filtering approaches to characterize the Canadian financial cycle by capturing fluctuations in the underlying variables with respect to a long-term trend. The first approach is a dynamically weighted composite, and the second is a stochastic cycle model.

  Content Type(s): Staff research, Staff analytical notes Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Financial stability, Monetary and financial indicators, Recent economic and financial developments JEL Code(s): C, C0, C01, C1, C13, C14, C18, C3, C32, C5, C51, C52, E, E3, E32, E6, E66, G, G0, G01, G1, G18

• Challenges in Implementing Worst-Case Analysis

  Staff Working Paper 2018-47 Jon Danielsson, Lerby Ergun, Casper G. de Vries

  Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach.

  Content Type(s): Staff research, Staff working papers Topic(s): Financial stability JEL Code(s): C, C0, C01, C1, C14, C5, C58

• A Look Inside the Box: Combining Aggregate and Marginal Distributions to Identify Joint Distributions

  Staff Working Paper 2018-29 Marie-Hélène Felt

  This paper proposes a method for estimating the joint distribution of two or more variables when only their marginal distributions and the distribution of their aggregates are observed. Nonparametric identification is achieved by modelling dependence using a latent common-factor structure.

  Content Type(s): Staff research, Staff working papers Topic(s): Bank notes, Digital currencies and fintech, Econometric and statistical methods JEL Code(s): C, C1, C14, D, D1, D14, E, E4, E41