Search results for "205"

On Independent Component Analysis with Stochastic Volatility Models

2017

Consider a multivariate time series where each component series is assumed to be a linear mixture of latent mutually independent stationary time series. Classical independent component analysis (ICA) tools, such as fastICA, are often used to extract latent series, but they don't utilize any information on temporal dependence. Also financial time series often have periods of low and high volatility. In such settings second order source separation methods, such as SOBI, fail. We review here some classical methods used for time series with stochastic volatility, and suggest modifications of them by proposing a family of vSOBI estimators. These estimators use different nonlinearity functions to…

Properties of Design-Based Functional Principal Components Analysis.

2010

This work aims at performing Functional Principal Components Analysis (FPCA) with Horvitz-Thompson estimators when the observations are curves collected with survey sampling techniques. One important motivation for this study is that FPCA is a dimension reduction tool which is the first step to develop model assisted approaches that can take auxiliary information into account. FPCA relies on the estimation of the eigenelements of the covariance operator which can be seen as nonlinear functionals. Adapting to our functional context the linearization technique based on the influence function developed by Deville (1999), we prove that these estimators are asymptotically design unbiased and con…

Comments on “Unobservable Selection and Coefficient Stability

2019

Abstract–: We establish a link between the approaches proposed by Oster (2019) and Pei, Pischke, and Schwandt (2019) which contribute to the development of inferential procedures for causal effects in the challenging and empirically relevant situation where the unknown data-generation process is not included in the set of models considered by the investigator. We use the general misspecification framework recently proposed by De Luca, Magnus, and Peracchi (2018) to analyze and understand the implications of the restrictions imposed by the two approaches.

Olley–Pakes productivity decomposition: computation and inference

2016

Summary We show how a moment-based estimation procedure can be used to compute point estimates and standard errors for the two components of the widely used Olley–Pakes decomposition of aggregate (weighted average) productivity. When applied to business level microdata, the procedure allows for autocovariance and heteroscedasticity robust inference and hypothesis testing about, for example, the coevolution of the productivity components in different groups of firms. We provide an application to Finnish firm level data and find that formal statistical inference casts doubt on the conclusions that one might draw on the basis of a visual inspection of the components of the decomposition.

Financial distress and real economic activity in Lithuania: a Granger causality test based on mixed-frequency VAR

2020

In this paper, we extend the monthly financial stress index for Lithuania, computed by the European Central Bank, to a daily frequency and we also include banking sector stress among its constituents, beyond bond, equity and foreign exchange markets. We investigate the causal relationship between the daily financial stress index and monthly industrial production growth, using a Granger causality test applied to a mixed-frequency VAR. Our results suggest evidence of Granger causality from financial stress to industrial production growth once the index is enriched by daily observations from the financial markets. Our findings, based on impulse response analysis, confirm the negative effect of…

The Age Structure of Human Capital and Economic Growth

2018

This paper shows that the age structure of human capital is a relevant characteristic to take into account when analysing the role of human capital in economic growth. The effect of an increase in the education of the population aged 40–49 years is found to be an order of magnitude larger than an increase in the education attained by any other age cohort. The results are unlikely to be driven by the age structure of the population, as we find that the effects on growth of the age structure of education and the age structure of population are distinct. The findings are robust across specifications and remain unchanged when we control for long‐delayed effects in human capital or for the exper…

Explaining German outward FDI in the EU: a reassessment using Bayesian model averaging and GLM estimators

2021

The last decades have seen an increasing interest in FDI and the process of production fragmentation. This has been particularly important for Germany as the core of the European Union (EU) production hub. This paper attempts to provide a deeper under standing of the drivers of German outward FDI in the EU for the period 1996–2012 by tackling the two main challenges faced in the modelization of FDI, namely the variable selection problem and the choice of the estimation method. For that purpose, we first extend previous BMA analysis developed by Camarero et al. (Econ Model 83:326–345, 2019) by including country-pair-fixed effects to select the appropriate set of variables. Second, we compare…

Latent class models for multiple ordered categorical health data: testing violation of the local independence assumption

2019

Latent class models are now widely applied in health economics to analyse heterogeneity in multiple outcomes generated by subgroups of individuals who vary in unobservable characteristics, such as genetic information or latent traits. These models rely on the underlying assumption that associations between observed outcomes are due to their relationship to underlying subgroups, captured in these models by conditioning on a set of latent classes. This implies that outcomes are locally independent within a class. Local independence assumption, however, is sometimes violated in practical applications when there is uncaptured unobserved heterogeneity resulting in residual associations between c…

2019

In the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we study an ICA method which combines the use of linear and quadratic autocorrelations to enable efficient estimation of various kinds of stationary time series. Statistical properties of the estimator are studied by finding its limiting distribution under general conditions, and the asymptotic variances are derived in the case of ARMA-GARCH model. We use the asymptotic results and a finite sample simulation study to compare different choices of a weight coef…

Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo

2020

We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context of Bayesian latent variable models, the MCMC typically operates on the hyperparameters, and the subsequent weighting may be based on IS or sequential Monte Carlo (SMC), but allows for multilevel techniques as well. The IS approach provides a natural alternative to delayed acceptance (DA) pseudo-marginal/particle MCMC, and has many advantages over DA, including a straightforward parallelisation and additional flexibility in MCMC implementation. We detail minimal conditions which ensure strong consistency of the sug…