Unobserved Components Research Articles

The drop in female homicide victimisation in Australia

Despite widespread community concern about the incidence of lethal violence against women in Australia, the numbers and rates of women murdered each year have been falling for almost 30 years. The risk of homicide victimisation among Australian women is now the lowest it has ever been since federation. Few studies have explored these patterns or sought to explain their development. One plausible explanation, anticipated by feminist theories of violence, is that homicide has declined as the status of women and gender equality have increased. In this paper, I analyse changes in the annual female homicide victimisation rate in Australia from 1962 to 2016. Using unobserved components models, I investigate the extent to which increases in either the status of women or gender equality can account for the decline in female homicide victimisation since the late 1980s. Despite absolute and relative advances in women's educational access and achievement, labour force participation, and earnings, those changes had little discernible impact on the aggregate-level risk of lethal violence against women. Instead, other social and economic conditions, such as falling unemployment, the shrinking of the young male population and ongoing urbanisation, seem better able to explain recent improvements in women's safety.

When Should You Adjust Standard Errors for Clustering?

Abstract Clustered standard errors, with clusters defined by factors such as geography, are widespread in empirical research in economics and many other disciplines. Formally, clustered standard errors adjust for the correlations induced by sampling the outcome variable from a data-generating process with unobserved cluster-level components. However, the standard econometric framework for clustering leaves important questions unanswered: (i) Why do we adjust standard errors for clustering in some ways but not others, for example, by state but not by gender, and in observational studies but not in completely randomized experiments? (ii) Is the clustered variance estimator valid if we observe a large fraction of the clusters in the population? (iii) In what settings does the choice of whether and how to cluster make a difference? We address these and other questions using a novel framework for clustered inference on average treatment effects. In addition to the common sampling component, the new framework incorporates a design component that accounts for the variability induced on the estimator by the treatment assignment mechanism. We show that, when the number of clusters in the sample is a nonnegligible fraction of the number of clusters in the population, conventional clustered standard errors can be severely inflated, and propose new variance estimators that correct for this bias.

Impact of the COVID-19 pandemic and its control measures on cardiovascular and antidiabetic drugs use in France in 2020: a nationwide repeated cohort study.

Since pandemic start, patients may have faced difficulties in accessing to care and treatment. This study aimed at assessing the impact of COVID-19 pandemic and its control measures on the use of drugs indicated in cardiovascular prevention and diabetes mellitus in France. From 09/17/2018 to 09/20/2020, a repeated cohort analysis was performed using the French nationwide health insurance databases. The pandemic impact was assessed using time-series analyses and unobserved components model for the weekly number of patients with (i) drug dispensing, (ii) ongoing treatment, (iii) treatment initiation, (iv) treatment disruption. Overall, 14,822,132 patients with cardiovascular drug dispensings and 3,231,618 with antidiabetic ones were identified. After a sharp spike in the amount of dispensings in the week the first national lockdown was announced, the period was marked by decreased levels and trends. Altogether, the estimated impact of the pandemic on dispensings appeared limited over the lockdown period (1–3% lack in dispensings). During lockdown, the weekly numbers of treatment disruptions remained stable whereas a significant decrease in treatment initiations was observed for almost all drug classes (e.g. β-blockers initiations: − 8.9%). Conversely, the post-lockdown period showed increases in treatment disruptions especially for antihypertensive and lipid lowering drugs (e.g. statins disruptions: + 4.9%). The pandemic and associated measures had a significant impact on cardiovascular and antidiabetic drugs use in France, mostly consisting in decreases of treatment initiations over lockdown and increases in treatment disruptions afterwards. Both could result in increased morbimortality that remains to be assessed.

Shock Effects on Tourism Demand Cycles to Bali

Various kinds of shocks can influence tourism demand cycles. However, previous studies have typically concentrated within economic frameworks in explaining variations of tourism demand cycles, ignoring the possibility of different shocks influencing tourism demand cycles. This paper analyses various shocks in explaining the variation of tourism demand cycles to Bali on different source market countries. Monthly tourist arrivals data from January 2002 to January 2020 are decomposed into cyclical and irregular components. The cyclical patterns represent tourism demand cycles, and shock events are represented by irregular component patterns of international tourist arrivals data series. This study employs an unobserved component model (UCM) procedure to decompose the data, unit root tests to examine data stationarity, and logistic regression to analyse the relationship between shock effects on tourism demand cycles. The unit root test result indicates that shock effects on tourism demand cycles are not permanent. However, the persistence and magnitude of shock effects vary between source market countries. Shock effects could be persistent and influence the negative phase of the cycles for several countries. The persistence and magnitude of shock effects are the highest for tourist arrivals from the United States, India, and Germany. The results provide theoretical and managerial implications.

Cognitive and non-cognitive abilities of immigrants: New perspectives on migrant quality from a selective immigration country

We study innate ability – a traditionally unobserved component of migrant quality – in Australia, one of the top six destination countries worldwide. We proxy innate ability with validated and widely used measures of non-cognitive (personality) and cognitive (task performance) skills. We find that both first- and second-generation immigrants consistently outperform Australian-born residents with no immediate migration background (natives) on socially beneficial personality traits (Conscientiousness, Openness to Experience) and cognitive ability measures associated with high levels of executive function (coding speed), independent of their gender. The migrant pool resulting from the phase in of a selective immigration policy since 1974 was significantly better educated and more diversified in terms of language background relative to earlier arrivals. Although this meant that the point system attracted migrants with poorer English-language skills relative to natives with equal educational attainment, it did not change their innate ability composition or labor market outcomes. We conclude that selecting migrants based on observable quality characteristics did not lead to a migrant pool of lower quality in traditionally unobservable characteristics in the Australian immigration context.

Dynamic generalised additive models (DGAMs) for forecasting discrete ecological time series

Abstract Generalised additive models (GAMs) are increasingly popular tools for estimating smooth nonlinear relationships between predictors and response variables. GAMs are particularly relevant in ecology for representing hierarchical functions for discrete responses that encompass complex features including zero inflation, truncation and uneven sampling. However, GAMs are less useful for producing forecasts as their smooth functions provide unstable predictions outside the range of training data. We introduce dynamic generalised additive models (DGAMs), where the GAM linear predictor is jointly estimated with unobserved dynamic components to model time series that evolve as a function of nonlinear predictor associations and latent temporal processes. These models are especially useful for analysing multiple series, as they can estimate hierarchical smooth functions while learning complex temporal associations via dimension‐reduced latent factor processes. We implement our models in the mvgam R package, which estimates unobserved parameters for smoothing splines and latent temporal processes in a probabilistic framework. Using simulations, we illustrate how our models outperform competing formulations in realistic ecological forecasting tasks while identifying important smooth predictor functions. We use a real‐world case study to highlight some of mvgam's key features, which include functions for calculating correlations among series' latent trends, performing model selection using rolling window forecasts and posterior predictive checks, online data augmentation via a recursive particle filter and visualising probabilistic uncertainties for smooth functions and predictions. Dynamic GAMs (DGAMs) offer a solution to the challenge of forecasting discrete time series while estimating ecologically relevant nonlinear predictor associations. Our Bayesian latent factor approach will be particularly useful for exploring competing dynamic ecological models that encompass hierarchical smoothing structures while providing forecasts with robust uncertainties, tasks that are becoming increasingly important in applied ecology.

Experimental Composite Indicators of Cyclical Response in Surveys of Consumer Expectations of the Population

The article proposes a theoretical rationale and procedure for constructing new composite indicators that combine the results of quarterly consumer expectations surveys conducted by the Federal State Statistics Service (Rosstat). The information capabilities of surveys for the period 2005–2021 were studied to find the best alternative to the traditional consumer confidence index in the context of early estimates of the growth in household final consumption. The experimental Consumer Behavior Index reflects the «common profile» in the changes in the key parameters of consumer activity. The index is based on time series of primary survey indicators with high cross-correlation coefficients and a confirmed statistically significant causal relationship with a statistical referent – the growth rate of the final consumption expenditure of households. The new Consumer Anxiety Index with a counter-directional profile assesses the scale and tendencies of household response to market shocks based on short-term gaps between the indicator components and their long-term average.In accordance with the proposed procedure for statistical testing of the time series, a cross-correlation analysis was carried out. Granger causality was studied, principal component analyses applied, unobservable cyclical components were identified in the dynamics of the Consumer Behavior Index and the referent using Hodrick – Prescott, Christian – Fitzgerald and Kalman statistical filters, and phase-by-phase movement of these components is demonstrated. A statistically significant cyclical correspondence and a close relationship of leading and coinciding nature between the composite survey-based index and the growth rate of the final consumption expenditure of households were confirmed.

Covid-19, credit risk management modeling, and government support

We investigate rating and default risk dynamics over the covid-19 crisis from a credit risk modeling perspective. We find that growth dynamics remain a stable and sufficient predictor of credit risk incidence over the pandemic period, despite its large, short-lived swings due to government intervention and lockdown. Unobserved component models as used in the recent credit risk literature appear mainly helpful for explaining the high-default wave in the early 2000s, but less so for default prediction above and beyond growth dynamics during the 2008 financial crisis or the early 2020 covid default peak. Government support variables do not reduce the impact of either growth proxies or unobserved components. Correlations between government support and credit risk are different, however, during the financial and the covid crisis. Using the empirical models in this paper as credit risk management tools, we show that growth factors also suffice to predict credit risk quantiles out-of-sample during covid times.

Dynamics and synchronization of global equilibrium interest rates

ABSTRACT Empirical estimates of equilibrium real interest rates are so far mostly limited to advanced economies, since no statistical procedure suitable for a larger set of countries is available. This is surprising, as equilibrium rates have strong policy implications in emerging markets and developing economies as well. Current estimates of the global equilibrium rate rely on only a few countries but estimates for a more diverse set of countries can improve understanding of its dynamics and drivers. This paper uses a model and estimation strategy that decompose ex-ante real interest rates into permanent and transitory components even with short samples and high volatility. This is done with an unobserved component local level stochastic volatility model, which is used to estimate equilibrium rates for 50 countries with Bayesian methods. Equilibrium rates were lower in emerging markets and developing economies than in advanced economies in the 1980s, similar in the 1990s, and have been higher since 2000. In line with economic integration and rising global capital markets, synchronization has been rising over time and is higher among advanced economies. Equilibrium rates of countries with stronger trade linkages and similar demographic and economic trends are more synchronized.

Use of mathematical modeling and its inverse analysis for precise assessment of pesticide dissipation in a paddy environment.

The extrapolability of the lysimeter test as a dissipation simulator in an actual paddy field was evaluated using mathematical models and their inverse analyses for predicting pesticide fate and transport processes in paddy test systems. As a source of experimental data, a four-year comparative experiment in lysimeters and paddy fields was conducted using various paddy pesticides. First, the dissipations for various active ingredients in granule pesticides under submerged applications were statistically compared using simple kinetic modeling. Second, the dissipation pathways, unobserved experimental components, and effect of the experimental setting were evaluated using a higher tier mathematical model with a novel inverse analysis protocol. Finally, owing to experimental constraints, the unobtainable parameters were extracted from the laboratory container test before being transferred to compare the outdoor experimental data under different formulation types.

Dynamic deconvolution and identification of independent autoregressive sources

We consider a multi‐variate system , where the unobserved components are independent AR(1) processes and the number of sources is greater than the number of observed outputs. We show that the mixing matrix , the AR(1) coefficients and distributions of can be identified (up to scale factors of ), which solves the dynamic deconvolution problem. The proof is constructive and allows us to introduce simple consistent estimators of all unknown scalar and functional parameters of the model. The approach is illustrated by an estimation and identification of the dynamics of unobserved short‐ and long‐run components in a time series. Applications to causal models with structural innovations are also discussed, such as the identification in error‐in‐variables models and causal mediation models.

Inflation Measured Every Day Keeps Adverse Responses Away: Temporal Aggregation and Monetary Policy Transmission

Using daily inflation data from the Billion Prices Project [Cavallo and Rigobon (2016)], we show how temporal aggregation biases estimates of monetary policy transmission. We argue that the information mismatch between private agents and the econometrician —the source of temporal aggregation bias —is equally important as the more studied mismatch between private agents and the central bank (the “Fed information effect”). We find that the adverse response of daily inflation to high-frequency monetary policy shocks is short-lived, if present at all, in impulse responses from both local projections and an unobserved components model of inflation dynamics. To reconcile how one can obtain a sizable adverse response with monthly or quarterly data when only a limited adverse response exists at a higher frequency, we appeal to a simple monetary policy model and show how temporal aggregation bias can exacerbate initial impulse response functions. Because our modeling results are generic and macroeconomic indicators are published with a lag, we argue that temporal aggregation bias will be a key feature of the nascent field of high-frequency macroeconomics.

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series.

Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use. This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques. We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting. When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account. e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States.

Trend Inflation in Sweden

AbstractIn this article, we estimate trend inflation in Sweden using an unobserved components stochastic volatility model. Using data from 1995Q4 to 2022Q1 and Bayesian estimation methods, we find that trend inflation has been well‐anchored during the period—although in general at a level below the inflation target—and it does not appear to have been affected much by the recent high inflation numbers.

Modeling tail risks of inflation using unobserved component quantile regressions

This paper proposes methods for Bayesian inference in time-varying parameter (TVP) quantile regressions (QRs) featuring conditional heteroskedasticity. I use data augmentation schemes to render the model conditionally Gaussian and develop an efficient sampling algorithm. Regularization of the high-dimensional parameter space is achieved via dynamic shrinkage priors. The merits of the proposed approach are illustrated in a simulation study, and a simple version of TVP-QR based on an unobserved components model is applied to dynamically trace the quantiles of inflation in the United States, the United Kingdom and the euro area. In an out-of-sample forecast exercise, I find the proposed model to be competitive and perform particularly well for higher-order and tail forecasts. A detailed analysis of the resulting predictive distributions reveals that they are sometimes skewed and occasionally feature heavy tails.

An unobserved components model of total factor productivity and the relative price of investment

AbstractThis paper applies the common stochastic trends representation approach to the time series of total factor productivity (TFP) and the relative price of investment (RPI) to investigate the modeling of neutral technology (NT) and investment-specific technology (IST) and its econometric ramifications on the analysis of general equilibrium model. The permanent and transitory movements in both series are estimated efficiently via Markov chain Monte Carlo methods using band matrix algorithms. The results indicate that TFP and the RPI are, each, well represented by a differenced first-order autoregressive process. In addition, their time series share a common trend component that we interpret as reflecting changes in general purpose technology. These results are consistent with studies that suggest that (1) the traditional view of assuming that NT and IST follow independent processes is not supported by the features of the time series and (2) improper specification of secular trends may distort estimation and inference. Notably, the findings provide some guidance to minimize the effect of idiosyncratic and common trend misspecifications on the analysis of impulse dynamics and propagation mechanisms in macroeconomic models.

Comparing Results from Unobserved Components Model and Hodrick-Prescott Filter of Output-Gap in Barbados

An important macro-economic issue of developed and developing countries is how best to decompose an economic time series into permanent (trend) and transitory (cycle) components. The issue is vital in empirical macroeconomics since, among other things, it relates to how one can estimate the output gap-the deviation of an economy's output from its potential or trend output. This paper considers how well the unobserved components model and the Hodrick Prescott (HP) filter decomposes real gross domestic product (GDP) in a small island developing state, the state of Barbados. The correlated unobserved components model for Barbados studied in the Agbeyegbe (2020) is modified to allow a second-order Markov trend. The effect of this modification is to make it possible to recover the HP trend as a particular case. The paper then compares several methods useful for trend decomposition of real GDP in Barbados. The competing methods are variants of two widely used trend-cycle decompositions of GDP that give markedly different estimates. Namely, methods based on the unobserved components model (UC) and the HP filter. The correlated unobserved components model produces smaller output gaps in amplitude, whereas the HP filter generates significant and persistent cycles. More specifically, the methods are: (i) the HP filter; (ii) an augmented HP filter (HP-AR), that allows for cyclical components to be serially correlated, introduced by Grant and Chan (2017b); (iii) the correlated unobserved components model (UCUR), without a break; (iv) the correlated unobserved components model (UCUR-t), with a break at time t; and (v) a correlated unobserved components model that allows for a second-order Markov trend process UCUR-2M. The result shows that for Barbados, with data covering the period 1967-2017, the correlated unobserved components model that allows for a break in trend fits the data better than the HP specification. These results are significant from a policy perspective. Knowing the correct duration of the business cycle is essential to providing appropriate recommendations; the result argues against the use of HP-filter in analyzing Barbados' business cycle. The result also finds that for Barbados, it is empirically important to correlate permanent and transitory shocks. By ignoring this correlation, researchers risk providing a misleading analysis of how the Barbadian economy works.

Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019

This paper presents a computational model based on interval type-2 fuzzy systems for analysis and forecasting of COVID-19 dynamic spreading behavior. The proposed methodology is related to interval type-2 fuzzy Kalman filters design from experimental data of daily deaths reports. Initially, a recursive spectral decomposition is performed on the experimental dataset to extract relevant unobservable components for parametric estimation of the interval type-2 fuzzy Kalman filter. The antecedent propositions of fuzzy rules are obtained by formulating a type-2 fuzzy clustering algorithm. The state space submodels and the interval Kalman gains in consequent propositions of fuzzy rules are recursively updated by a proposed interval type-2 fuzzy Observer/Kalman Filter Identification (OKID) algorithm, taking into account the unobservable components obtained by recursive spectral decomposition of epidemiological experimental data of COVID-19. For validation purposes, through a comparative analysis with relevant references of literature, the proposed methodology is evaluated from the adaptive tracking and forecasting of COVID-19 dynamic spreading behavior, in Brazil, with the better results for RMSE of 1.24×10−5, MAE of 2.62×10−6, R2 of 0.99976, and MAPE of 6.33×10−6.

주택가격요인 분석에 대한 연구

The volatility of the housing price based on demand and supply in the housing market reflects the structural features of the housing market. Consequently, decomposing the housing market into trends and cycles through econometric models will make it possible to deduce the nature of the housing market in the long term, in addition to provide hindsight for real estate portfolio asset allocation in a more systematic way.BRBR The paper addresses the housing price behavior through univariate time series analysis. The Unobserved-Component (UC) model and the Beveridge-Nelson (BN) decomposition model are applied to analyze the factors for the volatility of housing price with the trends and cycles. Specifically, the UC model is analyzed by UC-Zero(0) model assuming the covariance between the trends and the cycles, which are unobserved variables, is zero (0), and the UC-UR (UC-UnRestricted) model, which is free from the restraint of the covariance between the trends and cycles. The estimation is based on the Kalman Filter. The study analyzes how the volatility in the domestic housing price index was influenced by the trends and cycles, which are inherent variables, by comparing the estimated values of the UC-0 and UC-UR model with the BN decomposition model. Domestic housing prices have plunged two times: the 1997 Asian financial crisis and the 2008 Global financial crisis. However, the analysis data in this research include only the 2008 Global financial crisis.BRBR The results are summarized as follows: First, the volatility of the domestic housing price index can be explained by the UC-0 model. As explained by the UC-0 model with the assumption of the covariance between trend and cycles zero(0), the domestic housing price index has not shown any correlation between the trend and cycle, and thus it implies that the trends mainly explain the volatility in the housing price index. Second, as the trends mainly explain the volatility in housing prices, domestic housing prices do not follow a random walk under the efficient market hypothesis. Hence, the price volatility can be seen as predictable, and the right-upward price trend that has been lasted for a considerable period of time can be seen as a result of reflecting adaptive expectations of market participants. Lastly, institutional investors such as pension funds are increasing their share of real estate and alternative investments, and the share of real estate is increasing in the asset market. The fact that inefficient housing market, and the housing price return as seen through the housing price analysis follow a very differential distribution from the normal distribution would mean that as a factor hindering investors

A structural quantitative analysis of services trade de-liberalization

We propose a quantifiable multi-country-multi-sector model of trade in goods and services where markets are not fully penetrated by firms and trade is impeded by standard iceberg costs as well as market-entry costs. Using transaction-type information of German services traders, we inform the model about key behavioral parameters governing the activity of services producers. We calibrate market-entry costs by sector and market, and decompose these into observable and unobservable components. We then study the effects of de-liberalizing services trade. Overall costs on seller-to-customer-market transactions in services are reduced substantially by preferential market access for services through trade agreements. If all countries abandoned existing preferential market access to services, this would reduce their real consumption by up to 6.7% with a similar decline in real wages and dividends (depending on the country). For most economies, depending on their input-output structure, de-liberalizing preferential services-market access has adverse spillover effects on manufacturing.