Dynamic Factor Model Research Articles

Forecasting Korea’s GDP growth rate based on the dynamic factor model

Forecasting Korea’s GDP growth rate based on the dynamic factor model

Experimental Study on Dynamic Mechanical Performance of Post-Fire Concrete Confined by CFRP Sheets.

Impact tests on post-fire concrete confined by Carbon Fiber-Reinforced Polymer/Plastic (CFRP) sheets were carried out by using Split Hopkinson Pressure Bar (SHPB) experimental setup in this paper, with emphasis on the effect of exposed temperatures, CFRP layers and impact velocities. Firstly, according to the measured stress-strain curves, the effects of experiment parameters on concrete dynamic mechanical performance such as compressive strength, ultimate strain and energy absorption are discussed in details. Additionally, temperature caused a softening effect on the compressive strength of concrete specimens, while CFRP confinement and strain rate play a hardening effect, which can lead to the increase in dynamic compressive strength by 1.8 to 3.6 times compared to static conditions. However, their hardening mechanisms and action stages are extremely different. Finally, nine widely accepted Dynamic Increase Factor (DIF) models considering strain rate effect were summarized, and a simplified model evaluating dynamic compressive strength of post-fire concrete confined by CFRP sheets was proposed, which can provide evidence for engineering emergency repair after fire accidents.

Shaping the local business structure: drivers of firm location at the municipal level

ABSTRACT Because local economic development is linked to the ability to attract business, policymakers have begun to endow territories with suitable factor conditions for the enticement of new enterprises, as in the case of EU Structural Funds. We investigate this phenomenon to provide valuable policy suggestions with a dynamic factor model and a panel regression analysis with spatial effects on a database with many cross-sections (1531 municipalities) and variables (55 indicators). Results show that structural variables like technological endowment, infrastructural provision, productive structure, local taxation, human capital and innovation represent the most important drivers to stimulate business location, even at the municipal level, offering relevant cues for policymakers.

Interest rates across the world: Global, regional, and idiosyncratic factors

This paper employs a dynamic factor model with endogenous regional clustering and extreme value adjustment properties to construct a world interest rate, as well as regional factors, based on country-specific short-run real interest rates from 43 markets. We find that global and regional factors play crucial roles in determining local rates among advanced countries, while local factors are more important among emerging markets. Further, convenience yields essentially affect both global and regional rates, especially in the longer run. Moreover, the relationship between global and local rates depends crucially on capital account openness, while the choice of exchange rate regime is a critical determinant of the transmission of regional factors. Lastly, we show that a U.S. nominal rate shock would raise the global real rate a quarter later, with a stronger impact observed before the U.S. rate hits the zero lower bound in 2008.

Extreme weather shocks and state-level inflation of the United States

This study investigates the impact of a metric of extreme weather shocks on 32 state-level inflation rates of the United States (US) over the quarterly period of 1989:01 to 2017:04. In this regard, we first utilize a dynamic factor model with stochastic volatility (DFM-SV) to filter out the national factor from the local components of overall, non-tradable and tradable inflation rates, to ensure that the effect of regional climate risks is not underestimated, given the derived sizeable common component. Second, using impulse responses derived from linear and nonlinear local projections models, we find statistically significant increases in the state (and national) factor of overall inflation rates, with the aggregate effect being driven by the tradable sector relative to the non-tradable one, particularly across the agricultural states in comparison to the non (less)-agricultural ones. Our findings have important policy implications.

Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire

AbstactIn this paper we develop nowcasting models for the Pays‐de‐la‐Loire's jobseekers, a dynamic French regional economy. We ask whether these regional nowcasts are more accurate by only using the regional data or by combining the national and regional data. For this purpose, we use penalized regressions, random forest, and dynamic factor models as well as dimension reduction approaches. The best nowcasting performance is provided by the DFM estimated on the regional and regional‐national databases as well as the Elastic‐Net model with a prior screening step for which the national data are the most frequently selected data. For the latter, it appears that the Change in foreign orders in the industry sector, the OECD Composite leading indicator, and the BdF Business sentiment indicator are among the major predictors.

Spanish GDP short-term point and density forecasting using a mixed-frequency dynamic factor model

AbstractWe have assessed the effect of data releases when constructing short-term point and density forecasts of the Spanish gross domestic product growth. For this purpose, we considered a real-forecasting exercise in which we defined several pseudo-data vintages that had a mixture of monthly and quarterly frequencies and were unbalanced towards the end of the sample. We implemented a mixed-frequency dynamic factor model to deal with data features and to produce gross domestic product forecasts. We evaluated the predictive content of data releases from point and density forecast perspectives, the latter aspect of the analysis being previously unexplored in the literature producing Spanish gross domestic product short-term forecasts. We observed significant improvements in point forecasts as information is released throughout the quarter, confirming existing results. Additionally, our findings indicated substantial enhancements in the accuracy of density forecasts as new data releases materialized.

Financial connectedness in BRICS: Quantile effects and BRICS SUMMIT impacts

This study examines the financial connectedness among BRICS using a two-step framework. In the first step, financial indices for each BRICS country are estimated by dynamic factor models in which three market characteristics and six sub-financial markets are considered. The estimated financial indices provide more comprehensive pictures of the financial markets in BRICS countries. Then we use the quantile variance decomposition model to discover the financial connectedness in BRICS. The study finds significant heterogeneity in financial connectedness among BRICS countries across quantile levels, with the tail spillover effects being stronger than the mean spillover effects. Russia is a risk transmitter while India is a recipient of risk during the crisis. We also find that the BRICS SUMMIT significantly strengthens the financial connectedness in the BRICS. The study provides international investors with new portfolio strategies and a risk-hedging perspective.

Risk Measurement, Analysis, and Prediction Methods for Online Financial Services

The analysis and research on the risk of online financial business is divided into four main areas; the construction of metrics, analysis and modeling, risk prediction, and risk monitoring and control. It mainly uses high-frequency data to update the dynamic factor model, converts the results to low-frequency data to derive the systematic risk indicators; based on the Markov interval transformation model, the unpredictable and uncertain mechanisms are used as stochastic variables to identify the characteristics of the risk changes to cope with the Internet financial risks. The study concludes that the Internet financial risk has a certain trend and periodicity, and the presented market is relatively stable; due to the close connection between Internet finance and traditional financial industry, the resonance effect can bring certain risk to the whole financial market, and it is still necessary to pay attention to the changing risk factors and take corresponding risk management measures.

Individualized dynamic latent factor model for multi-resolutional data with application to mobile health

Summary Mobile health has emerged as a major success for tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great challenges in handling heterogeneous, multi-resolution data that arise ubiquitously in mobile health due to irregular multivariate measurements collected from individuals. In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled measurements of time series with low resolution. One major advantage of the proposed method is the capability to integrate multiple irregular time series and multiple subjects by mapping the multi-resolution data to the latent space. In addition, the proposed individualized dynamic latent factor model is applicable to capturing heterogeneous longitudinal information through individualized dynamic latent factors. Our theory provides a bound on the integrated interpolation error and the convergence rate for B-spline approximation methods. Both the simulation studies and the application to smartwatch data demonstrate the superior performance of the proposed method compared to existing methods.

Constructing a high‐frequency World Economic Gauge using a mixed‐frequency dynamic factor model

AbstractThis paper uses information at the daily, monthly, and quarterly frequencies to construct a daily World Economic Gauge (WEG). We postulate a mixed‐frequency dynamic factor model to extract data observable at different frequencies in order to track the health of the global economy. We show that the WEG offers a reliable basis for tracking economic activity during key events such as COVID‐19 and the Global Financial Crisis. Moreover, the WEG is shown to contain leading information about the output growth of the OECD, G7, NAFTA, European Union, and euro areas, in addition to the output growth of 42 individual countries.

Dynamic behaviors of nickel coated carbon nanotubes reinforced ultra-high performance cementitious composites under high strain rate impact loading

Understanding the dynamic mechanical behaviors of materials is a prerequisite for reliably analyzing and predicting the dynamic response of structures, which is of great significance for transportation infrastructure and military engineering subjected to extreme loads, particularly dynamic loads at high strain rates. Nickel coated multi-walled carbon nanotubes (Ni-MWCNTs) show promise as a reinforcement to modify the dynamic mechanical behaviors of ultra-high performance cementitious composites (UHPCC). Because Ni-MWCNTs combine the high tensile strength and large aspect ratio characteristics of fibers with the nano effect of nanomaterials, as well as have excellent dispersibility due to the nickel plating on the CNTs’ surface, they effectively improve the dynamic mechanical properties of UHPCC under impact loading at high strain rates of about 100 s−1/300 s−1/500 s−1 and reduce the brittle damage degree of composites. The maximal increase in dynamic compressive strength and energy absorption capacity of UHPCC reaches 47.9% and 68.2%, respectively. This enhancement effect arises from that Ni-MWCNTs improve the microstructure and form multiple fine cracks, resulting in an increased energy consumption required for crack formation, propagation and coalescence. Additionally, the dynamic mechanical properties of Ni-MWCNTs reinforced UHPCC exhibit noticeable strain rate effects. Consequently, a strain rate-dependent logarithmic functional dynamic increase factor model and dynamic constitutive model of UHPCC filled with Ni-MWCNTs are modified herein for predicting and modeling the dynamic mechanical behaviors of composites.

Tracking Weekly State-Level Economic Conditions

AbstractThis paper develops a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We find considerable cross-state heterogeneity in the length, depth, and timing of business cycles. We illustrate the usefulness of these state-level indices for quantifying the main contributors to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of the Paycheck Protection Program. We also propose an aggregate indicator that gauges the overall weakness of the U.S. economy.

Nowcasting the Russian economy macroeconomic indicators under uncertainty: Does taking into account the news sentiment help?

In this paper the following models are compared: restricted and unrestricted MIDAS-models (mixed data sampling models), MFBVAR-model (mixed frequency Bayesian vector autoregression), Linear model with regularization (MIDAS_L1-, MIDAS_L2and MIDAS_PC-model) and dynamic factor model. The results are compared with classical autoregression as a benchmark. Production indices for different industries and indicators characterizing Russian GDP and its components, energy prices and PMI of Russia and its main trading partners, as well as indicators derived from the analysis of sentiment of news articles published by a number of major media and blogs are used as explanatory variables. The paper also proposes a method of rapid assessment of the current state of the economy based on data for the first or first two months of the quarter in question only. The use of this approach in combination with news sentiment analysis allows to draw conclusions about current economic situation extremely rapidly. Models’ accuracy is assessed by cross-validation for periods before and after the Q2 2022, the significance of the effect of adding news variables is assessed using the Diebold—Mariano test. When testing during the crisis period (starting from the Q1 2022), the addition of news variables leads to an increase in accuracy for 45% of the models considered, and the average improvement (reduction in the average absolute error) was 1.39 points (the reduction in MAE for the science-based GDP growth rates of Russia is 0.64 p.p.). At the same time, in a calmer (pre-sanction) period, the advantage of news is less noticeable: an increase in accuracy was recorded in 30% of cases with an average decrease in error of 1.54 points (the decrease in MAE for Russia’s GDP growth rate is 0.26 p.p.), and the change accuracy of science data when adding variables reflecting the news background turns out to be statistically insignificant. Thus, the use of news sentiment is not a “silver bullet” in the task of nowcasting Russian GDP, but in times of crisis it can serve as a good and, importantly, very operative indicator of the state of the economy and can be used in conjunction with more traditional explanatory variables.

Commodity returns co-movement, uncertainty shocks, and the US dollar exchange rate

This study examines the time-varying effects of uncertainty shocks identified using the external instrument method on the broad-based movement of commodity returns since the early 1990s. We employ a vector autoregression augmented dynamic factor model with time-varying parameters and stochastic volatility to extract a common factor from 43 commodity returns. We find that uncertainty shocks reduce commodity returns across the board through this common factor and that their effects vary significantly over time, with a tendency to grow much stronger during recessions. Furthermore, uncertainty shocks often lead to dollar appreciation, so they can potentially account for the seemingly negative correlation between commodity returns and strength of the US dollar.

Impact of China on commodity exporters

AbstractWe compare the effect of a domestic shock in China and the US on the real economy and financial markets of various commodity‐exporting countries. To obtain a reliable indicator for China's macroeconomic conditions, we estimate a Bayesian dynamic factor model using block‐exclusion restrictions to identify a China factor and a US factor from monthly macroeconomic data for China and the US. We, then, assess the implications of a negative shock to both factors on the macroeconomy of a commodity‐exporting nation using Bayesian FAVARs based on recursive identification. A negative China shock leads to output loss and a fall in stock prices in these countries. China shock affects the output of only a subset of countries in our sample compared to the US shock, which affects all countries. China shock has a larger, quicker and more persistent impact on the stock markets of commodity‐exporting countries compared to the US shock. Countries with weaker institutional or business environments experience a larger negative real effect of the China shock, whereas countries with less stable financial systems demonstrate stronger financial effects. Using historical decomposition, we establish a growing role of the China factor over time, in particular for large emerging economies such as Brazil and Russia.

DIY google trends indicators in social sciences: A methodological note

Various branches of the social sciences often require high-frequency measures of collective behavior. Examining peoples' Google search patterns enables uncovering otherwise hard-to-measure social trends or constructing different leading indicators. Google Trends (GT) data presents a unique and rich dataset that enables a deeper look at the popularity of peoples’ search interests. This paper delves into the main empirical approaches of constructing GT indicators, conducts a thorough methodological review, and elucidates the nuances and potentials of GT data. Specifically, the paper furnishes a step-by-step methodological guide for constructing GT indicators: from data pre-treatment to composite estimation using an array of machine learning techniques (principal component analysis, XGBoost, and dynamic factor models). We scrutinize the prevalent construction strategies of GT indicators, compare the use of keyword-based vs. category-based estimations, single indicators vs. composite indicators, and evaluate the significance of various data transformations. Illustrating the utility of our methodological framework through a case study, we create GT indicators for US retail trade and examine their forecasting accuracy in comparison to a benchmark autoregressive model. Our findings reveal that XGBoost yields the optimal test sample fit among composite indicators, while category-specific indicators like Shopping and Retail Trade exhibit worse predictive accuracy. As an additional contribution, we provide a detailed and user-friendly R code for replicating our analysis.

International trade fluctuations: Global versus regional factors

AbstractThis paper examines the relative importance of global, regional, country and idiosyncratic factors as well as the determinants that underpin fluctuations in international trade flows across different regions of the world. Our analysis starts by using a Bayesian dynamic latent factor model (BDFM) to simultaneously estimate the four dynamic factors, followed by the application of Bayesian model averaging to identify the variables that explain the shares of variance. Our key findings are: (i) international factors are the most important in explaining fluctuations in international trade, suggesting that the interconnections between economies and policies/shocks at the regional and global level tend to be more important than country‐level factors and (ii) regional integration, particularly when the agreement goes beyond trade in goods, is positively related to the share of the regional factor and inversely related to the importance of the global factor. Furthermore, the regional factor is more important in the case of economically large trade blocks. Overall, our analysis illustrates the usefulness of applying a BDFM model to study the co‐movements of international trade series.

Comovement and Global Imbalances of Current Accounts

In contrast to existing studies that have paid limited attention to comovement in the current account balances of inter-related countries, we employ a dynamic factor model to evaluate the contributions of the common global factor, group factors (advanced or emerging countries), and country-specific factors for 12 advanced and eight emerging economies from 1970 to 2017. We find that current account balances are largely driven by the group factor for advanced economies; however, current account balances in most emerging economies are primarily driven by country-specific factors. We then investigate the roles of these factors using the traditional empirical model for current account determination. Global and group factors are essential for addressing current accounts’ cross-sectional dependence in panel data models. We find that the empirical results introducing these factors are stronger, and the coefficients of relevant variables become more significant. Furthermore, we examine how these factors are related to the structural determinants of current account balances, revealing that more capital mobility and trade openness tend to increase the share of the group factor, while increases in country-specific determinants lead to a greater share in the country factor.

International comovements of public debt

AbstractThis study presents evidence of global and regional comovements in public debt and examines their trends and features. We employ a Bayesian dynamic factor model with time‐varying parameters based on the debt‐to‐GDP ratio of 115 countries. We find the global factor accounts for 38 percent of the total variation in the debt ratio worldwide, but its share decreases over time. Countries with more financial openness and a greater share of external public debt are more exposed to global/regional comovements. The assertion that international financial integration encourages higher government borrowing applies primarily to high‐income countries and it does not hold globally.