Dynamic Probit Model Research Articles

Effectiveness of military spending in reducing the intensity of armed conflict in Sub-Saharan Africa

Abstract This study empirically examines the effectiveness of military spending in reducing the intensity of armed conflict in Sub-Saharan Africa. We use a dynamic probit model, and the results show that military spending reduces the intensity of conflict. It significantly reduces low-intensity conflicts more than high-intensity conflicts. Additionally, the effectiveness of military spending depends on its level and the quality of institutions. Finally, the analysis of transmission channels shows that military spending affects conflict intensity mainly through the channel of military capabilities. We conducted several tests and robustness checks to ensure the validity of our results.

State Dependence in Material and Social Deprivation in European Single-Parent Households

This paper explores the dynamics of social exclusion as measured by material and social deprivation in the particularly exposed category of single-parent households. We aim to assess whether there is true state dependence in deprivation and the role of other household factors, as well as that of the macro-economic and social welfare scenario. We use 2015–2018 European Union Statistics on Income and Living Conditions longitudinal data to explore a large set of European countries. We estimate three-level dynamic probit models that enable us to account for micro- and country-level unobserved heterogeneity. Our results suggest that material and social deprivation is likely to be a trap for single-parent households and that this effect is stronger for these households than for those composed of two adults and children. Among single-parent households, those headed by a female are worse off than those headed by a male. The policy implications of these findings are also discussed.

Food Insecurity Transition Among Agricultural Households In Nigeria

ABSTRACT: Hunger continues to be on the increase and food crises remain a developmental challenge. The incidence of food insecurity in the country has heightened in recent years due to unprecedented natural hazards, economic shocks and climate change, which have eroded livelihoods and worsened the country’s performance in food security-related indices. We therefore used the panel data from Nigeria to examine the food insecurity transition among agricultural households. The data covered four rounds. The food insecurity status of agricultural households was estimated using the Foster-Greer-Thorbecke measure. We explained the interaction among the states of food security of agricultural households across different periods using the food insecurity transition matrix. The determinants of food insecurity were explored using the dynamic probit model. The model addressed unobserved heterogeneity, state dependency, and serial correlation arising from the data and model. Findings revealed that the mean food expenditure increased across the period. Similarly, mean per capita food expenditure also increased across periods except for the 2012/2013 period where it dropped slightly. We observed a rising incidence of food insecurity across the period from 44.1% in 2010/2011 to 53.4% in 2018/2019 representing a 21.1% increase in food insecurity incidence. Widening inequality exists even among the food insecure. Our findings revealed that 87.6% of households are transitorily food insecure, 5.5% are chronically food insecure and 7% are food secured. The significant factors driving food insecurity across the period were the area of land under cultivation, the fall in the price of outputs, and the increase in the price of major food items. Coping strategies employed were eating less preferred foods, restriction in diversity of foods eaten, limiting food portions, and minimizing the frequency of meal consumption. It is therefore important that the government encourage the adoption of yield-enhancing technologies to ensure household food security. Rising prices of major food items and fall in output price gives a cause for concern over the macroeconomic environment in the country, which fuels rising food prices. Policymakers should exercise caution when implementing exchange rate policies that may affect food and output prices, given the impact they have on the livelihood and household security of smallholders.

Examining entry and exit rates of poverty in Turkey: A dynamic probit regression analysis

Purpose: The purpose of this study is to add to the current poverty dynamics literature by investigating the underlying causes of poverty persistence in Turkey, with an emphasis on both entry and exit rates.
 Methods: The study analyzes data from the Turkish Statistical Institute's "Survey on Income and Living Conditions" from 2018 to 2021 using dynamic probit models. This large dataset, which gives a detailed picture of socioeconomic situations, helps in properly understanding the complex aspects influencing poverty rates.
 Findings: The analysis reveals significant poverty persistence in Turkey, influenced by factors such as gender, marital status, employment, and health conditions. According to the research, these variables frequently interact, forming a complex structure that maintains poverty throughout the country.
 Implications: The findings necessitate targeted interventions to address persistent poverty, considering the diverse influencing factors. This could lead to a reduction in poverty rates and improved socioeconomic conditions for individuals.
 Originality/Value: This study offers a unique perspective on poverty dynamics in Turkey, focusing on both entry and exit rates. It provides valuable insights for those formulating policies or strategies aimed at poverty reduction, emphasizing the need for a comprehensive approach to poverty alleviation.

Predicting recessions using VIX–yield curve cycles

The VIX index and the spread between long- and short-term Treasury bond yields co-move in counterclockwise cycles that align with the business cycle. Based on this empirical fact, I predict U.S. recessions using an indicator of the economy’s location on the VIX–yield curve cycle. The proposed indicator significantly outperforms the yield curve spread in predicting U.S. recessions from 1950–2022 both in- and out-of-sample and using both static and dynamic probit models. VIX–yield curve cycles also contain predictive power above and beyond other leading economic indicators.

Infant Death Clustering in the Quarter of a Century in India: A Decomposition Analysis.

The study aims to examine the clustering of infant deaths in India and the relative contribution of infant death clustering after accounting for the socio-economic and biodemographic factors that explain the decline in infant deaths. The study utilized 10 years of birth history data from three rounds of the National Family Health Survey (NFHS). The random effects dynamic probit model was used to decompose the decline in infant deaths into the contributions by the socio-economic and demographic factors, including the lagged independent variable, the previous infant death measuring the clustering of infant deaths in families. The study found that there has been a decline in the clustering of infant deaths among families during the past two and half decades. The simulation result shows that if the clustering of infant deaths in families in India was completely removed, there would be a decline of nearly 30 percent in the infant mortality rate (IMR). A decomposition analysis based on the dynamic probit model shows that for NFHS-1 and NFHS-3, in the total change of the probability of infant deaths, the rate of change for a given population composition contributed around 45 percent, and about 44 percent was explained by a compositional shift. Between NFHS-3 and NFHS-4, the rate of change for a given population composition contributed 86%, and the population composition for a given rate contributed 10% to the total change in the probability of infant deaths. Within this rate, the contribution of a previous infant was 0.8% and the mother's age was 10%; nearly 31% was contributed by the region of residence, 69% by the mother's education, and around 20% was contributed by the wealth index and around 8.7% by the sex of the child. The mother's unobserved factors contributed more than 50 percent to the variability of infant deaths in all the survey rounds and was also statistically significant (p < 0.01). Bivariate analysis suggests that women with two or more infant losses were much less likely to have full immunization (10%) than women with no infant loss (62%), although institutional delivery was high among both groups of women.

From systematic to systemic risk among G7 members: Do the stock or real estate markets matter?

Stock and housing market systematic risk may be a critical ingredient in the precipitation of systemic risk and hence a financial crisis. However, surprisingly few studies have so far explored the role of their contagion in triggering systemic risk. In this paper, time-varying connectedness is used to measure two-market systematic risks and time-varying Granger-causality is employed to monitor their causal interactions over time. Using quarterly G7 data over a long period covering 1970–2021, our results indicate that these systematic risks both are very volatile. Moreover, the dominant source of causality in the housing systematic risk implies that policies directed against the possibility of a housing bubble, for example, macroprudential toolkits, deserve special emphasis. Finally, given that the duration of the bilateral causality coincides with those in past crises, a dynamic probit model reveals that adopting an expansionary monetary policy with a huge reduction in interest rates may greatly increase the likelihood of a financial crisis.

The dynamics of poverty in Europe: what has changed after the great recession?

This paper provides novel evidence on the importance of the phenomenon of poverty and its heterogeneity across European countries. We analyze the determinants of poverty in Europe and their evolution over time by disentangling the role of genuine state dependence and heterogeneity.We apply alternative dynamic probit models accounting for endogenous initial conditions and correlated random effects to the pre-Great Recession period of 2005–2008 and the post- Great Recession period of 2015–2018 using EU-SILC longitudinal datasets for a sample of European countries in order to estimate genuine state dependence and uncover the role of observable and unobservable factors in determining the risk of poverty. Our findings suggest that the degree of genuine state dependence is relevant in Europe and that it increased slightly from pre- to post-Great Recession. This suggests that measures aimed at lifting individuals out of poverty, including cash transfers, have become even more important during the Europe 2020 decade. Our analysis also reveals that Europe is characterized by an increasing scarring effect of poverty, the trend of which has been exacerbated in the post-recession period. The analysis at the country level clarifies why the evolution of genuine state dependence was heterogeneous. While a clear pattern within macro-regions does not emerge, we find an association between country-level variation in genuine state dependence and some macroeconomic indicators. Finally, our results suggest that the protective role of higher education has diminished over time, while the role of employment stability and of childcare provision during early childhood has become even more important in the post-recession period.

Public pensions and low-income dynamics in Canada

AbstractThis paper focuses on individuals over 50 and shows that considering persistence and low-income dynamics is essential for understanding poverty. We use administrative data for Canada from the Longitudinal and International Study of Adults. The paper shows that poverty for seniors is highly persistent and strongly depends on lifetime earnings. We show that beginning to receive a public pension implies a higher probability of exit from poverty. Public pensions thereby help to explain the lower overall incidence of poverty among the elderly. These results are confirmed in a dynamic probit model, which allows controlling for individuals' unobserved heterogeneity and state dependence. While public pensions do not eliminate poverty among older adults, they help to alleviate it by reducing persistence and increasing exit for those who are most at risk.

Persistence and learning effects in design innovation: Evidence from panel data

This paper explores persistence and learning effects in the aesthetic and symbolic dimensions of design innovation. By combining insights from innovation economics and design studies, we discuss design innovation as the result of firm-specific cumulative learning. We then conceptualise design and product innovation as complementary processes whose interplay may lead to learning effects across different dimensions of knowledge creation. We provide quantitative evidence for these insights applying dynamic probit and bivariate probit models to a longitudinal dataset of manufacturing firms based in Spain for the period 2007–2016. Our findings confirm the presence of persistence effects in design innovation, offering novel evidence in support of the view whereby design is an iterative process shaped by the knowledge generated through firms’ previous engagement with design. In addition, the results contribute to our understanding of the role of design beyond its functional dimension, pointing to mutually reinforcing effects between aesthetic and symbolic design and product innovation.

Energy poverty and its relationship with health: empirical evidence on the dynamics of energy poverty and poor health in Australia

Does poor health increase the likelihood of energy poverty or vice versa creating a vicious poverty trap? We use data from the Household, Income and Labour Dynamics of Australia (HILDA) survey from 2005–2018 to explore if these two processes are dynamically related across a number of subjective and objective measures of physical and mental health as well as subjective and objective measures of energy poverty. We employ univariate dynamic models, introduce controls for initial conditions, and explore inter-dependence between energy poverty and health using a dynamic bivariate probit model. Our results show that controlling for initial conditions impacts on the magnitude and significance of the lagged coefficients. We only find cross-dependency effects between energy poverty and health for subjective measures of energy poverty. This suggests that individuals’ feelings about being in energy poverty may impact on their health leading to poor health/energy poverty traps. Targeting individuals in financial stress/debt may be one way to reduce these poor health/energy poverty traps.

Predicting French SME failures: new evidence from machine learning techniques

ABSTRACT The aim of this study is to provide new insights into French small and medium-sized enterprises (SME) failure prediction using a unique database of French SMEs over the 2012–2018 period including both financial and nonfinancial variables. We also include text variables related to the type of activity. We compare the predictive performance of three estimation methods: a dynamic Probit model, logistic Lasso regression, and XGBoost algorithm. The results show that the XGBoost algorithm has the highest performance in predicting business failure from a broad dataset. We use SHAP values to interpret the results and identify the main factors of failure. Our analysis shows that both financial and nonfinancial variables are failure factors. Our results confirm the role of financial variables in predicting business failure, while self-employment is the factor that most strongly increases the probability of failure. The size of the SME is also a business failure factor. Our results show that a number of nonfinancial variables, such as localization and economic conditions, are drivers of SME failure. The results also show that certain activities are associated with a prediction of lower failure probability while some activities are associated with a prediction of higher failure.

Farmers' choices of climate-resilient strategies: Evidence from Vietnam

Farmers have a long history of adapting to changing conditions, including changing climate, towards more sustainable agricultural production. In this study, we construct a unique long-duration pseudo-panel dataset from nationally representative households in Vietnam to investigate factors behind farmer's choices to adopt soil and water conservation techniques to adapt to climatic change. Since farmers' adoption decisions are inherently dynamic, a dynamic probit model was estimated. We find that weather shocks and long-run changes in temperature are significant determinants of farmers' choices. The decision to make new investments in adaptation practices in subsequent periods is confirmed to be strongly influenced by the past adoption decision. Farmer's experience, farm size, and access to weather and output price information are also associated with households that apply conservation measures. These findings suggest that policies aiming at promoting climate-resilient strategies should pay attention to farmers' adaptation behavior and the persistence of choices in farmers' decision-making processes. Policies should target improving farmers' access to information with a special focus on market- and weather-related information to enhance farmers' adaptive capacity to better cope with ongoing climatic uncertainty.

A closed-form filter for binary time series

Non-Gaussian state-space models arise in several applications, and within this framework the binary time series setting provides a relevant example. However, unlike for Gaussian state-space models — where filtering, predictive and smoothing distributions are available in closed form — binary state-space models require approximations or sequential Monte Carlo strategies for inference and prediction. This is due to the apparent absence of conjugacy between the Gaussian states and the likelihood induced by the observation equation for the binary data. In this article we prove that the filtering, predictive and smoothing distributions in dynamic probit models with Gaussian state variables are, in fact, available and belong to a class of unified skew-normals (sun) whose parameters can be updated recursively in time via analytical expressions. Also the key functionals of these distributions are, in principle, available, but their calculation requires the evaluation of multivariate Gaussian cumulative distribution functions. Leveraging sun properties, we address this issue via novel Monte Carlo methods based on independent samples from the smoothing distribution, that can easily be adapted to the filtering and predictive case, thus improving state-of-the-art approximate and sequential Monte Carlo inference in small-to-moderate dimensional studies. Novel sequential Monte Carlo procedures that exploit the sun properties are also developed to deal with online inference in high dimensions. Performance gains over competitors are outlined in a financial application.

Will Public Environmental Concerns Foster Green Innovation in China’s Automotive Industry? An Empirical Study Based on Multi-Sourced Data Streams

This study explores the impact of public concerns on green innovation in China’s automotive industry and examines whether the effect varies based on firm size, ownership, and time phase. The study investigates 151 automobile enterprises and provides a novel, large-scale, and data-based perspective and estimation method for exploring critical factors of green innovation. By applying transition probabilities matrix (TPM) model, this paper finds that for different-sizes automotive enterprises there are significant differences in innovation sustainability, non-innovation sustainability, and liquidity between innovation and non-innovation, and such differences also exist for state-owned and non-state-owned enterprises. Then, based on the dynamic panel random probit (DPRP) model, the paper further analyzes the possible reasons for these differences, and particularly focuses on exploring the impact of public environmental concern on the environmental technology innovation. The empirical results show: 1) public concerns encourages green innovation emerging in all automotive firms, but only affects innovation persistence in medium and large companies. 2) public concerns encourages non-innovator state-owned companies to become innovators and motivates them to maintain continuous innovation. 3) the impact of public concerns changes over time. In the periods of 2002–2007 and 2012–2013, the role of public concerns is not significant. However, in the 2007–2012 period, public concerns significantly stimulate enterprises to move from non-innovators to innovators and promotes continuous innovation.

Predicting Recessions Using VIX-Yield-Curve Cycles

The VIX index and the spread between long- and short-term Treasury bond yields co-move in counterclockwise cycles that align with the business cycle. Based on this empirical fact, I predict U.S. recessions using an indicator of the economy’s location on the VIX-yield-curve cycle. The proposed indicator significantly outperforms the yield-curve spread in predicting U.S. recessions from 1990–2021 both in- and out- of-sample using both static and dynamic probit models. VIX-yield-curve cycles also contain predictive power above and beyond other leading economic indicators.

Comments on Default Probability by Employment Status in South Korea

Comments on Default Probability by Employment Status in South Korea

Monetary regimes, the term structure and business cycles in Ireland, 1972–2018

AbstractThe literature emphasizes the role of monetary policy for the term structure's ability to predict business cycles. Between 1972 and 2018, Ireland experienced three monetary regimes: first, the Irish Pound was fixed to Sterling (1972–1979); second the Pound floated in a band when Ireland was a member of EMS (1979–1998); and third, as a member of the euro area (1999–2018). Using dynamic probit models and monthly data, I show that the term spread only had predictive power during the second regime, the only one in which the Central Bank of Ireland had discretion to set interest rates based on the domestic conditions.

What does forecaster disagreement tell us about the state of the economy?

ABSTRACT This article shows in a simple model that the part of uncertainty measured by forecaster disagreement rises in advance of and during recessions. Subsequently, it is tested using the Survey of Professional Forecasters in a dynamic probit model. It is shown that increases in disagreement help predict recessions in an out-of-sample context for the US.

The term structure, leading indicators, and recessions: evidence from Switzerland, 1974\u20132017

This paper studies the predictive power for recessions of the slope of the Swiss term structure using monthly data for 1974–2017. Dynamic probit models indicate that the term structure contains information useful for predicting recessions for horizons up to 19 months. Whether the economy is currently in recession or not is also useful for forecasting recessions. These relationships prove stable over the sample. Robustness tests indicate that the KOF business course indicator and some monetary aggregates contain different information from the term structure which can improve the in- and out-of-sample fit of the model.