scholarly journals Economic Cycle and Bank Liquidity Hoarding: Are They Procyclical or Countercyclical?

2021 ◽  
Vol 58 (2) ◽  
pp. 217-237
Author(s):  
Van Dan Dang
Keyword(s):  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Economic Cycle ◽  
Balance Sheets ◽  
Panel Models ◽  
Economic Downturns ◽  
Precautionary Motive ◽  
Method Of Moments Estimator ◽  
Fixed Effects Regression ◽  
Dynamic Panel Models

The paper empirically examines bank liquidity hoarding fluctuations over the economic cycle and provides further evidence on the heterogeneous cyclicality of bank liquidity hoarding across different banks in Vietnam for the period 2007–2019. Using both static panel models with the fixed-effects regression using corrected Driscoll-Kraay standard errors and dynamic panel models with the two-step system generalized method of moments estimator, we find that the liquidity hoarding of banks is procyclical. Concretely bank liquidity hoarding on- and off-balance sheets tends to increase during economic upturns and decrease during economic downturns. Our additional analysis yields a consistent pattern that financially weaker banks are more procyclical than their stronger counterparts. During booms and busts, the behaviour of hoarding liquidity is more pronounced for banks with smaller sizes, less capital, more risk, and less profit. This heterogeneity also contributes to understanding the core mechanism behind our main findings, further confirming the precautionary motive of bank liquidity hoarding.

scholarly journals Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects

2017 ◽  
Vol 3 ◽  
pp. 237802311771057 ◽  
Author(s):  
Paul D. Allison ◽  
Richard Williams ◽  
Enrique Moral-Benito
Keyword(s):  
Maximum Likelihood ◽  
Fixed Effects ◽  
Structural Equation ◽  
Generalized Method Of Moments ◽  
Equation Modeling ◽  
Ml Estimation ◽  
Panel Models ◽  
Software Packages ◽  
Wide Range ◽  
Dynamic Panel Models

Panel data make it possible both to control for unobserved confounders and allow for lagged, reciprocal causation. Trying to do both at the same time, however, leads to serious estimation difficulties. In the econometric literature, these problems have been solved by using lagged instrumental variables together with the generalized method of moments (GMM). Here we show that the same problems can be solved by maximum likelihood (ML) estimation implemented with standard software packages for structural equation modeling (SEM). Monte Carlo simulations show that the ML-SEM method is less biased and more efficient than the GMM method under a wide range of conditions. ML-SEM also makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models.

Using information from singletons in fixed-effects estimation: xtfesing

2020 ◽  
Vol 20 (4) ◽  
pp. 965-975
Author(s):  
Laura Magazzini ◽  
Randolph Luca Bruno ◽  
Marco Stampini
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Panel Data Regression ◽  
Data Regression ◽  
Method Of Moments Estimator ◽  
Fixed Effects Estimation

In this article, we describe the xtfesing command. The command implements a generalized method of moments estimator that allows exploiting singleton information in fixed-effects panel-data regression as in Bruno, Magazzini, and Stampini (2020, Economics Letters 186: Article 108519).

scholarly journals A Crook is a Crook . . . But is He Still a Crook Abroad? On the Effect of Immigration on Destination-Country Corruption

2015 ◽  
Vol 16 (4) ◽  
pp. 464-489 ◽  
Author(s):  
Eugen Dimant ◽  
Margarete Redlin ◽  
Tim Krieger
Keyword(s):  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Destination Country ◽  
Estimation Methods ◽  
Econometric Methodology ◽  
Migration Flows ◽  
The Difference ◽  
Method Of Moments Estimator ◽  
The Impact

AbstractThis paper analyzes the impact of migration on destination-country corruption levels. Capitalizing on a comprehensive dataset consisting of annual immigration stocks of OECD countries from 207 countries of origin for the period 1984-2008, we explore different channels through which corruption might migrate. We employ different estimation methods using fixed effects and Tobit regressions in order to validate our findings. Moreover, we also address the issue of endogeneity by using the Difference- Generalized Method of Moments estimator. Independent of the econometric methodology, we consistently find that while general migration has an insignificant effect on the destination country’s corruption level, immigration from corruption-ridden origin countries boosts corruption in the destination country. Our findings provide a more profound understanding of the socioeconomic implications associated with migration flows.

scholarly journals Does job promotion affect men’s and women’s health differently? Dynamic panel models with fixed effects

2016 ◽  
pp. dyw310 ◽  
Author(s):  
Anna Nyberg ◽  
Paraskevi Peristera ◽  
Hugo Westerlund ◽  
Gunn Johansson ◽  
Linda L Magnusson Hanson
Keyword(s):  
Women’S Health ◽  
Women's Health ◽  
Fixed Effects ◽  
Dynamic Panel ◽  
Panel Models ◽  
Job Promotion ◽  
Dynamic Panel Models

scholarly journals FDML versus GMM for Dynamic Panel Models with Roots Near Unity

2021 ◽  
Vol 14 (9) ◽  
pp. 405
Author(s):  
Adrian Mehic
Keyword(s):  
Maximum Likelihood ◽  
Method Of Moments ◽  
Financial Management ◽  
Generalized Method Of Moments ◽  
Dynamic Panel ◽  
Panel Models ◽  
Higher Power ◽  
Size Distortions ◽  
High Degree ◽  
Dynamic Panel Models

This paper evaluates the first-differenced maximum likelihood (FDML) and the continuously updating system generalized method of moments (CU-GMM) estimators of dynamic panel models when the data is close to non-stationary. This case is far from trivial, as a high degree of persistence is the norm rather than the exception in economic panels, particularly in financial management. While the CU-GMM is shown to have lower bias and higher power, it suffers from severe size distortions, which are exacerbated when the data approaches non-stationarity.

scholarly journals Bidirectional Associations Between Depressive and Anxiety Symptoms and Loneliness During the COVID-19 Pandemic: Dynamic Panel Models With Fixed Effects

2021 ◽  
Vol 12 ◽  
Author(s):  
Cillian P. McDowell ◽  
Jacob D. Meyer ◽  
Daniel W. Russell ◽  
Cassandra Sue Brower ◽  
Jeni Lansing ◽  
...  
Keyword(s):  
Mental Health ◽  
Depressive Symptoms ◽  
Fixed Effects ◽  
Online Survey ◽  
Retention Rate ◽  
Anxiety Symptoms ◽  
Dynamic Panel ◽  
Panel Models ◽  
Bidirectional Associations ◽  
Dynamic Panel Models

Background: Understanding the direction and magnitude of mental health-loneliness associations across time is important to understand how best to prevent and treat mental health and loneliness. This study used weekly data collected over 8 weeks throughout the COVID-19 pandemic to expand previous findings and using dynamic panel models with fixed effects which account for all time-invariant confounding and reverse causation.Methods: Prospective data on a convenience and snowball sample from all 50 US states and the District of Colombia (n = 2,361 with ≥2 responses, 63.8% female; 76% retention rate) were collected weekly via online survey at nine consecutive timepoints (April 3–June 3, 2020). Anxiety and depressive symptoms and loneliness were assessed at each timepoint and participants reported the COVID-19 containment strategies they were following. Dynamic panel models with fixed effects examined bidirectional associations between anxiety and depressive symptoms and loneliness, and associations of COVID-19 containment strategies with these outcomes.Results: Depressive symptoms were associated with small increases in both anxiety symptoms (β = 0.065, 95% CI = 0.022–0.109; p = 0.004) and loneliness (β = 0.019, 0.008–0.030; p = 0.001) at the subsequent timepoint. Anxiety symptoms were associated with a small subsequent increase in loneliness (β = 0.014, 0.003–0.025; p = 0.015) but not depressive symptoms (β = 0.025, −0.020–0.070; p = 0.281). Loneliness was strongly associated with subsequent increases in both depressive (β = 0.309, 0.159–0.459; p < 0.001) and anxiety (β = 0.301, 0.165–0.436; p < 0.001) symptoms. Compared to social distancing, adhering to stay-at-home orders or quarantining were not associated with anxiety and depressive symptoms or loneliness (both p ≥ 0.095).Conclusions: High loneliness may be a key risk factor for the development of future anxiety or depressive symptoms, underscoring the need to combat or prevent loneliness both throughout and beyond the COVID-19 pandemic. COVID-19 containment strategies were not associated with mental health, indicating that other factors may explain previous reports of mental health deterioration throughout the pandemic.

scholarly journals Capital adjustment cost and inconsistency in income-based dynamic panel models with fixed effects

2019 ◽  
Vol 20 (4) ◽  
pp. e1002-e1018 ◽  
Author(s):  
Parantap Basu ◽  
Yoseph Getachew ◽  
Keshab Bhattarai
Keyword(s):  
Fixed Effects ◽  
Cost Of Capital ◽  
Moving Average ◽  
Adjustment Cost ◽  
Dynamic Panel ◽  
Panel Models ◽  
Capital Adjustment ◽  
Long Run ◽  
Fixed Effects Estimator ◽  
Dynamic Panel Models

Abstract After the seminal work of Nickell (1981), a vast literature demonstrates the inconsistency of ‘conditional convergence’ estimator in income-based dynamic panel models with fixed effects when the time horizon (T) is short but the sample of countries (N) is large. Less attention is given to the economic root of inconsistency of the fixed effects estimator when T is also large. Using a variant of the Ramsey growth model with long-run adjustment cost of capital, we demonstrate that the fixed effects estimator of such models could be inconsistent when T is large. This inconsistency arises because of the long-run adjustment cost of capital which gives rise to a negative moving average coefficient in the error term. Income convergence will be thus overestimated. We theoretically characterize the order of this inconsistency. Our Monte Carlo simulation demonstrates that the size of the bias is substantial and it is greater in economies with higher capital adjustment costs. We show that the use of instrumental variables that take into account the presence of the negative moving average term in the error will overcome this bias.

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