A life course model of self-reported violence exposure and illhealth with a public health problem perspective

Niclas Olofsson; Niclas Olofsson

doi:10.3934/publichealth.2014.1.9

AIMS Public Health

2014, Volume 1, Issue 1: 9-24. doi: 10.3934/publichealth.2014.1.9

Previous Article Next Article

Review

A life course model of self-reported violence exposure and illhealth with a public health problem perspective

Niclas Olofsson ^,

Department of Research and Development, Härnösand, 871 85 Härnösand, Sweden

Received: 31 October 2013 Accepted: 22 January 2014 Published: 27 January 2014

Violence has probably always been part of the human experience. Its impact can be seen, in various forms, in all parts of the world. In 1996, WHO:s Forty-Ninth World Health Assembly adopted a resolution, declaring violence a major and growing public health problem around the world. Public health work centers around health promotion and disease prevention activities in the population and public health is an expression of the health status of the population taking into account both the level and the distribution of health. Exposure to violence can have many aspects, differing throughout the life course ― deprivation of autonomy, financial exploitation, psychological and physical neglect or abuse — but all types share common characteristics: the use of destructive force to control others by depriving them of safety, freedom, health and, in too many instances, life; the epidemic proportions of the problem, particularly among vulnerable groups; a devastating impact on individuals, families, neighborhoods, communities, and society. There is considerable evidence that stressful early life events influence a variety of physical and/or psychological health problems later in life. Childhood adversity has been linked to elevated rates of morbidity and mortality from number of chronic diseases. A model outlining potential biobehavioural pathways is put forward that may be a potential explanation of how exposure to violence among both men and women work as an important risk factor for ill health and should receive greater attention in public health work.

Keywords:

Citation: Niclas Olofsson. A life course model of self-reported violence exposure and illhealth with a public health problem perspective[J]. AIMS Public Health, 2014, 1(1): 9-24. doi: 10.3934/publichealth.2014.1.9

Related Papers:

[1]	Thomas C. Chiang . Economic policy uncertainty and stock returns—evidence from the Japanese market. Quantitative Finance and Economics, 2020, 4(3): 430-458. doi: 10.3934/QFE.2020020
[2]	Saeed Sazzad Jeris, Ridoy Deb Nath . Covid-19, oil price and UK economic policy uncertainty: evidence from the ARDL approach. Quantitative Finance and Economics, 2020, 4(3): 503-514. doi: 10.3934/QFE.2020023
[3]	Sudeshna Ghosh . Asymmetric impact of COVID-19 induced uncertainty on inbound Chinese tourists in Australia: insights from nonlinear ARDL model. Quantitative Finance and Economics, 2020, 4(2): 343-364. doi: 10.3934/QFE.2020016
[4]	Md Akther Uddin, Mohammad Enamul Hoque, Md Hakim Ali . International economic policy uncertainty and stock market returns of Bangladesh: evidence from linear and nonlinear model. Quantitative Finance and Economics, 2020, 4(2): 236-251. doi: 10.3934/QFE.2020011
[5]	Gülserim Özcan . The amplification of the New Keynesian models and robust optimal monetary policy. Quantitative Finance and Economics, 2020, 4(1): 36-65. doi: 10.3934/QFE.2020003
[6]	Diogo Matos, Luís Pacheco, Júlio Lobão . Availability heuristic and reversals following large stock price changes: evidence from the FTSE 100. Quantitative Finance and Economics, 2022, 6(1): 54-82. doi: 10.3934/QFE.2022003
[7]	Simiso Msomi, Damien Kunjal . Industry-specific effects of economic policy uncertainty on stock market volatility: A GARCH-MIDAS approach. Quantitative Finance and Economics, 2024, 8(3): 532-545. doi: 10.3934/QFE.2024020
[8]	Chiao Yi Chang, Fu Shuen Shie, Shu Ling Yang . The relationship between herding behavior and firm size before and after the elimination of short-sale price restrictions. Quantitative Finance and Economics, 2019, 3(3): 526-549. doi: 10.3934/QFE.2019.3.526
[9]	Serçin ŞAHİN, Serkan ÇİÇEK . Interest rate pass-through in Turkey during the period of unconventional interest rate corridor. Quantitative Finance and Economics, 2018, 2(4): 837-859. doi: 10.3934/QFE.2018.4.837
[10]	Albert A. Agyemang-Badu, Fernando Gallardo Olmedo, José María Mella Márquez . Conditional macroeconomic and stock market volatility under regime switching: Empirical evidence from Africa. Quantitative Finance and Economics, 2024, 8(2): 255-285. doi: 10.3934/QFE.2024010

Abstract

1. Introduction

Since introduction of the measure of policy uncertainty by Baker et al. (2016), the impact of policy uncertainty on macro variables have gained momentum. While Baker et al. (2016) demonstrated its adverse effects on economic activity, Wang et al. (2014) assessed the response of corporate investment to the new uncertainty measure. On the other hand, Pastor and Veronesi (2013), Ko and Lee (2015), and Brogaard and Detzel (2015) investigated response of risk premium and market returns to uncertainty. Other macro variables which are said to be affected by policy uncertainty include oil prices which was subject of an analysis by Kang and Ratti (2013) as well as Bahmani-Oskooee et al. (2018a). Additionally, while Bahmani-Oskooee et al. (2016) assessed the impact of policy uncertainty on the demand for money in the U.S., Bahmani-Oskooee and Ghodsi (2017) investigated the response of house prices in each state of the U.S.

The new policy uncertainty measure is now constructed on a monthly basis by the Policy Uncertainty Group for countries other than the U.S.¹ It is an index based on the volume of news associated with any uncertainty. Recently, Bahmani-Oskooee and Saha (2019) assessed the short- run and long-run effects of the policy uncertainty measure on stock returns in Canada, Japan, Korea, U.S., and U.K. These were the five countries for which a multivariate model could be estimated since monthly data were available for all the variables for these countries. Their main finding was that in all countries, increased uncertainty has adverse short-run effects but not long-run effects on stock prices. The main assumption included in Bahmani-Oskooee and Saha's (2019) study as well as other studies is that they have assumed that the effects of policy uncertainty on macro variables are symmetric. The symmetry assumption implies that if an x% increase in uncertainty hurts a variable by y%, an x% decrease in uncertainty will boost that variable by y%. However, this may not be the case since investors' reaction could be different to an increase in policy uncertainty as compared to a decline in uncertainty. If due to an x% increase in uncertainty investors shift their assets from stock to safer assets by y%, an x% decline in uncertainty may induce them to shift their portfolio into stock market by less than y%, if they expect decreased uncertainty to be short-lived, hence asymmetric effects. Similar arguments were recently made by Bahmani-Oskooee and Maki-Nayeri (2019) who assessed the asymmetric effects of policy uncertainty on domestic investment.

¹ For more details on constructing this index visit: www.policyuncertainty.com.

Therefore, the main purpose of this paper is to extend Bahmani-Oskooee and Saha's (2019) study from symmetric analysis to an asymmetric analysis of policy uncertainty on stock prices for the same five countries. To that end, we introduce the model and the methodology in Section Ⅱ that is followed by the results in Section Ⅲ. A summary is provided in Section Ⅳ and data definition and sources are cited in an Appendix.

2. The models and methodology

The model adopted here closely follows the model by Bahmani-Oskooee and Saha (2019), who followed the literature and used the following specification:²

² The same model is used by Boonyanam (2014) and Moore and Wang (2014) with no policy uncertainty measure. Other variants of the model without policy uncertainty measure is also used by Granger et al. (2000), Ansari and Kolari (2001), Nieh and Lee (2001), Phylaktis and Ravazzolo (2005), Yau and Nieh (2006), Pan et al. (2007), Richards et al. (2009), Liu and Tu (2011), Tsai (2012), Lin (2012), Tsagkanos et al. (2013), Groenewold et al. (2013), and Bahmani-Oskooee and Saha (2016).

$ L n S P_{t} = a+b L n E X_{t}+c L n I P I_{t}+d L n C P I_{t}+e L n M_{t}+f L n P U_{t}+\varepsilon_{t} $

(1)

where SP denotes the stock prices, EX is the nominal effective exchange rate, IPI is a measure of output proxied by Index of Industrial Production (IPI), CPI is the Consumer Price Index as a measure of the price level, M is a measure of nominal money supply, and finally PU is the measure of policy uncertainty.

Specification (1) is a long run model and coefficient estimates are long-run estimates. Once the long run estimates are obtained, an estimate of b is expected to be negative or positive depending upon if firms associated with the specific stock are export or import oriented. Since an increase in economic activity measured by IPI is expected to increase the demand for stocks, an estimate of c is expected to be positive. An estimate of d is also expected to be negative or positive depending on whether inflation hurts firm costs and profit margin or if stocks are considered a hedge against inflation in the long run.³ An estimate of e is also expected to be positive or negative, depending whether increase in money supply leads to lower interest rates and economic growth or inflation. Finally, if increase in uncertainty is to hurt the markets, we expect an estimate of f to be negative.

³ See Fama (1981) for the negative effects and Anari and Kolari (2001) for the positive effects.

As mentioned above, specification (1) is a long-run model and in order to assess the short-run effects of right-hand side variables, we must incorporate the short-run dynamics. Bahmani-Oskooee and Saha (2019) followed Pesaran et al.'s (2001) ARDL (Auto Regressive Distributed Lag) bounds testing approach and considered the following error-correction model:

$\begin{array}{l} \Delta LnS{P_t} = \alpha + \sum\limits_{i = 1}^{n1} {{\beta _i}} \Delta LnS{P_{t - i}} + \sum\limits_{i = 0}^{n2} {{\delta _i}} \Delta LnE{X_{t - i}} + \sum\limits_{k = 0}^{n3} {{\varphi _i}} \Delta LnIP{I_{t - i}} + \sum\limits_{i = 0}^{n4} {{\theta _i}} \Delta LnCP{I_{t - i}}\\ + \sum\limits_{i = 0}^{n5} {{\pi _i}} \Delta Ln{M_{t - i}} + \sum\limits_{i = 0}^{n6} {{\omega _i}} \Delta LnP{U_{t - i}} + {\lambda _1}\ln S{P_{t - 1}} + {\lambda _2}LnE{X_{t - 1}} + \\ + {\lambda _3}LnIP{I_{t - 1}} + {\lambda _4}LnCP{I_{t - 1}} + {\lambda _5}Ln{M_{t - 1}} + {\lambda _6}LnP{U_{t - 1}} + {\mu _t} \end{array}$ $

(2)

In (2), the short-run effects of each variable is judged by the coefficient estimates attached to the first-differenced variables and the long-run effects by the estimates of λ₂–λ₆ normalized on λ_1.⁴ However, for the long-run estimates to be meaningful, cointegration must be established. Pesaran et al. (2001) propose two tests: the F test to establish joint significance of lagged level variables in (2) and the t-test to establish significance of λ₁ which must be negative.⁵ They demonstrate that the distribution of both tests are non-standard, hence they tabulate new critical values which also account for degree of integration of all the variables in a given model. Their upper bound critical values should be used to establish cointegration. These values are valid, even if some variables are I(0) and some I(1) and this is one of the main advantage of this approach since almost all macro variables are either I(0) or I(1).⁶

⁴ Note that once normalization is done we will have $\frac{{{{\hat \lambda }_2}}}{{ - {{\hat \lambda }_1}}} = \hat b, \frac{{{{\hat \lambda }_3}}}{{ - {{\hat \lambda }_1}}} = \hat c, \frac{{{{\hat \lambda }_4}}}{{ - {{\hat \lambda }_1}}} = \hat d, \frac{{{{\hat \lambda }_5}}}{{ - {{\hat \lambda }_1}}} = \hat e, {\rm{ }}and\; \; {\rm{ }}\frac{{{{\hat \lambda }_6}}}{{ - {{\hat \lambda }_1}}} = \hat f.$

⁵ Note that the estimate of λ₁ in (2) is the same as estimate of coefficient attached to lagged error-correction term in Engle and Granger (1987) error-correction specification. For empirical demonstration of this, see Bahmani-Oskooee (2019) and Bahmani-Oskooee and Ghodsi (2019).

⁶ Another advantage of this approach is that since short-run adjustment process is included in estimating the long-run effects, any feedback effects among variables are allowed to exert themselves which help reduce multicolinearity or endogeniety (Pesaran et al. 2001).

Shin et al. (2014) modified Pesaran et al.'s (2001) approach so that it could be used to assess asymmetric effects of any variable of concern. Since our concern is to assess asymmetric effects of policy uncertainty measure, PU, we take the following steps. First, we form ΔLnPU which includes positive as well as negative changes. Second, we separate increases in uncertainty from declines and form two new time-series variable as follows:

$ PO{S_t} = \sum\limits_{j = 1}^t {\max } \left({\Delta LnP{U_j}, 0} \right), \quad NE{G_t} = \sum\limits_{j = 1}^t {\min } \left({\Delta LnP{U_j}, 0} \right) $

(3)

Where POS_t is the partial sum of the positive changes and reflects only increased uncertainty and NEG_t is the partial sum of negative changes in uncertainty and reflects only the decline in uncertainty. Next, we move back to the error-correction model (2) and replace the LnPU variable by the two partial sum variables to arrive at:

$\begin{array}{l} \Delta LnS{P_t} = \alpha + \sum\limits_{i = 1}^{n1} {{\beta _i}} \Delta LnS{P_{t - i}} + \sum\limits_{i = 0}^{n2} {{\delta _i}} \Delta LnE{X_{t - i}} + \sum\limits_{i = 0}^{n3} {{\varphi _i}} \Delta LnIP{I_{t - i}} + \sum\limits_{i = 0}^{n4} {{\theta _i}} \Delta LnCP{I_{t - i}}\\ + \sum\limits_{i = 0}^{n5} {{\pi _i}} \Delta Ln{M_{t - i}} + \sum\limits_{i = 0}^{n6} {\omega _i^ + } \Delta PO{S_{t - i}} + \sum\limits_{i = 0}^{n7} {\omega _i^ - } \Delta NE{G_{t - i}} + {\lambda _1}LnS{P_{t - 1}} + {\lambda _2}\ln E{X_{t - 1}} + \\ + {\lambda _3}LnIP{I_{t - 1}} + {\lambda _4}LnCP{I_{t - 1}} + {\lambda _5}Ln{M_{t - 1}} + \lambda _6^ + PO{S_{t - 1}} + \lambda _6^ - NE{G_{t - 1}} + {\mu _t} \end{array}$ $

(4)

Since the construction of the partial sum variables introduces nonlinear adjustment of policy uncertainty variable into our specification, Shin et al. (2014) refer models such as (4) as nonlinear ARDL model whereas, (2) is commonly referred to as the linear ARDL model. However, both models are subject to the same estimation, interpretation, and diagnostic tests.⁷ Once (4) is estimated, a few asymmetric assumptions could be tested. First, short-run effects of policy uncertainty will be asymmetric if at any given lag order, i, $\hat \omega _i^ + \ne \hat \omega _i^ - $. However, if the Wald-test rejects the null hypothesis of $\sum {\hat \omega _i^ + } = \sum {\hat \omega _i^ - } $, short-run cumulative or impact asymmetry will be established. Second, short-run adjustment process will be asymmetric if ΔPOS and ΔNEG take a different lag order, i.e., if n6 ≠ n7. Finally, long-run effects of policy uncertainty on stock prices will be asymmetric if we reject the null hypothesis of $\frac{{\hat \lambda _6^ + }}{{ - \hat \lambda _1^{}}} = \frac{{\hat \lambda _6^ - }}{{ - {{\hat \lambda }_1}}}$. Again, the Wald test will be used for this purpose.⁸

⁷ Shin et al. (2014) even argue that the critical value of the F test should stay at the same high level when we move from (2) to (4) even if (4) has one more variable.

⁸ For some other application of these and other nonlinear models see Apergis and Miller (2006), Wimanda (2014), McFarlane et al. (2014), Gogas and Pragidis (2015), Durmaz (2015), Baghestani and Kherfi (2015), Al-Shayeb and Hatemi-J (2016), Lima et al. (2016), Nusair (2017), Aftab et al. (2017), Arize et al. (2017), Gregoriou (2017), Bahmani-Oskooee and Kanitpong (2018), Olaniyi (2019), and Istiak and Alam (2019).

3. The results

Since the linear model (2) has already been estimated by Bahmani-Oskooee and Saha (2019) for Canada, Japan, Korea, U.K., and the U.S. using monthly data, we restrict ourselves to estimating only the nonlinear model (4) and compare our findings to theirs. We have updated the data and used monthly data over the period January 1985–October 2018. Following their approach we impose a maximum of eight lags on each first-differenced variable and use Akaike Information Criterion to select the optimum number of lags. In any case, if there was evidence of serial correlation, following Bahmani-Oskooee and Fariditavana (2019) we added additional lags of dependent variable to reduce autocorrelation. Using required critical values reported in the notes to each table, we identify significant statistics by * at the 10% level and ** at the 5% level. The results are reported in five tables, one for each country.

From the short-run estimates in Panel A of each table, we gather that either ΔPOS or ΔNEG variable carry at least one significant coefficient in every optimum model, implying that changes in policy uncertainty has short-run effects on stock prices in all the five countries. This was also the case in the linear model. However, the new results reveal that except in the case of Japan, short-run effects are asymmetric since at the same lag order, the estimate attached to the ΔPOS variable is different than the one attached to the ΔNEG variable. Thus, in the four remaining countries, short-run effects of changes in policy uncertainty are asymmetric. However, sum of the coefficients attached to the ΔPOS variable is different than the sum attached to the ΔNEG variable only in the results for the U.S., since the Wald test reported as Wald-S in Panel C of each table is significant only in Table 5 for the U.S. In sum, while the short-run effects of policy uncertainty are asymmetric in four countries, evidence of cumulative or impact asymmetric effects is discovered only in one country (the U.S.).

Table 1. Full information estimate of the nonlinear ARDL model for Canada.

Panel A: short-run coefficient estimates
Lag order	0	1	2	3	4	5	6	7	8
ΔLnSP
ΔLnEX	0.50
ΔLnEX	(3.65)**
ΔLnIPI	0.21	0.47
ΔLnIPI	(0.93)	(2.17)**
ΔLnCPI	0.76	0.03	-0.67	-0.31	-1.56
ΔLnCPI	(1.30)	(0.06)	(1.15)	(0.53)	(2.72)**
ΔLnM	0.001
	(0.93)
ΔPOS	-0.06
ΔPOS	(5.30)**
ΔNEG	-0.007	-0.02	0.03
ΔNEG	(0.56)	(1.39)	(2.18)**
Panel B: long-run coefficient estimates
Constant	LnEX	LnIPI	LnCPI	LnM	POS	NEG
12.94	0.41	0.09	-1.84	0.01	-0.18	-0.25
(0.83)	(1.34)	(0.12)	(1.86)*	(0.03)	(2.27)**	(3.08)**
Panel C: Diagnostics
F	t-test	LM	RESET	${{\bar R}^2}$	CUSUM (CUSUMQ)	Wald-L	Wald-S
2.57	-0.08	0.02	12.15**	0.19	S (S)	3.77*	1.33
	(4.25)**
Notes: a. Numbers inside the parentheses are absolute value of t-ratios. , * indicate significance at the 10% and 5% levels respectively. b. The upper bound critical value of the F-test for cointegration when there are five exogenous variables is 3.35 (3.79) at the 10% (5%) level of significance. These come from Pesaran et al. (2001, Table CI, Case Ⅲ, p. 300). c. The critical value for significance of ECM_t-1 is -4.04 (-4.38) at the 10% (5%) level when k = 6. These come from Pesaran et al. (2001, Table CII, Case Ⅲ, p. 303). d. LM is the Lagrange Multiplier statistic to test for autocorrelation. It is distributed as χ² with 1 degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level. e. RESET is Ramsey's test for misspecification. It is distributed as χ² with one degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level. f. Both Wald tests are also distributed as χ² with one degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level.

| Show Table

DownLoad: CSV

Table 2. Full information estimate of the nonlinear ARDL model for Japan.

Panel A: short-run coefficient estimates
Lag order	0	1	2	3	4	5	6	7	8
ΔLnSP
ΔLnEX	-0.01
ΔLnEX	(0.39)
ΔLnIPI	0.04	0.36	-0.32
ΔLnIPI	(0.22)	(2.17)**	(1.88)*
ΔLnCPI	-0.65
ΔLnCPI	(2.62)**
ΔLnM	-2.71
	(1.73)*
ΔPOS	-0.002
ΔPOS	(2.01)**
ΔNEG	-0.002
ΔNEG	(2.47)**
Panel B: long-run coefficient estimates
Constant	LnEX	LnIPI	LnCPI	LnM	POS	NEG
66.48	-0.25	-1.00	-12.34	0.17	-0.03	-0.03
(0.92)	(0.41)	(0.63)	(1.98)**	(0.06)	(1.94)*	(2.79)**
Panel C: Diagnostics
F	t-test	LM	RESET	${{\bar R}^2}$	CUSUM (CUSUMQ)	Wald-L	Wald-S
2.98	-0.05	1.08	0.00	0.07	S (S)	0.34	1.93
	(4.48)**
Notes: a. Numbers inside the parentheses are absolute value of t-ratios. , * indicate significance at the 10% and 5% levels respectively. b. The upper bound critical value of the F-test for cointegration when there are five exogenous variables is 3.35 (3.79) at the 10% (5%) level of significance. These come from Pesaran et al. (2001, Table CI, Case Ⅲ, p. 300). c. The critical value for significance of ECM_t-1 is -4.04 (-4.38) at the 10% (5%) level when k = 6. These come from Pesaran et al. (2001, Table CII, Case Ⅲ, p. 303). d. LM is the Lagrange Multiplier statistic to test for autocorrelation. It is distributed as χ² with 1 degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level. e. RESET is Ramsey's test for misspecification. It is distributed as χ² with one degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level. f. Both Wald tests are also distributed as χ² with one degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level.

| Show Table

DownLoad: CSV

Table 3. Full information estimate of the nonlinear ARDL model for Korea.

Panel A: short-run coefficient estimates
Lag order	0	1	2	3	4	5	6	7	8
ΔLnSP	-	0.05	-0.10	0.15
ΔLnSP	(0.71)	(1.47)	(2.30)**
ΔLnEX	-0.42	-0.20	-0.06	-0.08	0.40
ΔLnEX	(1.94)*	(0.85)	(0.27)	(0.36)	(2.48)**
ΔLnIPI	0.42	0.41
ΔLnIPI	(1.95)**	(1.96)**
ΔLnCPI	-4.12	0.31	-1.07	-2.85	-0.88	-4.58	0.74	-3.39
ΔLnCPI	(3.24)**	(0.24)	(0.82)	(2.24)**	(0.70)	(3.87)**	(0.62)	(2.98)**
ΔLnM	-0.07
	(0.38)
ΔPOS	-0.04
ΔPOS	(2.05)**
ΔNEG	-0.0003
ΔNEG	(0.13)
Panel B: long-run coefficient estimates
Constant	LnEX	LnIPI	LnCPI	LnM	POS	NEG
25.32	0.36	-1.27	1.23	-0.61	0.06	-0.002
(0.54)	(0.73)	(1.36)	(0.34)	(0.38)	(2.47)**	(0.13)
Panel C: Diagnostics
F	t-test	LM	RESET	${{\bar R}^2}$	CUSUM (CUSUMQ)	Wald-L	Wald-S
6.84**	-0.13	2.36	0.24	0.31	S (U)	1.92	1.02
	(7.00)**
Notes: a. Numbers inside the parentheses are absolute value of t-ratios. , * indicate significance at the 10% and 5% levels respectively. b. The upper bound critical value of the F-test for cointegration when there are five exogenous variables is 3.35 (3.79) at the 10% (5%) level of significance. These come from Pesaran et al. (2001, Table CI, Case Ⅲ, p. 300). c. The critical value for significance of ECM_t-1 is -4.04 (-4.38) at the 10% (5%) level when k = 6. These come from Pesaran et al. (2001, Table CII, Case Ⅲ, p. 303). d. LM is the Lagrange Multiplier statistic to test for autocorrelation. It is distributed as χ² with 1 degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level. e. RESET is Ramsey's test for misspecification. It is distributed as χ² with one degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level. f. Both Wald tests are also distributed as χ² with one degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level.

| Show Table

DownLoad: CSV

Table 4. Full information estimate of the nonlinear ARDL model for the U.K.

Panel A: short-run coefficient estimates
Lag order	0	1	2	3	4	5	6	7	8
ΔLnSP
ΔLnEX	-1.19
ΔLnEX	(2.58)**
ΔLnIPI	1.99	1.67
ΔLnIPI	(2.91)**	(2.57)**
ΔLnCPI	0.23
ΔLnCPI	(0.35)
ΔLnM	-0.69	-0.67	-1.29	-1.50
	(1.10)	(1.00)	(1.94)*	(2.32)**
ΔPOS	-0.04
ΔPOS	(3.70)**
ΔNEG	-0.01
ΔNEG	(4.36)**
Panel B: long-run coefficient estimates
Constant	LnEX	LnIPI	LnCPI	LnM	POS	NEG
-83.52	-2.55	4.87	1.52	2.60	-0.25	-0.07
(4.10)**	(1.57)	(2.61)**	(0.36)	(3.82)**	(3.17)**	(4.45)**
Panel C: Diagnostics
F	t-test	LM	RESET	${{\bar R}^2}$	CUSUM (CUSUMQ)	Wald-L	Wald-S
3.69*	-0.14	0.31	1.73	0.18	S (S)	3.28*	0.13
	(5.05)**
Notes: a. Numbers inside the parentheses are absolute value of t-ratios. , * indicate significance at the 10% and 5% levels respectively. b. The upper bound critical value of the F-test for cointegration when there are five exogenous variables is 3.35 (3.79) at the 10% (5%) level of significance. These come from Pesaran et al. (2001, Table CI, Case Ⅲ, p. 300). c. The critical value for significance of ECM_t-1 is -4.04 (-4.38) at the 10% (5%) level when k = 6. These come from Pesaran et al. (2001, Table CII, Case Ⅲ, p. 303). d. LM is the Lagrange Multiplier statistic to test for autocorrelation. It is distributed as χ² with 1 degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level. e. RESET is Ramsey's test for misspecification. It is distributed as χ² with one degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level. f. Both Wald tests are also distributed as χ² with one degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level.

| Show Table

DownLoad: CSV

Table 5. Full Information Estimate of the Nonlinear ARDL Model for U.S.A.

Panel A: short-run coefficient estimates
Lag order	0	1	2	3	4	5	6	7	8
ΔLnSP	-	0.05	0.03	0.12
ΔLnSP	(1.00)	(0.59)	(2.32)**
ΔLnEX	-0.33
ΔLnEX	(2.28)**
ΔLnIPI	-0.93	0.75	0.61
ΔLnIPI	(2.49)**	(2.06)**	(1.61)
ΔLnCPI	0.25
ΔLnCPI	(1.80)*
ΔLnM	-0.79	-0.98
	(1.14)	(1.40)
ΔPOS	-0.04	-0.03	0.005	0.03
ΔPOS	(3.77)**	(2.21)**	(0.39)	(2.45)**
ΔNEG	-0.01	0.004	0.03
ΔNEG	(1.35)	(0.39)	(2.27)**
Panel B: long-run coefficient estimates
Constant	LnEX	LnIPI	LnCPI	LnM	POS	NEG
-23.17	-0.34	2.00	3.29	0.36	-0.05	-0.01
(1.18)	(0.65)	(2.92)**	(1.86)*	(0.63)**	(2.28)**	(0.94)
Panel C: Diagnostics
F	t-test	LM	RESET	${{\bar R}^2}$	CUSUM (CUSUMQ)	Wald-L	Wald-S
2.98	-0.06	0.29	12.03**	0.16	S (S)	0.67	4.83**
	(4.51)**
Notes: a. Numbers inside the parentheses are absolute value of t-ratios. , * indicate significance at the 10% and 5% levels respectively. b. The upper bound critical value of the F-test for cointegration when there are five exogenous variables is 3.35 (3.79) at the 10% (5%) level of significance. These come from Pesaran et al. (2001, Table CI, Case Ⅲ, p. 300). c. The critical value for significance of ECM_t-1 is -4.04 (-4.38) at the 10% (5%) level when k = 6. These come from Pesaran et al. (2001, Table CII, Case Ⅲ, p. 303). d. LM is the Lagrange Multiplier statistic to test for autocorrelation. It is distributed as χ² with 1 degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level. e. RESET is Ramsey's test for misspecification. It is distributed as χ² with one degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level. f. Both Wald tests are also distributed as χ² with one degree of freedom. The critical value is 2.70 (3.84) at the 10% (5%) significance level.

| Show Table

DownLoad: CSV

Short-run effects of policy uncertainty last into the long-run significant effects in all countries. In all the countries, either the POS variable or the NEG variable carry a significant coefficient that is reported in Panel B of each table. These long-run effects are meaningful since cointegration is supported in all five countries either by the F or by the t-test, reported in Panel C. This was not the case in Bahmani-Oskooee and Saha (2019), who failed to find significant long-run effects of policy uncertainty on stock returns in any of the five countries, except Canada. These new significant long-run effects must be attributed to the nonlinear adjustment of policy uncertainty. Furthermore, the long-run effects are asymmetric in the results for Canada and the U.K., since the Wald test reported as Wald-L in Panel C is significant in these two cases, rejecting the equality of normalized coefficients attached to the POS and NEG variables.

In each table we have also reported additional diagnostic statistics. To test for serial correlation, the Lagrange Multiplier (LM) statistic is reported in Panel C and since it is insignificant in all models, there is no evidence of serial correlation. Ramsey's RESET test is also reported to check for misspecification. It is insignificant in three models and significant in two models. We have also applied the CUSUM and CUSUMSQ tests to the residuals of each optimum model to establish stability of estimated short-run and long-run coefficients. The stable coefficients are indicated by "S" and the unstable ones by "U". As can be seen, all estimates are stable.

4. Summary and conclusion

In a recent study by the same authors they asked, "are these adverse effects of different types of uncertainty on stock prices transitory or do they have long run implication?". By using the policy uncertainty measure and a linear ARDL model, they concluded that indeed the effects are short run only.

In this paper, when we separated the increases in uncertainty from the declines and estimated a nonlinear multivariate model for the same five countries, we found that, short-run effects of policy uncertainty lasts into the long run which we attribute to the nonlinear adjustment of policy uncertainty. More precisely, we found that policy uncertainty has short-run asymmetric effects in Canada, Korea, U.K., and the U.S. but not in Japan. Short-run effects translated into significant and meaningful long-run effects in all five countries. However, the results are country-specific. While in the cases of Canada, Japan, and the U.K., we found that increased policy uncertainty hurts stock prices, decreased uncertainty boosts them, though at different rates. In the U.S. however, we found that while increased uncertainty hurts stock returns, decreased uncertainty does not have any long-run effects, a clear sign of long-run asymmetric effects. All in all, it appears that reducing uncertainty could benefit stock returns in the short run as well as in the long run.

Acknowledgements

Valuable comments of two anonymous referees are greatly appreciated. Remaining errors, however, are ours.

Conflict of interest

We, hereby, declare that the submitted paper is not associated with any kind of conflict of interest.

References

[1]	Krug EG, Dahlberg LL, Mercy JA, et al. , editors (2002) World report on violence and health. Geneva: World Health Organization.
[2]	Max W, Rice DP, Finkelstein E, et al. (2004) The economic toll of intimate partner violence against women in the United States. Violence Vict 19: 259-272. doi: 10.1891/vivi.19.3.259.65767
[3]	Waters HR, Hyder AA, Rajkotia Y, et al. (2005) The costs of interpersonal violence—— an international review. Health Policy 73: 303-315. doi: 10.1016/j.healthpol.2004.11.022
[4]	Jones AS, Dienemann J, Schollenberger J, et al. (2006) Long-term costs of intimate partner violence in a sample of female HMO enrollees. Womens Health Issues 16:252-261. doi: 10.1016/j.whi.2006.06.007
[5]	Arias I, Corso P (2005) Average Cost per person vicimized by an Intimate Partner of the Opposite Gender: A Comparison of Men and Women. Violence and Victims 20:379-391. doi: 10.1891/vivi.2005.20.4.379
[6]	(WHO) WHO (2010) Injuries and violence: the facts. Geneva, Switzerland: WHO: WHO.
[7]	Mercy JA, Rosenberg ML, Powell KE, et al. (1993) Public health policy for preventing violence. Health Aff (Millwood) 12: 7-29.
[8]	J. LD (1986) A conception of adult development. American Psychologist Vol 41 3-13.
[9]	F HD, Francine D (1981) Adult develompment and aging: alife-span perspective. New York: McGraw-Hill Book Company.
[10]	Kuh D, Ben-Shlomo Y (2004) A life Course Approach to Cronic Disease Epidemiology; Kuh D, Ben-Shlomo Y, editors. Oxford: Oxford Medical Publications.
[11]	Kuh D, Ben-Shlomo Y, Lynch J, et al. (2003) Life course epidemiology. J Epidemiol Community Health 57: 778-783. doi: 10.1136/jech.57.10.778
[12]	Braveman P, Barclay C (2009) Health disparities beginning in childhood: a life-course perspective. Pediatrics 124 Suppl 3: S163-175.
[13]	Osler M (2006) The life course perspective: a challenge for public health research and prevention. Eur J Public Health 16: 230. doi: 10.1093/eurpub/ckl030
[14]	Greenfield EA (2010) Child abuse as a life-course social determinant of adult health. Maturitas 66: 51-55. doi: 10.1016/j.maturitas.2010.02.002
[15]	Cairney J, Krause N (2008) Negative life events and age-related decline in mastery: are older adults more vulnerable to the control-eroding effect of stress? J Gerontol B Psychol Sci Soc Sci 63: S162-170. doi: 10.1093/geronb/63.3.S162
[16]	Brady SS (2006) Lifetime community violence exposure and health risk behavior among young adults in college. J Adolesc Health 39: 610-613. doi: 10.1016/j.jadohealth.2006.03.007
[17]	Campbell J, Jones AS, Dienemann J, et al. (2002) Intimate partner violence and physical health consequences. Arch Intern Med 162: 1157-1163. doi: 10.1001/archinte.162.10.1157
[18]	Danielsson I, Olofsson N, Gadin KG (2005) [Consequences of violence——a public health issue. Strong connection between violence/threat and illness in both women and men]. Lakartidningen 102: 938-940,
[19]	Heise L, Ellsberg M, Gottmoeller M (2002) A global overview of gender-based violence. Int J Gynaecol Obstet 78 Suppl 1: S5-14.
[20]	Lundgren E, Heimer G, Westerstrand J, et al. (2002) Captured queen- men's violence against women in "equal" Sweden - a prevalence study. Stockholm: Fritzes Offentliga Publikationer.
[21]	Bair-Merritt MH, Crowne SS, Burrell L, et al. (2008) Impact of intimate partner violence on children's well-child care and medical home. Pediatrics 121: e473-480. doi: 10.1542/peds.2007-1671
[22]	Bair-Merritt MH, Feudtner C, Localio AR, et al. (2008) Health care use of children whose female caregivers have intimate partner violence histories. Arch Pediatr Adolesc Med 162: 134-139. doi: 10.1001/archpediatrics.2007.32
[23]	Carbone-Lopez K, Kruttschnitt C, Macmillan R (2006) Patterns of intimate partner violence and their associations with physical health, psychological distress, and substance use. Public Health Rep 121: 382-392.
[24]	Coker AL, Flerx VC, Smith PH, et al. (2007) Intimate partner violence incidence and continuation in a primary care screening program. Am J Epidemiol 165: 821-827. doi: 10.1093/aje/kwk074
[25]	Bowen E, Heron J, Waylen A, et al. (2005) Domestic violence risk during and after pregnancy: findings from a British longitudinal study. Bjog 112: 1083-1089. doi: 10.1111/j.1471-0528.2005.00653.x
[26]	Lagerberg D (2004) A descriptive survey of Swedish child health nurses' awareness of abuse and neglect. II. Characteristics of the children. Acta Paediatr 93: 692-701.
[27]	Murphy LM (2011) Childhood and adolescent violent victimization and the risk of young adult intimate partner violence victimization. Violence Vict 26: 593-607. doi: 10.1891/0886-6708.26.5.593
[28]	Boynton-Jarrett R, Ryan LM, Berkman LF, et al. (2008) Cumulative violence exposure and self-rated health: longitudinal study of adolescents in the United States. Pediatrics 122: 961-970. doi: 10.1542/peds.2007-3063
[29]	Bogat GA, Dejonghe E, Levendosky AA, et al. (2006) Trauma symptoms among infants exposed to intimate partner violence. Child Abuse & Neglect 30: 109-125.
[30]	Garcia-Moreno C, Jansen HA, Ellsberg M, et al. (2006) Prevalence of intimate partner violence: findings from the WHO multi-country study on women's health and domestic violence. Lancet 368: 1260-1269. doi: 10.1016/S0140-6736(06)69523-8
[31]	Mahony DL, Campbell JM (1998) Children witnessing domestic violence: a developmental approach. Clin Excell Nurse Practi 2: 362-369.
[32]	Goodwin RD, Wickramaratne P, Nomura Y, et al. (2007) Familial depression and respiratory illness in children. Arch Pediatr Adolesc Med 161: 487-494. doi: 10.1001/archpedi.161.5.487
[33]	Flynn HA, Davis M, Marcus SM, et al. (2004) Rates of maternal depression in pediatric emergency department and relationship to child service utilization. Gen Hosp Psychiatry 26: 316-322. doi: 10.1016/j.genhosppsych.2004.03.009
[34]	Minkovitz CS, Strobino D, Scharfstein D, et al. (2005) Maternal depressive symptoms and children's receipt of health care in the first 3 years of life. Pediatrics 115: 306-314. doi: 10.1542/peds.2004-0341
[35]	Casey P, Goolsby S, Berkowitz C, et al. (2004) Maternal depression, changing public assistance, food security, and child health status. Pediatrics 113: 298-304. doi: 10.1542/peds.113.2.298
[36]	Weissman MM, Pilowsky DJ, Wickramaratne PJ, et al. (2006) Remissions in maternal depression and child psychopathology: a STAR*D-child report. Jama 295: 1389-1398. doi: 10.1001/jama.295.12.1389
[37]	Kessler RC, McLaughlin KA, Green JG, et al. (2010) Childhood adversities and adult psychopathology in the WHO World Mental Health Surveys. Br J Psychiatry 197:378-385. doi: 10.1192/bjp.bp.110.080499
[38]	Grassi-Oliveira R, Stein LM (2008) Childhood maltreatment associated with PTSD and emotional distress in low-income adults: the burden of neglect. Child Abuse Negl32: 1089-1094.
[39]	Graham-Bermann SA, Seng J (2005) Violence exposure and traumatic stress symptoms as additional predictors of health problems in high-risk children. Journal of Pediatrics 146: 349-354. doi: 10.1016/j.jpeds.2004.10.065
[40]	Friedman MJ SP (1995) The relationship between trauma, posttraumatic stress disorder, and physical health. In: Friedman Mj CDSDAY, editor. Neurobiological and Clinical Consequences of Stress: From Normal Adaption to PTSD. Philadelphia: Lippincott-Raven. pp. 507-524.
[41]	Seng JS, Graham-Bermann SA, Clark MK, et al. (2005) Posttraumatic stress disorder and physical comorbidity among female children and adolescents: results from service-use data. Pediatrics 116: e767-776. doi: 10.1542/peds.2005-0608
[42]	Cisler JM, Begle AM, Amstadter AB, et al. (2012) Exposure to interpersonal violence and risk for PTSD, depression, delinquency, and binge drinking among adolescents: data from the NSA-R. J Trauma Stress 25: 33-40. doi: 10.1002/jts.21672
[43]	Nooner KB, Linares LO, Batinjane J, et al. (2012) Factors related to posttraumatic stress disorder in adolescence. Trauma Violence Abuse 13: 153-166. doi: 10.1177/1524838012447698
[44]	McCart MR, Zajac K, Kofler MJ, et al. (2012) Longitudinal examination of PTSD symptoms and problematic alcohol use as risk factors for adolescent victimization. J Clin Child Adolesc Psychol 41: 822-836. doi: 10.1080/15374416.2012.717872
[45]	Heath NM, Chesney SA, Gerhart JI, et al. (2013) Interpersonal violence, PTSD, and inflammation: potential psychogenic pathways to higher C-reactive protein levels. Cytokine 63: 172-178. doi: 10.1016/j.cyto.2013.04.030
[46]	Ogle CM, Rubin DC, Siegler IC (2013) The Impact of the Developmental Timing of Trauma Exposure on PTSD Symptoms and Psychosocial Functioning Among Older Adults. Dev Psychol.
[47]	Linton SJ, Larden M, Gillow AM (1996) Sexual abuse and chronic musculoskeletal pain: prevalence and psychological factors. Clin J Pain 12: 215-221. doi: 10.1097/00002508-199609000-00009
[48]	Linton SJ (1997) A population-based study of the relationship between sexual abuse and back pain: establishing a link. Pain 73: 47-53. doi: 10.1016/S0304-3959(97)00071-7
[49]	Linton SJ (2002) A prospective study of the effects of sexual or physical abuse on back pain. Pain 96: 347-351. doi: 10.1016/S0304-3959(01)00480-8
[50]	Riley JL, 3rd, Robinson ME, Kvaal SA, et al. (1998) Effects of physical and sexual abuse in facial pain: direct or mediated? Cranio 16: 259-266.
[51]	Campbell JC (2002) Health consequences of intimate partner violence. Lancet 359:1331-1336. doi: 10.1016/S0140-6736(02)08336-8
[52]	Irwin M, Patterson T, Smith TL, et al. (1990) Reduction of immune function in life stress and depression. Biol Psychiatry 27: 22-30. doi: 10.1016/0006-3223(90)90016-U
[53]	Webster J, Creedy DK (2002) Domestic violence. Screening can be made acceptable to women. Bmj 325: 44.
[54]	Stenson K, Posse B (2004) [Routine questions on violence asked in maternal health services are justified]. Lakartidningen 101: 401.
[55]	Wijma B, Heimer G, Wijma K (2002) [Is it possible that the patient has been exposed to violence?Should we ask——and how?]. Lakartidningen 99: 2260-2261,
[56]	Burkert NT, Rasky E, Freidl W, et al. (2013) Female and male victims of violence in an urban emergency room——prevalence, sociodemographic characteristics, alcohol intake, and injury patterns. Wien Klin Wochenschr 125: 134-138. doi: 10.1007/s00508-013-0329-z
[57]	Margolin G, Ramos MC, Baucom BR, et al. (2013) Substance use, aggression perpetration, and victimization: temporal co-occurrence in college males and females. J Interpers Violence 28: 2849-2872. doi: 10.1177/0886260513488683
[58]	Pinchevsky GM, Wright EM, Fagan AA (2013) Gender differences in the effects of exposure to violence on adolescent substance use. Violence Vict 28: 122-144. doi: 10.1891/0886-6708.28.1.122
[59]	Olofsson N, Lindqvist K, Gadin K, et al. (2009) Violence against young men and women: a vital health issue. The Open Health Journal 2: 1-6. doi: 10.2174/1874944500902010001
[60]	Olofsson N, Lindqvist K, Danielsson I (2012) Fear of crime and psychological and physical abuse associated with ill health in a Swedish population aged 65-84 years. Public Health 126: 358-364. doi: 10.1016/j.puhe.2012.01.015
[61]	Ajdukovic M, Ogresta J, Rusac S (2009) Family Violence and Health Among Elderly in Croatia. Journal of Aggression, Maltreatment & Trauma 18: 261-279.
[62]	Lachs MS, Pillemer K (1995) Abuse and neglect of elderly persons. N Engl J Med 332:437-443. doi: 10.1056/NEJM199502163320706
[63]	Dong X, Simon M, Mendes de Leon C, et al. (2009) Elder self-neglect and abuse and mortality risk in a community-dwelling population. Jama 302: 517-526. doi: 10.1001/jama.2009.1109
[64]	Schonfeld L, Larsen RG, Stiles PG (2006) Behavioral health services utilization among older adults identified within a state abuse hotline database. Gerontologist 46:193-199. doi: 10.1093/geront/46.2.193
[65]	Mosqueda L, Burnight K, Liao S, et al. (2004) Advancing the field of elder mistreatment: a new model for integration of social and medical services. Gerontologist 44: 703-708. doi: 10.1093/geront/44.5.703
[66]	Acierno R, Hernandez MA, Amstadter AB, et al. (2009) Prevalence and correlates of emotional, physical, sexual, and financial abuse and potential neglect in the United States: the National Elder Mistreatment Study. Am J Public Health 100: 292-297.
[67]	Amstadter AB, Begle AM, Cisler JM, et al. (2010) Prevalence and correlates of poor self-rated health in the United States: the national elder mistreatment study. Am J Geriatr Psychiatry 18: 615-623. doi: 10.1097/JGP.0b013e3181ca7ef2
[68]	Cisler JM, Amstadter AB, Begle AM, et al. (2010) Elder mistreatment and physical health among older adults: the South Carolina Elder Mistreatment Study. J Trauma Stress 23: 461-467. doi: 10.1002/jts.20545
[69]	Holmström MR, Olofsson N, Asplund K, et al. (2012) Exploring the development of school children's health. British Journal of School Nursing Vol 7: 183-190. doi: 10.12968/bjsn.2012.7.4.183
[70]	Olofsson N (2012) Violence through the life-cycle:a public health problem. Linköping: Linköping University.
[71]	Olofsson N, Lindqvist K, Shaw BA, et al. (2012) Long-term health consequences of violence exposure in adolescence: a 26-year prospective study. BMC Public Health 12:411. doi: 10.1186/1471-2458-12-411
[72]	Chandola T (2001) The fear of crime and area differences in health. Health Place 7:105-116. doi: 10.1016/S1353-8292(01)00002-8
[73]	Surtees PG, Wainwright NW (2007) The shackles of misfortune: social adversity assessment and representation in a chronic-disease epidemiological setting. Soc Sci Med 64: 95-111. doi: 10.1016/j.socscimed.2006.08.013
[74]	Pollitt RA, Rose KM, Kaufman JS (2005) Evaluating the evidence for models of life course socioeconomic factors and cardiovascular outcomes: a systematic review. BMC Public Health 5: 7. doi: 10.1186/1471-2458-5-7
[75]	Hallqvist J, Lynch J, Bartley M, et al. (2004) Can we disentangle life course processes of accumulation, critical period and social mobility? An analysis of disadvantaged socio-economic positions and myocardial infarction in the Stockholm Heart Epidemiology Program. Soc Sci Med 58: 1555-1562.
[76]	Dannefer D (2011) Age, the life Course, and the sociological Imagination: Prospects for theory. In: Binstock RH, George LK, editors. Handbook of Aging and the Social Sciiencces. 7:th ed. Amsterdam: Academic Press. pp. 3-16.
[77]	Lundberg O (1997) Childhood conditions, sense of coherence, social class and adult ill health: exploring their theoretical and empirical relations. Soc Sci Med 44: 821-831. doi: 10.1016/S0277-9536(96)00184-0
[78]	Galobardes B, Lynch JW, Smith GD (2008) Is the association between childhood socioeconomic circumstances and cause-specific mortality established? Update of a systematic review. J Epidemiol Community Health 62: 387-390. doi: 10.1136/jech.2007.065508
[79]	Barker DJ (2000) In utero programming of cardiovascular disease. Theriogenology 53:555-574. doi: 10.1016/S0093-691X(99)00258-7
[80]	Barker DJ, Shiell AW, Barker ME, et al. (2000) Growth in utero and blood pressure levels in the next generation. J Hypertens 18: 843-846. doi: 10.1097/00004872-200018070-00004
[81]	Lundberg O (1993) The impact of childhood living conditions on illness and mortality in adulthood. Soc Sci Med 36: 1047-1052. doi: 10.1016/0277-9536(93)90122-K
[82]	Fors S, Lennartsson C, Lundberg O (2009) Childhood living conditions, socioeconomic position in adulthood, and cognition in later life: exploring the associations. J Gerontol B Psychol Sci Soc Sci 64: 750-757.
[83]	Lynch JW, Kaplan GA, Salonen JT (1997) Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse. Soc Sci Med 44: 809-819.
[84]	Lynch JW, Kaplan GA, Shema SJ (1997) Cumulative impact of sustained economic hardship on physical, cognitive, psychological, and social functioning. N Engl J Med337: 1889-1895.
[85]	Singh-Manoux A, Ferrie JE, Chandola T, et al. (2004) Socioeconomic trajectories across the life course and health outcomes in midlife: evidence for the accumulation hypothesis? Int J Epidemiol 33: 1072-1079. doi: 10.1093/ije/dyh224
[86]	Kivimaki M, Vahtera J, Elovainio M, et al. (2002) Death or illness of a family member, violence, interpersonal conflict, and financial difficulties as predictors of sickness absence: longitudinal cohort study on psychological and behavioral links. Psychosom Med 64: 817-825.
[87]	Haavet OR, Straand J, Saugstad OD, et al. (2004) Illness and exposure to negative life experiences in adolescence: two sides of the same coin? A study of 15-year-olds in Oslo, Norway. Acta Paediatr 93: 405-411.
[88]	Honkalampi K, Koivumaa-Honkanen H, Hintikka J, et al. (2004) Do stressful lifeevents or sociodemographic variables associate with depression and alexithymia among a general population?——A 3-year follow-up study. Compr Psychiatry 45: 254-260.
[89]	Tosevski DL, Milovancevic MP (2006) Stressful life events and physical health. Curr Opin Psychiatry 19: 184-189. doi: 10.1097/01.yco.0000214346.44625.57
[90]	Skillgate E, Vingard E, Josephson M, et al. (2007) Life events and the risk of low back and neck/shoulder pain of the kind people are seeking care for: results from the MUSIC-Norrtalje case-control study. J Epidemiol Community Health 61: 356-361. doi: 10.1136/jech.2006.049411
[91]	Vahtera J, Kivimaki M, Vaananen A, et al. (2006) Sex differences in health effects of family death or illness: are women more vulnerable than men? Psychosom Med 68:283-291. doi: 10.1097/01.psy.0000203238.71171.8d
[92]	Davis MC, Matthews KA, Twamley EW (1999) Is life more difficult on Mars or Venus? A meta-analytic review of sex differences in major and minor life events. Ann Behav Med 21: 83-97.
[93]	Jordanova V, Stewart R, Goldberg D, et al. (2007) Age variation in life events and their relationship with common mental disorders in a national survey population. Soc Psychiatry Psychiatr Epidemiol 42: 611-616. doi: 10.1007/s00127-007-0209-9
[94]	Spurgeon A, Jackson CA, Beach JR (2001) The Life Events Inventory: re-scaling based on an occupational sample. Occup Med (Lond) 51: 287-293. doi: 10.1093/occmed/51.4.287
[95]	Elder GH, Jr. (1994) Time, Human Agency, and Social Change: Perspectives on the Life Course. Social Psychology Quartely 57: 4-15. doi: 10.2307/2786971
[96]	Kendall-Tackett K (2002) The health effects of childhood abuse: four pathways by which abuse can influence health. Child Abuse Negl 26: 715-729. doi: 10.1016/S0145-2134(02)00343-5
[97]	Greenfield EA, Marks NF (2009) Profiles of physical and psychological violence in childhood as a risk factor for poorer adult health: evidence from the 1995-2005 National Survey of Midlife in the United States. J Aging Health 21: 943-966. doi: 10.1177/0898264309343905
[98]	Finkelhor D (1995) The victimization of children: a developmental perspective. Am J Orthopsychiatry 65: 177-193. doi: 10.1037/h0079618
[99]	Shonkoff JP, Garner AS (2012) The lifelong effects of early childhood adversity and toxic stress. Pediatrics 129: e232-246. doi: 10.1542/peds.2011-2663
[100]	Garner AS, Shonkoff JP (2012) Early childhood adversity, toxic stress, and the role of the pediatrician: translating developmental science into lifelong health. Pediatrics 129: e224-231. doi: 10.1542/peds.2011-2662
[101]	Sachs-Ericsson N, Cromer K, Hernandez A, et al. (2009) A review of childhood abuse, health, and pain-related problems: the role of psychiatric disorders and current life stress. J Trauma Dissociation 10: 170-188. doi: 10.1080/15299730802624585
[102]	Finkelhor D, Ormrod RK, Turner HA (2007) Polyvictimization and trauma in a national longitudinal cohort. Dev Psychopathol 19: 149-166.
[103]	Huas C, Hassler C, Choquet M (2008) Has occasional cannabis use among adolescents also to be considered as a risk marker? Eur J Public Health 18: 626-629. doi: 10.1093/eurpub/ckn065
[104]	Miller GE, Chen E, Parker KJ (2011) Psychological stress in childhood and susceptibility to the chronic diseases of aging: moving toward a model of behavioral and biological mechanisms. Psychol Bull 137: 959-997. doi: 10.1037/a0024768
[105]	McEwen BS, Gianaros PJ (2010) Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease. Ann N Y Acad Sci 1186: 190-222. doi: 10.1111/j.1749-6632.2009.05331.x
[106]	Shonkoff JP, Boyce WT, McEwen BS (2009) Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. Jama 301: 2252-2259. doi: 10.1001/jama.2009.754
[107]	Fagundes CP, Glaser R, Kiecolt-Glaser JK (2013) Stressful early life experiences and immune dysregulation across the lifespan. Brain Behav Immun 27: 8-12. doi: 10.1016/j.bbi.2012.06.014
[108]	McEwen BS (2005) Stressed or stressed out: what is the difference? J Psychiatry Neurosci 30: 315-318.
[109]	Danese A, McEwen BS (2012) Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiol Behav 106: 29-39. doi: 10.1016/j.physbeh.2011.08.019
[110]	Altemus M, Cloitre M, Dhabhar FS (2003) Enhanced cellular immune response in women with PTSD related to childhood abuse. Am J Psychiatry 160: 1705-1707. doi: 10.1176/appi.ajp.160.9.1705
[111]	Norman RE, Byambaa M, De R, et al. (2012) The long-term health consequences of child physical abuse, emotional abuse, and neglect: a systematic review and metaanalysis. PLoS Med 9: e1001349. doi: 10.1371/journal.pmed.1001349
[112]	Bauldry S, Shanahan MJ, Boardman JD, et al. (2012) A life course model of self-rated health through adolescence and young adulthood. Soc Sci Med 75: 1311-1320. doi: 10.1016/j.socscimed.2012.05.017
[113]	Fors S, Lennartsson C, Lundberg O (2011) Live long and prosper? Childhood living conditions, marital status, social class in adulthood and mortality during mid-life: a cohort study. Scand J Public Health 39: 179-186.
[114]	Dube SR, Felitti VJ, Dong M, et al. (2003) The impact of adverse childhood experiences on health problems: evidence from four birth cohorts dating back to 1900. Prev Med 37: 268-277. doi: 10.1016/S0091-7435(03)00123-3
[115]	Winersjo R, Ponce de Leon A, Soares JF, et al. (2011) Violence and self-reported health: does individual socioeconomic position matter? J Inj Violence Res 2011: 122.
[116]	Power C, Graham H, Due P, et al. (2005) The contribution of childhood and adult socioeconomic position to adult obesity and smoking behaviour: an international comparison. Int J Epidemiol 34: 335-344. doi: 10.1093/ije/dyh394
[117]	Boynton-Jarrett R, Hair E, Zuckerman B (2013) Turbulent times: effects of turbulence and violence exposure in adolescence on high school completion, health risk behavior, and mental health in young adulthood. Soc Sci Med 95: 77-86. doi: 10.1016/j.socscimed.2012.09.007
[118]	Gluckman PD, Hanson MA (2004) Living with the past: evolution, development, and patterns of disease. Science 305: 1733-1736. doi: 10.1126/science.1095292
[119]	Sokolovsky J (2009) The life course and intergenerational ties in cultural and global context. In: Sokolovsky J, editor. The cultural context of aging: worldwide perspectives. 3rd ed: Greenwood.
[120]	Ogle CM, Rubin DC, Siegler IC (2013) Cumulative exposure to traumatic events in older adults. Aging Ment Health.
[121]	Mishra G, Nitsch D, Black S, et al. (2009) A structured approach to modelling the effects of binary exposure variables over the life course. Int J Epidemiol 38: 528-537. doi: 10.1093/ije/dyn229
[122]	Hart H, Rubia K (2012) Neuroimaging of child abuse: a critical review. Front Hum Neurosci 6: 52.
[123]	Jovanovic T, Norrholm SD, Blanding NQ, et al. (2009) Fear potentiation is associated with hypothalamic-pituitary-adrenal axis function in PTSD. Psychoneuroendocrinology 35: 846-857.
[124]	Woods SJ, Wineman NM, Page GG, et al. (2005) Predicting immune status in women from PTSD and childhood and adult violence. ANS Adv Nurs Sci 28: 306-319. doi: 10.1097/00012272-200510000-00003
[125]	Hegadoren KM, Lasiuk GC, Coupland NJ (2006) Posttraumatic stress disorder Part III: health effects of interpersonal violence among women. Perspect Psychiatr Care 42:163-173. doi: 10.1111/j.1744-6163.2006.00078.x
[126]	Duxbury F (2006) Recognising domestic violence in clinical practice using the diagnoses of posttraumatic stress disorder, depression and low self-esteem. Br J Gen Pract 56: 294-300.
[127]	Griffing S, Lewis CS, Chu M, et al. (2006) Exposure to interpersonal violence as a predictor of PTSD symptomatology in domestic violence survivors. J Interpers Violence 21: 936-954. doi: 10.1177/0886260506288938
[128]	Holbrook TL, Hoyt DB, Stein MB, et al. (2002) Gender differences in long-term posttraumatic stress disorder outcomes after major trauma: women are at higher risk of adverse outcomes than men. J Trauma 53: 882-888. doi: 10.1097/00005373-200211000-00012
[129]	Sledjeski EM, Speisman B, Dierker LC (2008) Does number of lifetime traumas explain the relationship between PTSD and chronic medical conditions? Answers from the National Comorbidity Survey-Replication (NCS-R). J Behav Med 31: 341-349.
[130]	Knapp JF (1998) The impact of children witnessing violence. Pediatr Clin North Am45: 355-364.
[131]	Levendosky AA, Leahy KL, Bogat GA, et al. (2006) Domestic violence, maternal parenting, maternal mental health, and infant externalizing behavior. J Fam Psychol20: 544-552.
[132]	Mullender A (1996) Children living with domestic violence. Adoption and fostering20: 8-15.
[133]	Mullender A, Hague G, Imam U, et al. (2002) Children's Perspectives on Domestic Violence. London: Sage.
[134]	Macmillan R, Hagan J (2004) Violence in the Transition to Adulthood: Adolescent Visctimization, Education; and Socioeconomic Attainment in later Life. Journal of research on adolescence 14: 127-158. doi: 10.1111/j.1532-7795.2004.01402001.x
[135]	Olofsson N, Lindqvist K, Gadin KG, et al. (2011) Physical and psychological symptoms and learning difficulties in children of women exposed and non-exposed to violence: a population-based study. Int J Public Health 56: 89-96. doi: 10.1007/s00038-010-0165-0
[136]	Danielsson M (2009) Public Health report 2009. Västerås: Edita.
[137]	Hooven C, Nurius PS, Logan-Greene P, et al. (2012) Childhood Violence Exposure: Cumulative and Specific Effects on Adult Mental Health. J Fam Violence 27: 511-522. doi: 10.1007/s10896-012-9438-0
[138]	Luthar SS, Cicchetti D, Becker B (2000) The construct of resilience: a critical evaluation and guidelines for future work. Child Dev 71: 543-562. doi: 10.1111/1467-8624.00164
[139]	Ungar M (2013) Resilience, trauma, context, and culture. Trauma Violence Abuse 14:255-266. doi: 10.1177/1524838013487805
[140]	Montgomery SM, Bartley MJ, Wilkinson RG (1997) Family conflict and slow growth. Arch Dis Child 77: 326-330. doi: 10.1136/adc.77.4.326
[141]	Wenzel SL, Tucker JS, Elliott MN, et al. (2004) Physical violence against impoverished women: a longitudinal analysis of risk and protective factors. Womens Health Issues 14: 144-154. doi: 10.1016/j.whi.2004.06.001
[142]	Campbell JC, Lewandowski LA (1997) Mental and physical health effects of intimate partner violence on women and children. Psychiatr Clin North Am 20: 353-374. doi: 10.1016/S0193-953X(05)70317-8
[143]	Aris R (1994) Mathematical Modelling Techniques. New York: Dover Publications INC.
[144]	Karlen J, Ludvigsson J, Frostell A, et al. (2011) Cortisol in hair measured in young adults - a biomarker of major life stressors? BMC Clin Pathol 11: 12. doi: 10.1186/1472-6890-11-12
[145]	Russell E, Koren G, Rieder M, et al. (2012) Hair cortisol as a biological marker of chronic stress: current status, future directions and unanswered questions. Psychoneuroendocrinology 37: 589-601. doi: 10.1016/j.psyneuen.2011.09.009
[146]	Staufenbiel SM, Penninx BW, Spijker AT, et al. (2013) Hair cortisol, stress exposure, and mental health in humans: a systematic review. Psychoneuroendocrinology 38:1220-1235. doi: 10.1016/j.psyneuen.2012.11.015
[147]	Gilbert R, Fluke J, O'Donnell M, et al. (2012) Child maltreatment: variation in trends and policies in six developed countries. Lancet 379: 758-772. doi: 10.1016/S0140-6736(11)61087-8

This article has been cited by:

1.	Zhifeng Dai, Huiting Zhou, Fenghua Wen, Shaoyi He, Efficient predictability of stock return volatility: The role of stock market implied volatility, 2020, 52, 10629408, 101174, 10.1016/j.najef.2020.101174
2.	Md Akther Uddin, Mohammad Enamul Hoque, Md Hakim Ali, International economic policy uncertainty and stock market returns of Bangladesh: evidence from linear and nonlinear model, 2020, 4, 2573-0134, 236, 10.3934/QFE.2020011
3.	Afşin ŞAHİN, Mabruka MOHAMED, Cementing the Asymmetric Confluence of Exchange Rate and Stock Prices: NARDL Modeling for Kuwait, 2020, 1308-5549, 10.18074/ckuiibfd.636490
4.	Tinghui Li, Junhao Zhong, Hai Zhang, Pierre Failler, Chinese financial cycle spillovers to developed countries, 2019, 1, 2643-1092, 364, 10.3934/GF.2019.4.364
5.	Sudeshna Ghosh, Asymmetric impact of COVID-19 induced uncertainty on inbound Chinese tourists in Australia: insights from nonlinear ARDL model, 2020, 4, 2573-0134, 343, 10.3934/QFE.2020016
6.	Jieqiong Yang, Panzhu Luo, Yong Tan, Contingent Decision of Corporate Environmental Responsibility Based on Uncertain Economic Policy, 2020, 12, 2071-1050, 8839, 10.3390/su12218839
7.	Gizem Uzuner, Sudeshna Ghosh, Do pandemics have an asymmetric effect on tourism in Italy?, 2020, 0033-5177, 10.1007/s11135-020-01074-7
8.	Yue Liu, Pierre Failler, Jiaying Peng, Yuhang Zheng, Time-Varying Relationship between Crude Oil Price and Exchange Rate in the Context of Structural Breaks, 2020, 13, 1996-1073, 2395, 10.3390/en13092395
9.	Muhammad Asif Khan, Masood Ahmed, József Popp, Judit Oláh, US Policy Uncertainty and Stock Market Nexus Revisited through Dynamic ARDL Simulation and Threshold Modelling, 2020, 8, 2227-7390, 2073, 10.3390/math8112073
10.	Fenghua Wen, Cui Li, Han Sha, Liuguo Shao, How does economic policy uncertainty affect corporate risk-taking? Evidence from China, 2020, 15446123, 101840, 10.1016/j.frl.2020.101840
11.	Jiaying Peng, Zhenghui Li, Benjamin M. Drakeford, Dynamic Characteristics of Crude Oil Price Fluctuation—From the Perspective of Crude Oil Price Influence Mechanism, 2020, 13, 1996-1073, 4465, 10.3390/en13174465
12.	Prince Mensah Osei, Reginald Djimatey, Anokye M. Adam, Dehua Shen, Economic Policy Uncertainty Linkages among Asian Countries: Evidence from Threshold Cointegration Approach, 2021, 2021, 1607-887X, 1, 10.1155/2021/6656176
13.	Liming Chen, Ziqing Du, Yong Tan, Sustainable exchange rates in China: Is there the heterogeneous effect of economic policy uncertainty?, 2019, 1, 2643-1092, 346, 10.3934/GF.2019.4.346
14.	Rong Li, Sufang Li, Di Yuan, Keming Yu, Does economic policy uncertainty in the U.S. influence stock markets in China and India? Time-frequency evidence, 2020, 52, 0003-6846, 4300, 10.1080/00036846.2020.1734182
15.	Yanhong Feng, Dilong Xu, Pierre Failler, Tinghui Li, Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation, 2020, 12, 2071-1050, 6523, 10.3390/su12166523
16.	Liuguo Shao, Hua Zhang, Jinyu Chen, Xuehong Zhu, Effect of oil price uncertainty on clean energy metal stocks in China: Evidence from a nonparametric causality-in-quantiles approach, 2021, 73, 10590560, 407, 10.1016/j.iref.2021.01.009
17.	Emmanuel Asafo-Adjei, Daniel Agyapong, Samuel Kwaku Agyei, Siaw Frimpong, Reginald Djimatey, Anokye M. Adam, Junhai Ma, Economic Policy Uncertainty and Stock Returns of Africa: A Wavelet Coherence Analysis, 2020, 2020, 1607-887X, 1, 10.1155/2020/8846507
18.	Tangyong Liu, Xu Gong, Lizhi Tang, The uncertainty spillovers of China's economic policy: Evidence from time and frequency domains, 2020, 1076-9307, 10.1002/ijfe.2385
19.	Jian Liu, Ziting Zhang, Lizhao Yan, Fenghua Wen, Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model, 2021, 7, 2199-4730, 10.1186/s40854-021-00292-8
20.	Djula Borozan, Bartol Borozan, The asymmetric effect of economic policy uncertainty on energy consumption, 2022, 15, 1570-646X, 10.1007/s12053-022-10037-w
21.	Sang Hoon Kang, Jose Arreola Hernandez, Mobeen Ur Rehman, Syed Jawad Hussain Shahzad, Seong-Min Yoon, Spillovers and hedging between US equity sectors and gold, oil, islamic stocks and implied volatilities, 2023, 81, 03014207, 103286, 10.1016/j.resourpol.2022.103286
22.	Abdel Razzaq Al Rababa’a, Mohammad Alomari, Mobeen Ur Rehman, David McMillan, Raed Hendawi, Multiscale relationship between economic policy uncertainty and sectoral returns: Implications for portfolio management, 2022, 61, 02755319, 101664, 10.1016/j.ribaf.2022.101664
23.	Meiyu Tian, Wanyang Li, Fenghua Wen, The dynamic impact of oil price shocks on the stock market and the USD/RMB exchange rate: Evidence from implied volatility indices, 2021, 55, 10629408, 101310, 10.1016/j.najef.2020.101310
24.	Huiming Zhu, Hao Wu, Yinghua Ren, Dongwei Yu, Time-frequency effect of investor sentiment, economic policy uncertainty, and crude oil on international stock markets: evidence from wavelet quantile analysis, 2022, 54, 0003-6846, 6116, 10.1080/00036846.2022.2057912
25.	Wen-Yi Chen, Mei-Ping Chen, Twitter’s daily happiness sentiment, economic policy uncertainty, and stock index fluctuations, 2022, 62, 10629408, 101784, 10.1016/j.najef.2022.101784
26.	Kuashuai Peng, Guofeng Yan, A survey on deep learning for financial risk prediction, 2021, 5, 2573-0134, 716, 10.3934/QFE.2021032
27.	Salah A. Nusair, Jamal A. Al-Khasawneh, Impact of economic policy uncertainty on the stock markets of the G7 Countries:A nonlinear ARDL approach, 2022, 26, 17034949, e00251, 10.1016/j.jeca.2022.e00251
28.	Salah A. Nusair, Jamal A. Al-Khasawneh, Changes in oil price and economic policy uncertainty and the G7 stock returns: evidence from asymmetric quantile regression analysis, 2023, 1573-9414, 10.1007/s10644-023-09494-9
29.	Mohammed Armah, Ahmed Bossman, Godfred Amewu, Information flow between global financial market stress and African equity markets: An EEMD-based transfer entropy analysis, 2023, 9, 24058440, e13899, 10.1016/j.heliyon.2023.e13899
30.	Asiye Tutuncu, Burcu Savas Celik, Sukran Kahveci, Do the Political Uncertainty and Geopolitical Risk Indexes in the G-7 Countries Relate to Stock Prices? Fourier Causality Test Evidence, 2024, 1016-8737, 1, 10.1080/10168737.2024.2408483
31.	Lydia N. Kotur, Goodness C. Aye, Peter I. Ater, 2024, 9780443157851, 10.1016/B978-0-44-313776-1.00283-X
32.	Masudul Hasan Adil, Amrita Roy, Asymmetric effects of uncertainty on investment: Empirical evidence from India, 2024, 29, 17034949, e00359, 10.1016/j.jeca.2024.e00359
33.	Jonathan E. Ogbuabor, Oliver E. Ogbonna, Onyinye I. Anthony-Orji, Davidmac O. Ekeocha, Obed I. Ojonta, Symmetric or Asymmetric: How is Economic Growth Responding to Global Economic Uncertainty in Africa's Oil Exporters?, 2023, 32, 12100455, 446, 10.18267/j.pep.836
34.	Lydia N. Kotur, Goodness C. Aye, Josephine B. Ayoola, Asymmetric Effects of Economic Policy Uncertainty on Food Security in Nigeria, 2024, 17, 1911-8074, 114, 10.3390/jrfm17030114
35.	RAHEEL GOHAR, MOHAMED OSMAN, EMMANUEL UCHE, P. A. MARY AUXILIA, BISHARAT HUSSAIN CHANG, THE ECONOMIC POLICY UNCERTAINTY EXTREME DYNAMICS AND ITS EFFECT ON THE EXCHANGE RATE, 2022, 22, 2194-5659, 10.1142/S2194565923500069
36.	Chien-Chiang Lee, Xueli Wen, How Does Exchange Rate Policy Uncertainty Affect Corporate Performance: Evidence from China, 2023, 59, 1540-496X, 3060, 10.1080/1540496X.2023.2205545
37.	Segun Kamoru FAKUNMOJU , ECONOMIC POLICY UNCERTAINTY AND STOCK MARKET DEVELOPMENT IN NIGERIA, 2024, 29, 2353-0758, 29, 10.7862/rz.2024.mmr.18
38.	Burak Gülmez, GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism, 2025, 11, 24058440, e42393, 10.1016/j.heliyon.2025.e42393
39.	Aman Bilal, Shakeel Ahmed, Hassan Zada, Eleftherios Thalassinos, Muhammad Hassaan Nawaz, How Do Asymmetric Oil Prices and Economic Policy Uncertainty Shapes Stock Returns Across Oil Importing and Exporting Countries? Evidence from Instrumental Variable Quantile Regression Approach, 2025, 13, 2227-9091, 93, 10.3390/risks13050093

Reader Comments

Your name:*

Email:*
© 2014 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

AIMS Public Health

2.7 5.3

Metrics

Article views(5538) PDF downloads(1063) Cited by(9)

Preview PDF

Download XML

Export Citation

Article outline

Show full outline

Figures and Tables

Figures(2) / Tables(1)

AIMS Public Health

A life course model of self-reported violence exposure and illhealth with a public health problem perspective

Related Papers:

Abstract

1. Introduction

2. The models and methodology

3. The results

4. Summary and conclusion

Acknowledgements

Conflict of interest

References

This article has been cited by:

Reader Comments

通讯作者: 陈斌, bchen63@163.com

Metrics

Figures and Tables

Other Articles By Authors

Catalog

AIMS Public Health

A life course model of self-reported violence exposure and illhealth with a public health problem perspective

Related Papers:

Abstract

1. Introduction

2. The models and methodology

3. The results

4. Summary and conclusion

Acknowledgements

Conflict of interest

References

This article has been cited by:

Reader Comments

通讯作者: 陈斌, bchen63@163.com

Metrics

Figures and Tables

Other Articles By Authors

Related pages

Tools

Export File

Citation

Format

Content

Catalog