Distributed lag

<h2 id="unstructured-estimation">Unstructured estimation</h2>
The simplest way to estimate parameters associated with distributed lags is by <a href="/facts/Ordinary_least_squares/q7H9k5vM">ordinary least squares</a>, assuming a fixed maximum lag 
 
 
 
 p
 
 
 {\displaystyle p}
 
, assuming <a href="/facts/Independently_and_identically_distributed/othIRaWt">independently and identically distributed</a> errors, and imposing no structure on the relationship of the coefficients of the lagged explanators with each other. However, <a href="/facts/Multicollinearity/bQXzB6sA">multicollinearity</a> among the lagged explanators often arises, leading to high variance of the coefficient estimates.

<h2 id="structured-estimation">Structured estimation</h2>
Structured distributed lag models come in two types: finite and infinite. Infinite distributed lags allow the value of the independent variable at a particular time to influence the dependent variable infinitely far into the future, or to put it another way, they allow the current value of the dependent variable to be influenced by values of the independent variable that occurred infinitely long ago; but beyond some lag length the effects taper off toward zero. Finite distributed lags allow for the independent variable at a particular time to influence the dependent variable for only a finite number of periods.

<h3>Finite distributed lags</h3>
The most important structured finite distributed lag model is the <a href="/facts/Shirley_Montag_Almon/8YaVAy7m">Almon</a> lag model.<a class="footnote-ref" id="fnref:3" href="#fn:3">3</a> This model allows the data to determine the shape of the lag structure, but the researcher must specify the maximum lag length; an incorrectly specified maximum lag length can distort the shape of the estimated lag structure as well as the cumulative effect of the independent variable. The Almon lag assumes that k + 1 lag weights are related to n + 1 linearly estimable underlying parameters (n < k) aj according to

w
          
            i
          
        
        =
        
          ∑
          
            j
            =
            0
          
          
            n
          
        
        
          a
          
            j
          
        
        
          i
          
            j
          
        
      
    
    {\displaystyle w_{i}=\sum _{j=0}^{n}a_{j}i^{j}}

for 
 
 
 
 i
 =
 0
 ,
 …
 ,
 k
 .
 
 
 {\displaystyle i=0,\dots ,k.}

<h3>Infinite distributed lags</h3>
The most common type of structured infinite distributed lag model is the geometric lag, also known as the Koyck lag. In this lag structure, the weights (magnitudes of influence) of the lagged independent variable values decline exponentially with the length of the lag; while the shape of the lag structure is thus fully imposed by the choice of this technique, the rate of decline as well as the overall magnitude of effect are determined by the data. Specification of the regression equation is very straightforward: one includes as explanators (right-hand side variables in the regression) the one-period-lagged value of the dependent variable and the current value of the independent variable:

y
          
            t
          
        
        =
        a
        +
        λ
        
          y
          
            t
            −
            1
          
        
        +
        b
        
          x
          
            t
          
        
        +
        
          error term
        
        ,
      
    
    {\displaystyle y_{t}=a+\lambda y_{t-1}+bx_{t}+{\text{error term}},}

where 
 
 
 
 0
 ≤
 λ
 <
 1
 
 
 {\displaystyle 0\leq \lambda <1}
 
. In this model, the short-run (same-period) effect of a unit change in the independent variable is the value of b, while the long-run (cumulative) effect of a sustained unit change in the independent variable can be shown to be

b
        +
        λ
        b
        +
        
          λ
          
            2
          
        
        b
        +
        .
        .
        .
        =
        b
        
          /
        
        (
        1
        −
        λ
        )
        .
      
    
    {\displaystyle b+\lambda b+\lambda ^{2}b+...=b/(1-\lambda ).}

Other infinite distributed lag models have been proposed to allow the data to determine the shape of the lag structure. The polynomial inverse lag<a class="footnote-ref" id="fnref:4" href="#fn:4">4</a><a class="footnote-ref" id="fnref:5" href="#fn:5">5</a> assumes that the lag weights are related to underlying, linearly estimable parameters aj according to

w
          
            i
          
        
        =
        
          ∑
          
            j
            =
            2
          
          
            n
          
        
        
          
            
              a
              
                j
              
            
            
              (
              i
              +
              1
              
                )
                
                  j
                
              
            
          
        
        ,
      
    
    {\displaystyle w_{i}=\sum _{j=2}^{n}{\frac {a_{j}}{(i+1)^{j}}},}

for 
 
 
 
 i
 =
 0
 ,
 …
 ,
 ∞
 .
 
 
 {\displaystyle i=0,\dots ,\infty .}

The geometric combination lag<a class="footnote-ref" id="fnref:6" href="#fn:6">6</a> assumes that the lags weights are related to underlying, linearly estimable parameters aj according to either

w
          
            i
          
        
        =
        
          ∑
          
            j
            =
            2
          
          
            n
          
        
        
          a
          
            j
          
        
        (
        1
        
          /
        
        j
        
          )
          
            i
          
        
        ,
      
    
    {\displaystyle w_{i}=\sum _{j=2}^{n}a_{j}(1/j)^{i},}

for 
 
 
 
 i
 =
 0
 ,
 …
 ,
 ∞
 
 
 {\displaystyle i=0,\dots ,\infty }
 
 or

w
          
            i
          
        
        =
        
          ∑
          
            j
            =
            1
          
          
            n
          
        
        
          a
          
            j
          
        
        [
        j
        
          /
        
        (
        n
        +
        1
        )
        
          ]
          
            i
          
        
        ,
      
    
    {\displaystyle w_{i}=\sum _{j=1}^{n}a_{j}[j/(n+1)]^{i},}

for 
 
 
 
 i
 =
 0
 ,
 …
 ,
 ∞
 .
 
 
 {\displaystyle i=0,\dots ,\infty .}

The gamma lag<a class="footnote-ref" id="fnref:7" href="#fn:7">7</a> and the rational lag<a class="footnote-ref" id="fnref:8" href="#fn:8">8</a> are other infinite distributed lag structures.

<h2 id="distributed-lag-model-in-health-studies">Distributed lag model in health studies</h2>
Distributed lag models were introduced into health-related studies in 2000 by Schwartz<a class="footnote-ref" id="fnref:9" href="#fn:9">9</a> and 2002 by Zanobetti and Schwartz.<a class="footnote-ref" id="fnref:10" href="#fn:10">10</a> The Bayesian version of the model was suggested by Welty in 2007.<a class="footnote-ref" id="fnref:11" href="#fn:11">11</a> Gasparrini introduced more flexible statistical models in 2010<a class="footnote-ref" id="fnref:12" href="#fn:12">12</a> that are capable of describing additional time dimensions of the exposure-response relationship, and developed a family of distributed lag non-linear models (DLNM), a modeling framework that can simultaneously represent non-linear exposure-response dependencies and delayed effects.<a class="footnote-ref" id="fnref:13" href="#fn:13">13</a>
The distributed lag model concept was first to applied to <a href="/facts/Longitudinal_cohort_study/NeAbQH7s">longitudinal cohort</a> research by Hsu in 2015,<a class="footnote-ref" id="fnref:14" href="#fn:14">14</a> studying the relationship between <a href="/facts/Particulates/RleGr5E6">PM2.5</a> and child <a href="/facts/Asthma/7GkJvxn3">asthma</a>, and more complicated distributed lag method aimed to accommodate <a href="/facts/Longitudinal_cohort_study/NeAbQH7s">longitudinal cohort</a> research analysis such as Bayesian Distributed Lag Interaction Model<a class="footnote-ref" id="fnref:15" href="#fn:15">15</a> by Wilson have been subsequently developed to answer similar research questions.

<h2 id="see-also">See also</h2>
<ul><li><a href="/facts/ARMAX/sALPoUzm">ARMAX</a></li>
<li><a href="/facts/Mixed_data_sampling/zrHbzjjm">Mixed data sampling</a></li></ul>

<h2 id="references">References</h2>

<ol>
<li id="fn:1">Cromwell, Jeff B.; et al. (1994). Multivariate Tests For Time Series Models. SAGE Publications. ISBN 0-8039-5440-9. <a href="0-8039-5440-9" target="_blank">0-8039-5440-9</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></li>
<li id="fn:2">Judge, George G.; Griffiths, William E.; Hill, R. Carter; Lee, Tsoung-Chao (1980). The Theory and Practice of Econometrics. New York: Wiley. pp. 637–660. ISBN 0-471-05938-2. <a href="0-471-05938-2" target="_blank">0-471-05938-2</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></li>
<li id="fn:3">Almon, Shirley, "The distributed lag between capital appropriations and net expenditures," Econometrica 33, 1965, 178-196. <a href="#fnref:3" class="footnote-back-ref">↩</a></li>
<li id="fn:4">Mitchell, Douglas W., and Speaker, Paul J., "A simple, flexible distributed lag technique: the polynomial inverse lag," Journal of Econometrics 31, 1986, 329-340. <a href="#fnref:4" class="footnote-back-ref">↩</a></li>
<li id="fn:5">Gelles, Gregory M., and Mitchell, Douglas W., "An approximation theorem for the polynomial inverse lag," Economics Letters 30, 1989, 129-132. <a href="#fnref:5" class="footnote-back-ref">↩</a></li>
<li id="fn:6">Speaker, Paul J., Mitchell, Douglas W., and Gelles, Gregory M., "Geometric combination lags as flexible infinite distributed lag estimators," Journal of Economic Dynamics and Control 13, 1989, 171-185. <a href="#fnref:6" class="footnote-back-ref">↩</a></li>
<li id="fn:7">Schmidt, Peter (1974). "A modification of the Almon distributed lag". Journal of the American Statistical Association. 69 (347): 679–681. doi:10.1080/01621459.1974.10480188. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></li>
<li id="fn:8">Jorgenson, Dale W. (1966). "Rational distributed lag functions". Econometrica. 34 (1): 135–149. doi:10.2307/1909858. JSTOR 1909858. <a href="/wiki/Econometrica" target="_blank">/wiki/Econometrica</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></li>
<li id="fn:9">Schwartz, J. (May 2000). "The distributed lag between air pollution and daily deaths". Epidemiology (Cambridge, Mass.). 11 (3): 320–326. doi:10.1097/00001648-200005000-00016. ISSN 1044-3983. PMID 10784251. <a href="https://pubmed.ncbi.nlm.nih.gov/10784251/" target="_blank">https://pubmed.ncbi.nlm.nih.gov/10784251/</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></li>
<li id="fn:10">Zanobetti, Antonella; Schwartz, Joel; Samoli, Evi; Gryparis, Alexandros; Touloumi, Giota; Atkinson, Richard; Le Tertre, Alain; Bobros, Janos; Celko, Martin; Goren, Ayana; Forsberg, Bertil (January 2002). "The temporal pattern of mortality responses to air pollution: a multicity assessment of mortality displacement". Epidemiology. 13 (1): 87–93. doi:10.1097/00001648-200201000-00014. ISSN 1044-3983. PMID 11805591. S2CID 25181383. <a href="https://pubmed.ncbi.nlm.nih.gov/11805591" target="_blank">https://pubmed.ncbi.nlm.nih.gov/11805591</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></li>
<li id="fn:11">Welty, L. J.; Peng, R. D.; Zeger, S. L.; Dominici, F. (March 2009). "Bayesian distributed lag models: estimating effects of particulate matter air pollution on daily mortality". Biometrics. 65 (1): 282–291. doi:10.1111/j.1541-0420.2007.01039.x. ISSN 1541-0420. PMID 18422792. <a href="https://pubmed.ncbi.nlm.nih.gov/18422792" target="_blank">https://pubmed.ncbi.nlm.nih.gov/18422792</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></li>
<li id="fn:12">Gasparrini, A; Armstrong, B; Kenward, M G (2010-09-20). "Distributed lag non-linear models". Statistics in Medicine. 29 (21): 2224–2234. doi:10.1002/sim.3940. ISSN 0277-6715. PMC 2998707. PMID 20812303. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2998707" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2998707</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></li>
<li id="fn:13">"Distributed Lag Non-Linear Models [R package dlnm version 2.4.6]". cran.r-project.org. 2021-06-15. Retrieved 2021-09-17. <a href="https://cran.r-project.org/package=dlnm" target="_blank">https://cran.r-project.org/package=dlnm</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></li>
<li id="fn:14">Leon Hsu, Hsiao-Hsien; Mathilda Chiu, Yueh-Hsiu; Coull, Brent A.; Kloog, Itai; Schwartz, Joel; Lee, Alison; Wright, Robert O.; Wright, Rosalind J. (2015-11-01). "Prenatal Particulate Air Pollution and Asthma Onset in Urban Children. Identifying Sensitive Windows and Sex Differences". American Journal of Respiratory and Critical Care Medicine. 192 (9): 1052–1059. doi:10.1164/rccm.201504-0658OC. ISSN 1073-449X. PMC 4642201. PMID 26176842. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4642201" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4642201</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></li>
<li id="fn:15">Wilson, Ander; Chiu, Yueh-Hsiu Mathilda; Hsu, Hsiao-Hsien Leon; Wright, Robert O.; Wright, Rosalind J.; Coull, Brent A. (July 2017). "Bayesian distributed lag interaction models to identify perinatal windows of vulnerability in children's health". Biostatistics. 18 (3): 537–552. doi:10.1093/biostatistics/kxx002. ISSN 1465-4644. PMC 5862289. PMID 28334179. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862289" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862289</a> <a href="#fnref:15" class="footnote-back-ref">↩</a></li>
</ol>

Distributed lag open-in-new

Distributed lag