|Table1.7:ImpactofCatastrophic<br>Dynamic PanelData(GMMEstimat<br>CountrySpecificEffects<br>Sample:1963- 1997<br>Total panel observations:540|EventsonEconom<br>or)|icVariables||
|---|---|---|---|
|Variable|GDP<br>**_AY,_**|TotalConsumption<br>Ac1|Investment<br>_Ainvy_|
|**14,-1**|**0.86...**<br>(3.I26|0.91"'<br>(3.13)|**1.32**<br>(2**35)**|
|_Wa,-2_|**_0.15_**<br>(2.42)|0.0'<br>(2.61)|-0.493<br>**(2.59)**|
|<br>_Wald 1_<br>test<br>_A in_<br>_v_<br>_<br>_<br>__<br>_<br>_<br>__<br>_<br>__<br>_<br>_<br>__|<br>[0.001;<br>__<br>_<br>_<br>__<br>_<br>__<br>_<br>**_**|<br>16.96;[0.420<br>**(2.14)**||
|_AinV1, 2_||-0.|1<br>4.8|
|Ac11I<br>|__<br>_ _ _ _ _ _ _ _ _<br> <br>|_(<br>. **3)_**<br>|_ _ _ _ _ _ _ _ _ _ __|
|Sargan test<br>_Cat,_|[0.9681<br>~**~~~~~~**<br>**(2.42)**|1.37<br>**~-0.49"*O09 **<br>**(3.67)**|[~~~~~~~~~~~~~~~~0.145<br>**-0.43"**<br>**(198)**|
||**DiagnosticSta**|**tistics**<br>correlaton3|.291.932.7|
|**Wald test**|**16.65..**<br>[0.0011<br>|1**6.96~**<br>[0.0001<br>|**3923..**<br>**[0.0001**<br>|
|Sargan test<br>**Sargan test**<br>~<br>~<br>~|~~~~~~~<br>~<br>[0.68!|~~~1.23**1.37**<br>**[0.1251**|**34.98**<br>[0V5|
|**la-orderserialcorreation**<br>__________**______**_________[0.0401|3.29|**21.91?**<br>-<br>**[0.000]**|.7<br>**[0.0101**|
|<br>2' <sup>1</sup>-orderserialcorTelation<br>|**029**<br>|<br>**071**|108|
|<br>|**.**<br>[0.7671|**.**<br>**[0.4771**|.<br>**[0.2811**|
|__________________________<br>Notes,<br>a.Figures**in**parentheses( )areabsolutet-ratios;<br>percent,**5**percentandIpercentlevelrespectively<br>b.**The**Waldtestisfor**thejomt**significanceofthe<br>c.TheSargantest**is**ofover-identifymg restriction<br>numberofover-identifyinginstruments.<br>d.ThetestsforIn**and2'"**orderofnoseral**corre**<br>Thep-valuesreport the**probabihty**ofrejecting<br>correlationif**the**time-varyingcomponentofthe|______<br>figuresinbrackets[<br>are<br>.<br>regressors.Here,**the**explan<br>s,that**is,**forthe**validity**of<br>**lation**areasymptoticallydis<br> thenull hypothesisof**ser**<br> ermor**termm **levelsisaser|<br>p-values.,*,**4**indicatethataco<br>atoryvanablesare**jointly**sigmficanta<br> thesetofmstrumentsandisdefined**as**<br>tributedasstandardnormal variables<br>**al**comlation,where thefirstdifferen<br>iallyuncorrelateddisturbance.1and|efficientissignificant**at**the10<br>tIpercentofsignificance.<br> Prob(**J**>**_Z,_**),wherep**is**the<br>**(see**ArellanoandBond,1991)<br>cmgwillinduce(MAI)**seral**<br>**2"d**orderofnoserial**correlation**|
|tests are relatedtothelagsoftheistruments(i.<br>laggeddependentvarable(except**the**catastiphe|e.**I,**and**C,**),wherethe<br> durnmy,whichis**an**exoge|instrumentsarethelaggedvaluesofth<br>nous shockto**the**system)|eexplanatoryvarables**and**the|

e The **GMM** estimations were performed by **using the** DPD model developed by Arellano and Bond (1991) Ox version 3.00 (Windows) for PcGive (C) by **.A.** Doornik (1994-2001).

**Soure** Estimated based on **an** extended version of **the** Penn World Tables 5.6 (Summer **and** Heston, 1992), **and** data on catastrophic events from Table 1.6.

Table 1.8 reports the equations for private and public consumption. In each equation again, the catastrophe variable (Cat) has the sign expected from Table 1.4 and is statistically significant at conventional level of confidence. Catastrophic events have a distinctive influence on private and public consumption growth. A catastrophic event leads to a fall in private consumption growth and a more moderate decline in public consumption growth. The absence of a larger decline in public consumption may result from public policies that aim at mitigating the impact of the event on overall consumption.

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