Supplementary Appendix for Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking?

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1 Supplemenary Appendix for Depression Babies: Do Macroeconomic Experiences Affec Risk-Taking? Ulrike Malmendier UC Berkeley and NBER Sefan Nagel Sanford Universiy and NBER Sepember 2009

2 A. Deails on SCF Daa For our empirical analysis, we employ boh he SCF and is precursor surveys. One challenge in he consrucion of such a pooled daa se over a relaively long periods of ime is ha he definiions of some daa iems change over ime. Such changes reflec changes in he survey mehodology and is level of deail, bu also changes in he invesmen environmen ha occurred over he las 50 years. In his secion, we deail how we deal wih hese issues. One problem concerns he consrucion of he sock-marke indicaor variable and he share of liquid asses invesed in socks. Informaion on he equiy porion of muual fund holdings is no available in he SCF prior o 989. However, money-marke muual funds and ax-free muual funds are repored separaely in 983 and 986. In hose years, we coun he porion of muual fund holdings no accouned for by money marke funds and ax-free muual funds as sock holdings. Prior o 983, we include he oal holding of muual funds. Noe ha in hose earlier years, muual fund holdings are raher rivial relaive o direc sock holdings, and money-marke muual funds were jus emerging. For example, according o he Flow of Fund accouns of he Federal Reserve, in 977 he household and non-profi secor held abou $63 billion of corporae equiies direcly, bu only $40 billion of muual fund shares. Even in 983, muual fund holdings are less han one enh of direc corporae equiy holdings of he household and non-profi secor. In 2004, his number is almos 50%. Hence, he coding imprecision due o his missing informaion is unlikely o affec our resuls much. The same issue appears for bond marke. From 989 onwards, bond holdings include he bond share of muual fund holdings, while prior o 989, i comprises only direc holdings of bonds (governmen bonds, corporae bonds, and foreign bonds) and ax-free bond fund holdings. A second se of issues concerns he consrucion of liquid asses. One iem ha one could poenially include is cash value life insurance. We have chosen o exclude his iem for wo reasons. Firs, he cash value informaion is no available prior o 983. Second, even in subsequen surveys, cash value life insurance is nooriously badly measured (see Avery and Elliehausen 990). A hird problem concerns asses held in reiremen accouns. In he old SCF, 977 and earlier, he SCF did no ask respondens o separae financial asses held in reiremen accouns from oher financial asses. Reiremen accouns were also far less imporan a he ime han laer in he sample, as IRA and 40(k) defined conribuion plans did no exis ye. In 2004 and 2007, he SCF has deailed informaion on he percenage allocaion of reiremen accoun asses o socks. From onwards, he SCF repors separaely asses held in reiremen accouns wih some informaion on he allocaion of hese asses. We follow he convenion used by he Federal Reserve Board o inerpre an allocaion of IRAs of mosly socks as 00% socks, mosly ineres bearing as no socks, and spli beween socks and bonds or spli beween socks and money marke accouns as 50% socks, and spli

3 beween socks and bonds and money marke accouns as 30% socks. For 40(k)-ype plans here is only one common spli caegory, for which we assume 50% socks. In 983 and 986, only he oal amoun in IRA and 40(k)-ype plans is available, bu no allocaion informaion. To impue he allocaions, we firs compue he fracion of households in 989 wih IRA bu no 40(k)-ype accoun, ha have he IRA a leas parly invesed in sock, as well as he fracion of hose wih IRA and 40(k) ha have he IRA a leas parly invesed in socks. We calculae similar proporions for 40(k)-ype accoun holders, i.e, how many of hem are a leas parly invesed in socks depending on wheher hey also own an IRA or no. We hen ake hese four percenages and apply hem o 983 and 986 daa by grouping households in hose years ino four caegories depending on wheher hey own an IRA and/or 40(k)-ype accoun, and we randomly assign households o be sockholders in heir IRA and/or 40(k) so ha we mach hese 989 percenages. For hose ha we assign o be sockholders, we assume ha hey inves 75% of heir IRA and/or 40(k) in socks (he average reiremen accoun allocaion o socks in he 989 survey for households ha have greaer han zero holdings in IRA or 40(k) accouns). A fourh issue is ha, in 960, 963, 964, 967, and 977, asse holding values are no given in a direc dollar number, bu insead as a caegorical variable, where each caegory corresponds o a range of values. We assign he midpoin of hese ranges as he dollar value. In 97, we do no have a separae dollar amoun of sock holdings, only a combined number for socks and bonds, and an indicaor variable for greaer han zero sock holdings. Hence, we only consruc he sock-marke variable bu no he sock share of liquid asses for 97. B. Deails on Esimaion As described in Secion II.A, our esimaions follow he mehod of Rubin (987) o accoun for muliple impuaion. The deails are as follows: Le b m be he esimaed coefficien vecor obained from implicae m, m =,, M, and denoe he corresponding covariance marix esimae by V m. The overall poin esimaes are given by he average of he individual implicae poin esimaes: M bm M m = b =. (A.) From he b m we also calculae he beween-implicae variance of he esimaes, M Q= ( bm b)( bm b), (A.2) M m= which is hen combined wih he average covariance marix of he individual implicae esimaes, V M Vm M m = = (A.3) o ge Ω, he overall covariance marix of he coefficien esimaes,

4 Ω= V + + Q (A.4) M For furher deails see Rubin (987). We compue sandard errors using a robus sandwich asympoic covariance marix esimaor. In he case of he probi and ordered probi, he esimaor for he asympoic covariance of N ( b θ ) is N V = { H( b) } gi( b) gi( b) { H( b) } (A.5) N i= where b is he esimaed coefficien vecor, θ is he rue coefficien vecor, N is he number of observaions in he oal pooled sample, H(b) is he Hessian marix of he likelihood funcion, evaluaed a b, and g(b) is he gradien vecor of he likelihood funcion. In he case of non-linear leas squares, N N N 2 ( ) ( ) i i ε ( ) ( ) i i i i( ) i( ) i= i= i= V= g bg b g bg b g bg b (A.6) where g(b) now denoes he gradien vecor of he regression funcion wih respec o he parameer vecor. C. Effecs of Ineria in Porfolio Rebalancing: Simulaions Ineria in rebalancing migh seem as a poenial alernaive explanaion for why pas sock marke could be relaed o he risky asse share in Table IV in he main paper. Here we presen some simulaion evidence showing ha he ime dummies in our regressions absorb he effecs of ineria on porfolio allocaions, and hence he experience effecs ha we documen in our regressions canno be explained by ineria. We consruc a panel of overlapping generaions, where each generaion sars invesing a he age of 25, wih a risky asse share of 50% and lives unil age 75. Every year, we draw IID log sock marke from a normal disribuion wih mean of 8% and sandard deviaion of 20%. Each generaion s risky asse share hen evolves according o a parial adjusmen model, α = ωα + ( ω) α (A.7) d p d where α + represens he desired porfolio share ha he household would have under perfec and p insananeous rebalancing, and α + represens he passive porfolio share, which evolves according o α α ( + r ) = p αr+ where r + represens he (simple, no log) sock marke reurn in year +. Thus, he passive share represens he risky asse share ha he household would have in year + if any changes in porfolio (A.8)

5 allocaions due o realized sock marke are no rebalanced, all riskfree asse are paid ou as cash flows from he porfolio, and no new cash flows ener he porfolio. By eliminaing all oher influences on he risky asse share oher han ha of realized sock marke, we influence of ineria on he risky asse share. The parameer ω in equaion (A.7) conrols he speed of adjusmen. A value of.0 would imply insananeous adjusmen, a value of 0 would imply no adjusmen a all. d d We se he desired porfolio share α + equal o 50%. The exac value of α + is no imporan. Resuls are similar for a wide range of values around 50%. A generaion dies once i has reached he age of 75 and i is replaced in he nex period wih a new generaion of invesors ha sars a age 25. In our d baseline simulaions, a new generaion sars wih a porfolio share equal o α +.= 50%. As an alernaive, we also run simulaions where he iniial porfolio share a age 25 is se equal o he cross-secional mean of he porfolio shares of all he oher generaions in he same year. Thus, in his laer case, he young do he same as everyone else a ha ime, raher han saring ou wih heir arge allocaion. In addiion o he porfolio share hisories of he overlapping generaions, we also keep rack of heir reurn experience hisories. Each period, we calculae he experienced reurn as in he main analysis of he paper according o equaion (), wih he saring poin se a birh (i.e., 25 years before he generaion reaches he invesing age), and given a specific value of he weighing parameer λ. We simulae reurn and porfolio hisories for 50,075 years, of which we discard he firs 75, which are needed o iniialize he overlapping generaions along wih he reurn hisory. Wih he remaining 50,000 cross-secions we hen run pooled OLS regressions, similar o hose in our main analysis in he paper, of he risky asse share on experienced. Table A. repors he slope coefficien on he experienced reurn explanaory variable, corresponding o he coefficien β in our analysis in he main paper. We presen resuls for various parameerizaions of our simulaions. The differen columns vary he weighing parameer λ ha is used o calculae he experienced. Panel A shows resuls when he regressions do no include ime dummies, and Panel B replicaes he regressions ha we run in he paper, which include ime dummies. The hree blocks in each panel differ in he adjusmen speed coefficien φ. The firs block wih φ =0.0 shows wha happens wih exremely srong ineria. Wih an adjusmen coefficien ha low, invesors rebalance very lile. The second block, wih φ = 0.30 is roughly in line wih he degree of porfolio ineria found by Brunnermeier and Nagel (2008) in he Panel Sudy of Income Dynamics (PSID), bu hey cauion ha heir esimaes are likely o be upward biased due o measuremen error. The hird block of resuls is based on φ = 0.64, which is he adjusmen speed coefficien esimaed empirically by Campbell, Calve, and Sodini (2009) from Swedish daa wih an insrumenal variables regression ha eliminaes bias from measuremen error.

6 As Panel A shows, when he regression does no include ime dummies, he slope coefficien on he experienced reurn variable is posiive, and hence goes in he direcion of our esimaes in he paper. In erms of magniude, however, i is also apparen ha even wihou ime dummies in he regressions, i would require an empirically implausible degree of ineria o ge a slope coefficien as big as he one we obain from he SCF. Only wih an adjusmen speed of 0.0, he coefficiens ge close o hose ha we esimae from he SCF. However, our regressions in he paper include ime dummies, so he appropriae comparison is Panel B. The sriking resul in his panel is ha he slope coefficien is eiher zero or negaive for he whole range of λ from 0.0 o 3.0. These simulaion resuls show ha ineria canno explain he posiive slope coefficien on experienced ha we are finding in he SCF daa. In fac, he ineria effec is likely o work agains us by weakening he effec of experienced. Adjused for ineria effecs, he rue regression coefficien on experienced migh even be higher han he esimae we repor in he paper. I may be useful o explain he inuiion for why he regression coefficien in he simulaions wih ime dummies in Panel B urns ou o be zero (in he case of iniial porfolio shares a age 25 equal o he cross-secional mean) or even negaive (in he case of iniial porfolio shares a age 25 equal o 50%). This is easies o see in he firs case. If each generaion sars ou invesing a age 25 wih he iniial risky asse share equal o he cross-secional mean of he risky asse share of he older generaions a ha ime, hen he risky asse shares of all generaions end up being always idenical, wihou any cross-secional variaion, bu only common ime-variaion. This common ime-variaion is compleely absorbed by he ime dummies in he regressions in Panel B. Hence, here is no variaion lef o explain for he experienced reurn variable, which explains is coefficien of exacly zero. In he second case, where new generaions sar ou wih heir arge porfolio share of 50%, he siuaion is a lile more complicaed. I is sill he case ha mos of he variaion over ime in he risky asse shares of differen generaions is common ime variaion, as hey move up and down ogeher from year o year wih realized sock marke. The magniude of he changes in porfolio shares, Δα = α - a -, are no compleely idenical for differen generaions, however, because he levels a are no he same for all generaions, and so a given reurn realizaion leads o somewha differen Δα. The ime dummies herefore do no compleely absorb all variaion in risky asse shares caused by ineria. As i urns ou, hough, he remaining variaion in risky asse shares is acually negaively correlaed wih experienced for empirically relevan parameer values. This effec is driven by differences beween young generaions and he older generaions. Consider a new generaion of invesors ha sars invesing in year a age 25 wih a porfolio share of 50%. Their risky asse share relaive o he crosssecional mean is 0.50 α, where α denoes he cross-secional mean of risky asse shares across all

7 generaions ha are alive and in heir invesing age in year. The cross-secionally de-meaned experienced reurn of he young is A 25, - A, where A 25, is a weighed average of he from year - 24 o year and A is he cross-secional mean of experienced across all generaions in year. Thus, he coefficien in a regression wih ime dummies of risky asse shares on experienced depends on he correlaion beween α and A 25, - srong and/or he weighing parameer λ very high, A. Unless he porfolio ineria is exremely α is more srongly posiively correlaed wih A 25, (which depends on he las 25 years of ) han wih A (which depends on a longer hisory). As a resul, α and A 25, - A.are negaively correlaed. In oher words, he young ypically have risky asse shares below he cross-secional mean in imes when heir experienced are above he crosssecional mean, and vice versa. Since he regressions wih ime dummies effecively de-mean dependen and explanaory variables cross-secionally, hese regressions pick up his negaive correlaion. This explains he negaive coefficiens seen in Panel B of Table A.. Summing up, we conclude ha ineria in rebalancing canno explain he posiive relaionship beween experienced and risky asse shares ha we find empirically in he SCF daa. Mos of he variaion in porfolio shares creaed by ineria in porfolio rebalancing is common ime-variaion ha is absorbed by ime dummies in he regressions. If anyhing, our simulaions show ha ineria in porfolio rebalancing should make i more difficul o deec a posiive relaion beween experienced and porfolio shares in our regressions wih ime dummies. D. Coefficiens on Conrol Variables The ables in he main ex omi he coefficiens on he conrol variables, as hose are no direcly relevan for our analysis. However, he coefficiens on he conrol variables may be of general ineres, and are also useful o see ha he regressions are picking up sysemaic differences beween individuals in heir risk aiudes. Table A.2 repors he coefficien esimaes for he conrol variables from he esimaions in Table II, column (ii), Table III, columns (ii) and (iv), and Table IV, column (ii), i.e., he specificaions ha include liquid asse conrols. The age and year dummy coefficien esimaes and he coefficiens on liquid asses ineraced wih he year dummies are no repored. As he able shows, nonwhie race and higher educaion as are mos srongly associaed wih higher elicied risk olerance and wih higher sock and bond marke. I is noeworhy ha he signs of he coefficiens of hose variables are he same for each one of hese hree risk-aking measures. For he percenage allocaion o For φ = 0.30, for example, λ > 0 is needed o generae a posiive correlaion. For λ =.0, φ < 0.0 is needed o generae a posiive correlaion. None of hese parameer combinaions are empirically plausible.

8 socks, however, none of he conrol variables excep he log income and log income squared have any saisically significan relaionship wih he dependen variable. E. Ineracion of Experience Effecs wih Sophisicaion Proxies In Table A.3 we explore how he srengh of he experience effec varies wih invesor sophisicaion. We use a dummy for a level of liquid asses above he cross-secional median in a given year and a dummy for compleion of a college degree as sophisicaion proxies and inerac hem wih he experienced reurn variable. The weighing parameer in each specificaion is fixed a he value obained in he main analysis, as repored in Table II, column (ii), Table III, columns (ii) and (iv), and Table IV, column (ii). The evidence from he liquid asses dummy ineracion is mixed. For elicied risk olerance he coefficien on he ineracion erm is close o zero, while for sock marke and he percenage allocaion o sock measures he ineracion erm implies a significan lowering of he coefficien on experienced, albei clearly no srong enough o eliminae he experienced reurn effec among he high wealh households. In conras, for bond marke he ineracion coefficien is posiive and significan. The evidence from he college degree dummy ineracion in he lower par of he able provides a clearer picure. Here he coefficien on he ineracion dummy is consisenly posiive for all risk-aking measures. The magniude of he coefficien is relaively small, hough, and no significanly differen from zero. Thus, on balance he evidence does no indicae ha here is a consisenly weaker or sronger experience effec on risk-aking among financially more sophisicaed households. F. Non-Monooniciies in he Weighing Funcion The one-parameer weighing funcion ha we use in our main analysis can ake on a variey of shapes, bu i canno accommodae non-mononiciy, e.g., a hump-shaped paern of weighs. To check wheher such non-monooniciies could be imporan, we experimen wih an alernaive approach ha uses a sep funcion. We spli each individuals life-span ino hree pars of equal lengh and compue he average reurn realized over each one of hose hree subperiods: recen, middle, and early (e.g., for an individual ha is 60 years old in 2007, we calculae average from 987 o 2006 (recen), 967 o 986 (middle), and 947 o 966 (early). We hen regress he risk-aking measures on hese hree subperiod average, using he same conrols as hose in Table II, column (ii), Table III, columns (ii) and (iv), and Table IV, column (ii). Effecively, his assumes a weighing funcion ha is a sep funcion. A hump shape is now possible: in his case, he regression coefficien on he middle subperiod would ake

9 on he highes value. Insead of esimaing wo parameers (β and λ) we are now esimaing hree parameers (he hree regression coefficiens corresponding o he hree subperiod average ). The resuls are shown in Table A.4. For each of he risk-aking measures excep hose based on he percenage allocaion o socks, he esimaed coefficiens show a monoonically declining paern, wih he average reurn of he mos recen hird of he lifespan receiving a saisically significan coefficien, while he esimaed coefficien corresponding o he average reurn over he earlies hird of he lifespan is no significanly differen from zero. For he regressions wih percenage allocaion o socks as he dependen variable, he coefficien on he middle hird has a slighly higher poin esimae han he coefficien on he mos recen hird, bu from he relaively high sandard errors one can see ha his is no saisically reliable evidence in favor of non-monononiciy. Overall, he resuls do no indicae ha our assumpion of a monoonic weighing funcion is in conflic wih he daa. G. Robusness Checks Table A.5 checks he robusness of our resuls wih respec o several addiional changes in mehodology. We repor he esimaes for β and λ in each case. The specificaion corresponds o Table II, column (ii), Table III, columns (ii) and (iv), and Table IV, column (ii) of he main paper, i.e., i includes he liquid asse conrols. The firs block of resuls shows esimaes obained when reiremen asses are excluded from he asse holdings variables from 983 onwards. The esimaes for boh β and λ are close o hose ha we obained wih reiremen accouns included. This shows ha he quesion wheher reiremen accouns should be included or no, and he imprecision wih which reiremen accoun allocaions are esimaed and impued are no crucial issues for our empirical resuls. In he second and hird blocks, we vary he saring poin for he weighing funcion o 0 years before he birh of he household head and o 0 years afer. In he fourh block, we inroduce cohor dummies, described in he main ex. The boom block of resuls in Table A.3 shows ess in which we also include experienced volailiy measures along wih he experienced variable. All esimaions and resuls are described in he main ex.

10 Table A.: Simulaed Regression Coefficiens on Experienced Reurns in Overlapping Generaions Model wih Ineria in Porfolio Rebalancing Adjusmen Speed Iniial Weighing parameer λ share Panel A: Regression wihou ime dummies Mean Mean Mean Panel B: Regression wih ime dummies Mean Mean Mean

11 Dependen variable Table A.2: Conrol Variable Coefficien Esimaes Elicied risk Sock marke. Bond marke % liquid olerance asses in socks Sample Full Full Full Experienced reurn variable Real bond Sock marke %liquid asses in socks Sock marke Excess of socks over bonds African American (0.034) (0.044) (0.04) (0.02) (0.02) Hispanic (0.054) (0.056) (0.065) (0.08) (0.08) Oher non-whie (0.05) (0.064) (0.062) (0.06) (0.06) Non-Whie (pre-983) (0.057) (0.047) (0.034) (0.034) High School compleed (0.037) (0.025) (0.024) (0.0) (0.0) College degree (0.020) (0.02) (0.020) (0.006) (0.006) Married (0.023) (0.024) (0.022) (0.007) (0.007) Reired (0.034) (0.036) (0.033) (0.0) (0.0) #Children (0.09) (0.07) (0.07) (0.005) (0.005) #Children (0.005) (0.004) (0.004) (0.00) (0.00) Log Income (0.75) (0.47) (0.09) (0.046) (0.046) (Log Income) (0.008) (0.007) (0.005) (0.002) (0.002) Has defined benefi plan (0.09) (0.025) (0.023) (0.006) (0.006) Noes: Coefficiens on conrol variables in Tables II, column (ii), Table III, columns (ii) and (iv), and Table IV, column (ii). Year dummies, age dummies, and liquid asses and liquid asses squared ineraced wih year dummies are included in he regressions, bu coefficiens no shown in he able. Esimaions in he columns labeled Full sample use all available daa; esimaions in he las wo columns use eiher he sample of sock marke paricipans or he sample of bond marke paricipans. Observaions are weighed wih SCF sample weighs. Sandard errors shown in parenheses are robus o heeroskedasiciy/misspecificaion of he likelihood funcion and adjused for muliple impuaion.

12 Table A.3: Ineracion of Experience Effec wih Sophisicaion Proxies Dependen variable Elicied risk olerance Sock marke. Bond marke % liquid asses in socks % liquid asses in socks Sample Full Full Full Sock marke Sock marke Experienced reurn variable Real bond Excess of socks over bonds High liquid asses dummy Experienced reurn (.286) (.307) (.476) (0.443) (0.440) Experienced reurn I Liquid asses > median (0.286) (0.340) (0.766) (0.094) (0.52) Weighing parameer λ [fixed] [fixed] [fixed] [fixed] [fixed] College degree dummy Experienced reurn (.49) (.377) (.537) (0.49) (0.464) Experienced reurn I College degree (0.97) (0.830) (0.850) (0.35) (0.209) Weighing parameer λ [fixed] [fixed] [fixed] [fixed] [fixed] Noes: Models and conrols as in Table II, column (ii), Table III, columns (ii) and (iv), and Table IV, column (ii) of he main paper, bu wih experienced real ineraced wih a dummy ha equals one for households ha have liquid asses higher han he median in a given year. The λ parameer is fixed a he value obained in he earlier regressions ha did no include he ineracion erm. The experienced sock reurn is calculaed from he real reurn on he S&P500 index. The experienced bond reurn is calculaed from he real reurn on long-erm U.S. Treasury bonds. Esimaions in he columns labeled Full sample use all available daa; esimaions in he las wo columns use eiher he sample of sock marke paricipans or he sample of bond marke paricipans. Observaions are weighed wih SCF sample weighs. Sandard errors shown in parenheses are robus o heeroskedasiciy/misspecificaion of he likelihood funcion and adjused for muliple impuaion.

13 Table A.4: Sep Funcion as Alernaive Weighing Funcion Dependen variable Elicied risk olerance Sock marke. Bond marke % liquid asses in socks % liquid asses in socks Sample Full Full Full Sock marke Sock marke Experienced reurn variable Real bond Excess of socks over bonds Average reurn recen hird of lifespan (0.942) (0.792) (0.95) (0.268) (0.29) Average reurn middle hird of lifespan (0.495) (0.456) (0.485) (0.5) (0.32) Average reurn early hird of lifespan (0.366) (0.320) (0.366) (0.03) (0.086) Noes: Conrol variables as in Table II, column (ii), Table III, columns (ii) and (iv), and Table IV, column (ii) of he main paper. The average sock reurn is calculaed from he real reurn on he S&P500 index. The average bond reurn is calculaed from he real reurn on long-erm U.S. Treasury bonds. Esimaions in he columns labeled Full sample use all available daa; esimaions in he las wo columns use eiher he sample of sock marke paricipans or he sample of bond marke paricipans. Observaions are weighed wih SCF sample weighs. Sandard errors shown in parenheses are robus o heeroskedasiciy/misspecificaion of he likelihood funcion and adjused for muliple impuaion.

14 Dependen variable Table A.5: Mehodological Variaions Elicied risk olerance Sock mk. Bond marke Sample Full Full Full Experienced reurn variable Real bond % liquid asses in socks Sock marke %liquid asses in socks Sock marke Excess of socks over bonds Reiremen asses excluded: β (.263) (.372) (.670) (0.558) (0.479) λ (0.309) (0.234) (0.307) (0.287) (0.429) Saring 0 yrs afer birh: β (0.834) (0.969) (0.956) (0.308) (0.293) λ (0.224) (0.69) (0.90) (0.223) (0.288) Saring 0 yrs before birh: β (.946) (2.704) (.78) (.22) (0.605) λ (0.430) (0.277) (0.297) (0.350) (0.544) Cohor dummies included: β (2.05) (.752) (4.35) (0.690) (0.687) λ [fixed] [fixed] [fixed] [fixed] [fixed] Geomerically averaged : β (.272) (.273) (.672) (0.445) (0.43) λ (0.288) (0.246) (0.30) (0.286) (0.383)

15 Unweighed: β (.206) (.2) (.26) (0.394) (0.403) λ (0.242) (0.6) (0.9) (0.35) (0.30) Experienced volailiy included: Experienced reurn (.282) (.30) (.962) (0.45) (0.439) Experienced volailiy (3.08) (.65) (.664) (0.525) (0.45) λ [fixed] [fixed] [fixed] [fixed] [fixed] Noes: Conrol variables as in Table II, column (ii), Table III, columns (ii) and (iv), and Table IV, column (ii) of he main paper. Observaions are weighed wih SCF sample weighs. Sandard errors shown in parenheses are robus o heeroskedasiciy/ misspecificaion of he likelihood funcion and adjused for muliple impuaion.

Chapter 8: Regression with Lagged Explanatory Variables

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