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The wealth effects of quantitative easing.

The Federal Reserve, or the Fed, used an unusually wide range of policy tools during and following the financial crisis. These included bailouts of selected financial institutions, rescue of the commercial paper market, the manipulation of interest paid on bank reserves, and the announcement of a plan to keep the policy rate low years into the future. Other policies intended to keep long-term interest rates down include Operation Twist, quantitative easing rounds one (QEI), two (QEII), and three (QEIII), and the purchase of bonds in mortgage-backed security markets. Former Fed Chairman Bernanke made it clear that the Fed's aggressive policies should work not only through low interest rates alone, but also through enhanced wealth creation.

"If people feel that their financial situation is better because their 401(k) looks better or for whatever reason--their house is worth more--they're more willing to go out and spend ... That's going to provide the demand that firms need in order to be willing to hire and invest" (Bernanke 2012, p. 1).

The size, precise channels, and scope of those wealth effects are subject to debate. For example, while Buiter (2010) questions the existence of housing as wealth, Case et al. (2005) determined a stronger wealth effect on consumption through housing markets than stock markets.

Our Federal Reserve wealth literature review focuses on the identification of potential channels of influence from monetary policy actions to eventual effects on consumption and investment. Many studies were drawn from periods which precede the recent financial crisis. Acknowledgement of these contributions permits us to limit our empirical inquiry to the initial half of the wealth linkage, that is, from policy actions during the crisis to the effect on equity and housing markets, with the understanding that consumption and investment subsequently respond to changes in the valuation of equity and housing market wealth.

The purpose of this paper is twofold: to determine whether aggregate monetary policy variables such as the monetary base or excess reserves can be shown to have influenced the equity and housing markets over the crisis period, and to examine the effects of specific policy actions, such the purchase of mortgage-backed securities and Operation Twist, on the financial markets. Employing vector auto regressions (VARs), which may be better suited to the task than event studies, we find a consistently positive effect during the crisis era of general monetary policy variables on the financial markets, and clear effectiveness of some of the Fed's innovative policy actions.

The Wealth Effect

The Federal Reserve began to react to a growing financial crisis in the second half of 2007, cutting the discount rate, extending the loan repayment period and initiating the Term Auction Facility. Some subsequent actions, such as the rescue of Bear-Steams, can be deemed micro, with macro intent to signal the Fed's desire to assist troubled banks, but most involved enormous expansion of the central bank's balance sheet over an extended period.

Much of the expansion was accomplished through massive purchases of Treasury and mortgage-backed securities. With the federal funds rate fixed at near-zero, the Fed's ability to lower long-term interest rates was now better facilitated through direct purchase of long-term securities rather than through its usual short-term expectations route. Lower long-term interest rates directly affected borrowing rates and, it was hoped, indirectly translated into higher prices for equities and housing. Although some wealth effect studies focus almost exclusively on changes in stock valuations, other assets should not be neglected. Financial assets account for 37.9 % of all household assets. However, the primary residence accounts for 29.4 % and other real estate assets account for an additional 11.1 (1)

Poterba (2000, p. 104) observed: "Distinguishing corporate stock and other components of wealth can be important for analyzing wealth effects. There are conceptual reasons to expect differences in the marginal propensity to consume out of changes in the value of different wealth stocks." Poterba shows that the largest percentage of wealth creation in the 1990s occurred with household stock holdings, which likely contributed in substantial measure to the expansion of consumer expenditures that decade.

The Federal Reserve has long displayed an interest in the magnitude of the wealth effect, the channels through which it travels, and what it includes. William McChesney Martin, Federal Reserve Board Chairman 1951-1970, was a former president of the New York Stock Exchange who delineated specific channels through which increased stock market credit influenced real economic activity: (1) it increases the demand for stocks, resulting in higher stock prices, an encouragement to firms to issue additional stock that might be used to spur investment in plant and equipment; (2) if the additional stock market credit is financed by bank credit, it may lead to an increase in the money supply; (3) higher stock prices stimulate consumer expenditures; and (4) the velocity of money increases if expectations of continuing market gains encourage the activation of idle balances. (2)

At Alan Greenspan's first Federal Open Market Committee (FOMC) meeting he wondered aloud why, after a full morning's discussion, no one had mentioned the stock market (FOMC transcript, August 18, 1987). It was mentioned with some frequency, afterward, particularly in the wake of the October 1987 stock market crash. Escalating stock market prices through the mid-and-late 1990s caught the attention of FOMC members generally, and Greenspan in particular. A sampling of his comments: "Obviously implicit in any evaluation of stock market wealth going into consumption, one would have to trace to be sure that the level of capital investment is reflecting the stock market wealth creation" (FOMC transcript, December 19, 1995, p. 11). "Aggregate demand is currently running in excess of potential growth by about that amount. And that, as best I can judge, is being engendered wholly by a wealth effect" (FOMC transcript, June 30, 1999, pp. 86-87). "A wealth effect on consumption greater than the generally accepted figure of three or four cents per dollar of capital gains is consistent with ... all of the [excessive aggregate demand] problems [that] stem from a wealth effect" (FOMC transcript, December 21, 1999, pp. 45-46).

The stock market reached a peak in 2000 while home prices continued to rise. Greenspan assessed the impact of each on spending in a 2001 speech: "even setting aside all pension-type assets, household gains on directly held equities and mutual funds in recent years have been two to four times the size of overall gains on homes. The sheer size of such gains suggest that capital gains on equities have been a more potent factor in determining spending than gains on homes" (August 31, 2001, p. 5).

With the long-term gains in stock and housing markets, other analysts were also inspired to examine the differential impacts of the two major components of household wealth. Case et al. obtained empirical results that were the opposite of Greenspan's "[T]he evidence of a stock market wealth effect is weak; the common presumption that there is strong evidence for the wealth effect is not supported in our results. However, we do find strong evidence, that variations in housing market wealth have important effects on consumption" (Case et al. 2005, pp. 10-11). Their results in favor of housing markets are supported by, among others, Tracy et al. (1999), and Benjamin et al. (2004). A partial list of those who favor the impact of stock markets over housing markets includes Elliott (1980) and Ansari (2009).

Within the Federal Reserve, Ludvigson et al. (2002) of the New York Fed concluded that the wealth effect on consumption is relatively weak, particularly if a stock market price shift is perceived as temporary. On the other hand, Palumbo and Rudd, both of the Board of Governors, and Whelan (Palumbo et al. 2006) determined a wealth effect with flow of funds data about twice the typical finding of three or four cents on the dollar.

After monetary policy reached the limits of its traditional effectiveness with a near-zero federal funds rate, Bernanke's Fed initiated QE through massive purchases of Treasury and mortgage-backed securities. He cited housing, stock, and interest rate channels of monetary influence in support of these innovative actions: "Lower mortgage rates will make housing more affordable and allow more homeowners to refinance. Lower corporate bond rates will encourage investment and higher stock prices will boost consumer wealth and help increase confidence, which can also spur spending" (Bernanke 2010).

Event Studies

Event studies have shown large scale asset purchases (LSAPs) to be successful in lowering interest rates. Krishnamurthy and Vissing-Jorgensen (2011) looked at QE1 and QE2 using event studies. They found both programs were effective in reducing rates. A key contribution of their work is their attention to a range of interest rates and the effects of purchasing different types of securities on those rates. Gagnon et al. (2011) also found the LSAPs to be effective in lowering interest rates. A unique feature of their paper is the finding that most of the fall in interest rates came through a reduction in the risk premium rather than reduced expectations for future short-term interest rates. Two papers look at the effectiveness of LSAPs in raising asset prices in the context of a policy target rate stuck at the lower bound of zero. Rosa (2012) used an event study to examine the surprise component of LSAP announcements. He "concludes that for most asset prices the effects of asset purchases are not statistically different in the U.S. from an unanticipated cut in the target rate." (Rosa 2012, p 15.) His study goes beyond bonds to find significant effects on stocks and exchange rates. Kiley (2013) found that the effect of monetary policy on equity prices was attenuated once the federal funds rate reached its zero bound. A 1 % decline in long-term Treasury rates caused by monetary policy was associated with a 6-9 % increase in equity prices prior to the policy rate reaching its zero bound and only a 1.5 to 3 % increase in the period after the policy rate neared zero.

Swanson (2011) revisited the original "Operation Twist" begun in 1961. He noted that studies at the time were carried out with low frequency data with its heightened dangers of omitted variable risk and endogeneity. Performing an event study, he determined that Operation Twist had a statistically significant but moderate effect on longer-term Treasury yields. He then applied his results to QE2, finding, like Krishnamurthy and Vissing-Jorgensen (2011), that if the goal is to reduce private sector interest rates, then direct purchases of private sector instruments, such as mortgage-backed bonds, would be more effective.

Event studies reduce the risk of both omitted variable bias and endogeniety problems. High frequency data capture the immediate market reaction before external events affect results and feedback mechanisms come into play. That is particularly important with the complicated inter-connected relationships involved with large-scale asset purchases (LSAP) programs. However, the weaknesses of event study methodology are critical here. Giirkaynak and Wright (2013) provided a nice summary of event study technique and applications. They were very positive about the technique including its application to studies of QE but among the limitations they note is that "Event studies provide information on market participants' expectations, but not on actual outcomes" (p. 52). Many of the Federal Reserve actions were unprecedented in terms of the target securities and/or the size of the purchases. In addition, these were complicated programs and in some cases rolled out over a long period. It is likely that market participants would not be able to accurately predict the effects of these polices within minutes of their announcement. Indeed, economists are still attempting to measure their impact many years later despite much more certainty about the nature of the programs and retrospective data.

Event studies look at the immediate market reactions to policy actions but may miss longer term effects. With respect to LSAPs, Giirkaynak and Wright observed that "An important question in QE event study analysis (and indeed in event studies more broadly) is that it is hard to know the persistence of the effects" (p. 57). Another problem they noted is that for the later announcements, the Fed intentionally signaled the direction of their plans in advance. This is a problem for event analysis as the market reaction at the time of the announcement may be muted. Finally, the advantages of event studies are due to their use of high-frequency data. This is problematic when looking at wealth effects. As noted above, the key components of household wealth are financial assets and real estate. Event studies are well suited for looking at short-term financial market movements, but home values are reported in only low frequency data series. (3) In addition, to be able to separate the effects of an announcement, one must be able to control for prior expectations, and those data are lacking for home prices.

Mann and Klachkin (2015) provided a clever combination of low and high frequency data using auction-by-auction data for Treasuries. They found that the zero lower bound on the policy rate after 2008 significantly changed the correlations between key financial variables such as the federal funds rate, the auction high-yields on Treasury bonds, and the Standard & Poor's. Their results highlighted the need for further investigation of the effects of monetary policy on asset prices since those relationships appear to be altered under the current zero-lower-bound conditions.

This paper seeks to find the links between recent monetary policy and changes in the prices of equities and housing using lower frequency data. In the next section, a VAR model is developed to look for the links between Federal Reserve policy over the past eight years, and changes in asset prices.

VAR Model and Data

To capture the relationship between monetary policy and asset valuation, VAR is used to analyze the Federal Reserve's LSAP programs. VAR is well suited here because this is a situation with multiple endogenous variables, the lack of a sound structural model, and an unknown lag structure. The sample starts in September of 2008 with the dramatic credit market events of that month and the Fed's responses to those events. (4) The sample ends with March 2016, the last month with a complete set of data. (5) The traditional measure of monetary policy is the federal funds rate. However, from late 2008 to the present, the policy rate has been near zero and thus not useful for our purposes.

Other potential measures include the monetary base, money supply, and excess reserves. The various measures of money supply do not capture the effect, if any, of Fed activities that generated dramatic changes in bank reserves but little increase in Ml or M2. For money supply and excess reserves, we see the results of policy, but they are not themselves tools of monetary policy in the same way the federal funds rate has been. We need a measure that reflects the primary tools employed during this period, namely large scale open market purchases of mortgage-backed securities, federal agency debt and longer term Treasury bonds. These programs will be analyzed individually later in the paper.

We begin by using the monetary base (BOGMBASE) as a way to combine various Open Market Committee initiatives with a single gauge. The equity and housing variables are central to the study. Thus the S&P Case-Shiller 10-city home price index ('CSESHIL) and the real value of the S&P 500 Index (SP500R) are included. Capital markets are critical for both asset markets. Long-term Treasury and mortgage interest rates are logical candidates for inclusion. However, mortgage and Treasury rates are highly collinear with a correlation coefficient of just under 0.99 over the past 41 years and 0.90 since September of 2008. Given the key importance of the housing market and mortgage rate in this period, the 30-year conventional mortgage rate (MORTG) is retained. The civilian unemployment rate (UNEMP) is included as a measure of labor market and macroeconomic health. Since uncertainty shocks and risk factors were major drivers of financial markets during this period, we include the index of S&P 500 volatility (VIX). (6)



The full matrix of impulse response functions is not presented to save space. The results for monetary base shocks are presented in Fig. 1. The trends are as expected for all five impulse responses to a change in the monetary base: An increase in the monetary base increases stock values and housing prices. It reduces unemployment, volatility and the mortgage rate. (7) The effects on the S&P 500 VIX and unemployment are significant at the 5 % level though the effect on the VIX attenuates. The Case-Shiller index becomes significant only starting at the two-year mark. Mortgage rates fall but are not statistically significant. Using a VAR model but higher frequency data, Wright (2012, p. 464) finds that the LSAP reduced long-term interest rates initially but the effects were reversed in subsequent months. He offers two explanations: "the economic stimulus provided by these Federal Reserve actions caused the economy to pick up," which he labels as optimistic. "Another interpretation is that markets initially overreacted to the news of these quantitative easing actions" (p. 464).

Impulse response functions are good at providing direction and significance but offer little sense of the magnitude of the changes being observed. To explore the relative importance of Fed policy in affecting asset prices, a variance decomposition is performed with a 24-month forecast horizon. Results of the exercise are presented in Table 1. To conserve space only the results for the S&P 500 index and the Case-Shiller index are presented. In the early months, shocks to the SP500R's own equation error term dominate, but over the 24-month period the share of S&P 500 forecast error variance attributed to changes in the monetary base rises to over 40 %, far above the other factors. For housing prices, the role played by monetary base shocks is more modest. The proportion assigned to the monetary base still rises over time but at 24 months is just over 12 %. For the three variables not shown, monetary-base shocks claim the highest percent of forecast error variance for unemployment at 31.1 %. A peak of 16.7 % is reached for the monetary base share for the VTX at 21 months. Consistent with the insignificant impulse response, the monetary base share of forecast variance error for the mortgage rate is only 7.7 %.

Given the lack of a uniformly accepted measure of monetary policy while the federal funds rate sits at zero, the use of the monetary base in this study may be questioned. To address this, excess reserves are employed in the place of monetary base in the same VAR, generating Fig. 2. The pattern is very similar and the conclusions are identical. All variables move in the expected direction, with the stock market, unemployment rates and the VIX reliably significant.



Choosing M2 as the measure of monetary policy generates Fig. 3. The pattern looks somewhat different but the major conclusions remain. An increase in the money supply is seen to significantly increase the stock market and decrease the VIX and unemployment. The changes in housing prices become significantly positive over a shorter period.

Response of Equity and Housing Markets to Large Scale Asset Purchases

Changes in the monetary base were achieved in very different ways during this period as shown in Fig. 4. In QE1 in 2008 and 2009, the Federal Reserve purchased mortgage-backed securities and government agency debt in addition to traditional open market operations. (8) The QE2 in 2010 was primarily the purchase of Treasuries. 2011 and 2012 brought "Operation Twist" with the Fed buying long-term Treasuries and selling short-term Treasuries. In QE3 the Fed purchased more mortgage backed debt and longer term Treasuries.


To investigate the impact of purchasing novel assets, the amount of Federal agency debt securities held by the Federal Reserve (FEDDT), and mortgage-backed securities held by the Federal Reserve (MBST) are added to the VAR. Operation Twist changed the mix of Treasury bonds held by the Federal Reserve, thus Fed Treasury holdings with maturities greater than five years minus those with maturities less than five years (TWIST2) are included. Figure 5 displays the impulse response functions stemming from positive, one-standard-deviation shocks in the above variables. Only those graphs showing asset price response to Fed actions are included here.

Operation Twist and the purchase of mortgage-backed securities are seen to generate significant increases in equity prices. The purchase of federal agency debt generates a rising impulse response but the lower bound of the confidence interval never exceeds zero. Housing prices are most clearly affected by the purchase of mortgage-backed securities. This is consistent with the findings of Krishnamurthy and Vissing-Jorgensen (2011) and Swanson (2011) who found that direct purchases of mortgage backed securities were more effective than Treasury purchases in impacting the mortgage market. The responses to other variables are not significant.

Repeating the above variance decomposition exercise for specific Federal Reserve asset purchase initiatives produces the results in Table 2. Following the impulse response function results for equities, positive shocks to the errors of the (TWIST2) and MBST equations claim the largest share of the forecast error variance. Operation Twist reaches a maximum of 11.5 % in month 24. The proportion attributed to mortgage-backed securities peaks at 15.7 % in month nine. Of the three programs, mortgage-backed securities again show the largest proportion of forecast error variance for housing prices with a maximum of 12.2 % in month 10. The effects of Operation Twist and the purchase of agency debt never reach either a meaningful magnitude or statistical significance. For both markets, shocks to the monetary base capture the largest share at more than 20 %. (9)


Across all the analyses, positive monetary shocks are seen to boost equity prices. However, the LSAP analysis implies that the Fed's choice of which assets to purchase when expanding the monetary base is important. The purchase of federal agency debt has no statistically significant effect on any asset prices. This was the smallest of the programs and perhaps that lack of size limits one's ability to detect its import. Operation Twist has no significant impact on housing prices, but some efficacy in raising equity prices. The most consistently positive results for both equities and housing follow positive shocks in the form of mortgage-backed security purchases which can be seen in both the impulse response functions and variance decomposition.


Extraordinary Fed actions during the financial crisis have been questioned. Prior event studies have shown immediate responses of interest rates and equity prices to the Federal Reserve's large scale asset purchase programs. However, less is known about the longer-term responses of asset prices to LSAPs and in particular, the response of the housing market. Using low-frequency data and VAR estimation, this study finds that the expansionary monetary policy of the last she years has been quite effective.

Measuring monetary policy with the monetary base, excess reserves or M2 generates statistically significant increases in equity prices. Two LSAPs, Operation Twist and the purchase of mortgages-backed securities generated stock market increases. Indeed, with the exception of the purchase of federal agency debt, all of the measures of monetary expansion, in every sample period, show a positive and significant relationship with equity prices. The Fed's purchase of mortgage-backed securities was also efficacious in increasing housing prices. The ability of these programs to raise asset prices is a necessary, though not sufficient, condition for monetary policy to affect spending through increased wealth.

John H. Huston (1) * Roger W. Spencer (1)

[mail] John H. Huston

(1) Department of Economics, Trinity University, One Trinity Place, San Antonio, TX 78212, USA

Published online: 27 October 2016

DOI 10.1007/s11293-016-9511-9

(1) Survey of Consumer Finances Federal Reserve Board of Governors Tables 5 and 8 (2010) and authors' calculations.

(2) "Martin, U.S. News and World Report, Feb. 11, 1955, p. 60. Also, Spencer and Huston (2006), p. 72-73.

(3) Real estate investment trust (REIT) indexes provide instant reaction, but primarily cover commercial real-estate and not the single-family home market which was the target of these LSAPs. In addition, REIT's indexes capture price movements of REIT securities and not the underlying real estate.

(4) An argument can be made that the crisis began earlier in the year as during that period the Fed took some preliminary actions including lowering the federal funds rate, and the subprime market was in distress. Extending the sample to January of 2008 leads to the same pattern of impulse responses. However, the sup Wald statistic (Quandt Likelihood Ratio) identified September of that year as the optimal structural break. (See Canova 2007, p 116 for a discussion of this technique in the VAR context.) The same technique found no more breaks within the "trimmed" post 2008 period.

(5) One can make a case for ending the sample in October of 2014 when the Fed announced the phasing out of QE3. That too has minimal effect on the results, though the housing response gains significance soon after a monetary shock.

(6) The Schwarz Information Criterion(SIC) and Hannan-Quinn (HQ) lag length selection criteria suggest a lag of two months. The Akaike Information Criterion (AIC) recommends a lag of three months, but the AIC is known for overestimating lag length (for example, see Kapetanios (2001)). Given the small n, the more parsimonious model suggested by SIC and HQ is chosen. Using a longer lag structure leaves the pattern of the results intact. The VAR stability condition is met with the inverse roots of the characteristic equation all within the unit circle. (Note that these are inverse roots.) VAR stability implies stationarity. See Liitkepohl (2013, p 19-21) for a full description of this test.

(7) The impulse response functions were generally stable with respect to the Cholesky ordering of the variables. The directions of change in response to the monetary base were the same for all orderings. The significance was generally preserved with equities being significant in all orderings, housing values approaching significance only at the end of the period. Unemployment sometimes reached significance slightly earlier or later. VIX was always significant early and sometimes marginally regained that significance during the second year. Mortgage rates had the expected direction but only flirted with significance late in the forecast. Extending the sample back to January of 2008 gives the same pattern for all variables though the housing market's response becomes significant in month 19 and the mortgage rate is marginally significant from months 11 to 14.

(8) Fawley and Neely (2013) provided a nice summary of the activities of the central banks of the U.S., England, Japan and the EMU.

(9) Note, however, that designing the VAR with the monetary base included along with the individual programs potentially diminishes the impact of these programs since they also could work through raising the monetary base. If one repeats the experiment without the monetary base in the VAR the purchase of mortgage-backed securities gains a much larger share of the forecast error variance. It reaches 27.8 % share in month 17 for equities and 34.2 % in month 22 for housing prices. The other programs' results are less affected.


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Table 1 Monetary base forecast error variance decomposition for
asset prices

Period   S.E.         SP500R       CSESHIL     UNEMP

Variance Decomposition of SP500R:

1         45.31203    100.0000      0.000000    0.000000
2         64.17240     92.52639     0.003659    0.373011
3         75.19896     83.37823     0.373969    0.789921
4         82.50261     75.55928     1.671409    0.919123
5         88.14041     68.97302     3.981193    0.855040
6         93.05728     63.22132     6.997026    0.767662
7         97.65186     58.08912    10.27339     0.740748
8        102.0631      53.51614    13.42276     0.800719
12       117.7968      40.51922    21.47455     1.956863
16       130.0647      33.71952    22.51498     4.031311
20       140.7103      30.50946    20.16428     5.953686
24       151.9807      29.67172    17.33932     7.002662

Variance Decomposition of CSESHIL:

1          0.453244     0.003743   99.99626     0.000000
2          0.934469     0.007905   99.94441     0.005942
3          1.444927     0.020427   99.54738     0.052037
4          1.975651     0.213304   98.43876     0.152390
5          2.521932     0.695677   96.80105     0.305449
6          3.074515     1.423921   94.94533     0.520372
7          3.621884     2.272303   93.07457     0.810449
8          4.152842     3.115477   91.28554     1.182969
12         5.955402     5.232799   85.24331     3.431678
16         7.131695     5.187702   80.05918     6.549327
20         7.848961     4.403023   74.16154     9.863869
24         8.403113     4.704631   66.71097    12.41314

Period   MORTG        VIX          BOGMBASE

Variance Decomposition of SP500R:

1        0.000000     0.000000      0.000000
2        0.076560     0.591965      6.428411
3        0.534240     0.655848     14.26779
4        1.537177     0.551679     19.76133
5        2.756933     0.487750     22.94606
6        3.792693     0.438831     24.78247
7        4.503620     0.400591     25.99253
8        4.933062     0.379140     26.94818
12       5.282775     0.387694     30.37891
16       5.026895     0.384562     34.32273
20       4.717372     0.334732     38.32047
24       4.385762     0.315818     41.28472

Variance Decomposition of CSESHIL:

1        0.000000     0.000000      0.000000
2        0.001817     0.004588      0.035335
3        0.073368     0.088519      0.218273
4        0.205864     0.460929      0.528753
5        0.358521     0.961682      0.877619
6        0.518885     1.395922      1.195574
7        0.678721     1.703815      1.460144
8        0.827426     1.905088      1.683505
12       1.233263     2.204462      2.654488
16       1.481318     2.198542      4.523933
20       1.732758     2.041944      7.796861
24       1.995782     1.809430     12.36605

Data for the VAR come from the Federal Reserve Bank of St. Louis

Table 2 LSAP forecast error variance decomposition for equity and
housing prices

Period   SP500R       CSESHIL     UNEMP       MORTG       VIX

Variance Decomposition of SP500R

1        100.0000      0.000000    0.000000    0.000000   0.000000
2         87.98881     0.004076    0.709818    0.046089   1.938104
3         78.23161     0.034571    0.559531    0.036301   2.530839
4         71.46507     0.175146    0.854676    0.114082   2.382986
5         66.08023     0.293261    1.376630    0.568708   2.303767
6         61.71259     0.373885    1.683527    1.435245   2.733485
7         58.15589     0.493301    1.789551    2.400668   3.741255
8         55.47789     0.774415    1.845121    3.231725   4.550845
9         53.42442     1.369477    1.943991    3.870490   4.760396
10        51.44810     2.373633    2.115148    4.309743   4.604422
11        49.21014     3.750082    2.342969    4.547750   4.348718
12        46.69769     5.332911    2.606868    4.607457   4.091105
16        36.97908     9.972807    4.105395    4.087574   3.348873
20        30.94847    10.23527     6.912613    3.620178   3.104942
24        27.27626     9.124894   11.14281     3.228992   2.827965

Variance Decomposition of CSESHIL

1          0.780604   99.21940     0.000000    0.000000   0.000000
2          0.536434   96.91388     0.013031    0.256932   0.050772
3          0.345364   92.59042     0.112075    0.809630   0.410383
4          0.294406   87.20907     0.213474    1.571067   1.256463
5          0.397905   81.53370     0.298136    2.523995   2.346081
6          0.702863   76.21543     0.386184    3.627490   3.295709
7          1.206376   71.61816     0.498600    4.811150   3.887619
8          1.821880   67.83506     0.648864    5.988147   4.110968
9          2.420425   64.80017     0.843675    7.075882   4.061261
10         2.890529   62.37933     1.085982    8.014444   3.849497
11         3.172957   60.41935     1.377623    8.774321   3.562793
12         3.264195   58.77262     1.720451    9.353216   3.258589
16         2.634757   53.19578     3.634241   10.28774    2.316076
20         2.218576   47.46429     6.406991   10.12989    1.862522
24         2.315014   42.27952     9.753746    9.591911   1.644512

Period   BOGMBASE     FEDDT       TWIST2      MBST

Variance Decomposition of SP500R

1         0.000000    0.000000     0.000000    0.000000
2         6.761377    0.728727     1.659052    0.163946
3        10.63024     1.552889     4.429647    1.994370
4        10.67449     1.937548     6.577464    5.818533
5         9.920212    1.979295     7.533422    9.944478
6         9.284247    1.903009     7.811473   13.06253
7         8.864241    1.803112     7.861069   14.89091
8         8.913715    1.702275     7.848977   15.65504
9         9.524509    1.605285     7.793886   15.70754
10       10.58922     1.511711     7.691050   15.35697
11       11.97447     1.422584     7.577429   14.82586
12       13.56492     1.339111     7.511778   14.24816
16       19.56254     1.061681     8.365424   12.51662
20       22.50673     0.920613    10.09737    11.65381
24       23.37049     0.991833    11.47078    10.56597

Variance Decomposition of CSESHIL

1         0.000000    0.000000     0.000000    0.000000
2         0.361923    0.164098     0.408893    1.294034
3         0.878974    0.406217     0.871226    3.575709
4         1.514932    0.626065     1.287721    6.026799
5         2.296803    0.786345     1.619313    8.197724
6         3.195398    0.872301     1.821960    9.882663
7         4.159646    0.888657     1.883490   11.04631
8         5.160233    0.853785     1.831338   11.74973
9         6.199862    0.789928     1.712699   12.09610
10        7.297769    0.715436     1.571142   12.19587
11        8.469609    0.641919     1.435218   12.14621
12        9.714985    0.575126     1.318871   12.02195
16       14.89715     0.396753     1.092294   11.54521
20       18.93227     0.354347     1.192148   11.43897
24       21.17640     0.423538     1.540300   11.27505

Data for the VAR come from the Federal Reserve Bank of St. Louis
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Author:Huston, John H.; Spencer, Roger W.
Publication:Atlantic Economic Journal
Date:Dec 1, 2016
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