Identifying the Interdependence Between Us Monetary Policy and the Stock Market

Bjørnland, Hilde C.; Leitemo, Kai Working Paper Identifying the Interdependence Between Us Monetary Policy and the Stock Market Working Paper, No. 2008/4 Provided in Cooperation with: Norges Bank, Oslo Suggested Citation: Bjørnland, Hilde C.; Leitemo, Kai (2008) : Identifying the Interdependence Between Us Monetary Policy and the Stock Market, Working Paper, No. 2008/4, ISBN 978-82-7553-431-4, Norges Bank, Oslo, https://hdl.handle.net/11250/2497782 This Version is available at: https://hdl.handle.net/10419/209895 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Bjørnland and Kai Leitemo ISSN 0801-2504 (trykt) 1502-8143 (online) ISBN 978-82-7553-430-7 (printed), 978-82-7553-431-4 (online) Working papers from Norges Bank can be ordered by e-mail: [email protected] or from Norges Bank, Subscription service, P.O.Box. 1179 Sentrum N-0107Oslo, Norway. Tel. +47 22 31 63 83, Fax. +47 22 41 31 05 Working papers from 1999 onwards are available as pdf-files on the bank’s web site: www.norges-bank.no, under “Publications”. Norges Bank’s working papers present research projects and reports (not usually in their final form) and are intended inter alia to enable the author to benefit from the comments of colleagues and other interested parties. Views and conclusions expressed in working papers are the responsibility of the authors alone. Working papers fra Norges Bank kan bestilles over e-post: tjenestetor[email protected] eller ved henvendelse til: Norges Bank, Abonnementsservice Postboks 1179 Sentrum 0107 Oslo Telefon 22 31 63 83, Telefaks 22 41 31 05 Fra 1999 og senere er publikasjonene tilgjengelige som pdf-filer på www.norges-bank.no, under “Publikasjoner”. Working papers inneholder forskningsarbeider og utredninger som vanligvis ikke har fått sin endelige form. Hensikten er blant annet at forfatteren kan motta kommentarer fra kolleger og andre interesserte. Synspunkter og konklusjoner i arbeidene står for forfatternes regning. Identifying the Interdependence between US Monetary Policy and the Stock Market∗ Hilde C. Bjørnland‡ Norwegian School of Management (BI) and Norges Bank and Kai Leitemo Norwegian School of Management (BI) and Bank of Finland March 2008 Abstract We estimate the interdependence between US monetary policy and the S&P 500 using structural VAR methodology. A solution is proposed to the simultaneity problem of identifying monetary and stock price shocks by using a combination of short-run and long-run restrictions that maintains the qualitative properties of a monetary policy shock found in the established literature (Christiano et al., 1999). We find great interdependence between interest rate setting and real stock prices. Real stock prices immediately fall by 7-9 percent due to a monetary policy shock that raises the federal funds rate by 100 basis points. A stock price shock increasing real stock prices by one percent leads to an increase in the interest rate of close to 4 basis points. Keywords: VAR, monetary policy, asset prices, identification. JEL-codes: E61, E52, E43. ∗ We thank the Editor Martin Eichenbaum, two anonymous referees, Ida Wolden Bache, Petra Geraats, Bruno Gerard, Steinar Holden, Jan Tore Klovland, Jesper Lindé, Roberto Rigobon, Erling Steigum, Kjetil Storesletten, Øystein Thøgersen, Karl Walentin and seminar participants at Econometric Society World Congress 2005, Annual Meeting of Norwegian Economists 2005, Cambridge University, Norwegian School of Economics and Business Administration, Norwegian School of Management BI and University of Oslo for valuable comments and suggestions. We gratefully acknowledge financial support from the Norwegian Financial Market Fund under the Norwegian Research Council. The usual disclaimer applies. The views expressed in this paper are those of the authors and should not be attributed to Norges Bank and Bank of Finland. ‡ Addresses of the authors: Bjørnland (corresponding author): Norwegian School of Management BI, Nydalsveien 37, N-0442 Oslo, Norway. E-mail address: [email protected]. Leitemo: Norwegian School of Management BI, E-mail: [email protected] 1 1. Introduction It is commonly accepted that monetary policy influences private-sector decision-making. If prices are not fully flexible in the short run, as assumed by the New Keynesian theory framework, the central bank can temporarily influence the real interest rate and therefore have an effect on real output in addition to nominal prices. It is commonly believed that the central banks have some objectives for their exertion of control over the real interest rates, e.g. to have low and stable inflation and production close to the natural rate. In order to best fulfill these objectives, the central bank needs to monitor, respond to and influence private sector decisions appropriately. The central bank and the private sector will thus both affect and be affected by the other, leading to considerable interdependence between the two sectors. For the financial markets where information is readily available and prices are sensitive to agents’ expectations about the future, we would expect that a large part of the interdependence to be simultaneous. Allowing for simultaneity between monetary policy and financial markets is therefore likely to be both quantitatively and qualitatively important when measuring the degree of interdependence. The aim of this paper is to explore just how important this is. Analyses of the effects of monetary policy have to a large extent been addressed in terms of vector autoregressive (VAR) models, initiated by Sims (1980). Yet, studies that use VAR models to identify the interdependence have found only small effects of interaction between monetary policy and asset prices, see for instance Lee (1992), Thorbecke (1997) and Neri (2004) among others. However, these conventional VAR studies have not allowed for simultaneous interdependence, as the structural shocks have been recovered using recursive, short-run restrictions on the interaction between monetary policy and asset prices. In this study we analyze the interaction between asset prices and monetary policy in the U.S., represented by the S&P 500 and the federal funds rate respectively, using a VAR model that takes full account of the potential simultaneity of interdependence. We solve the simultaneity problem by imposing a combination of short-run and long-run restrictions on the 2 multipliers of the shocks, leaving the contemporaneous relationship between the interest rate and real stock prices intact. Identification is instead achieved by assuming monetary policy can have no long-run effect on the real stock price, which is a common long-run neutrality assumption. By using only one long-run restriction, we address the simultaneity problem without extensively deviating from the established literature (i.e., Christiano et al., 1999, 2005) of identifying a monetary policy shock. Contrary to what is found in previous studies, we find strong interaction effects between the stock market and interest rate setting. A considerable part of the interaction is simultaneous. These results are achieved without much affecting the conventional view on how monetary policy affects macroeconomic variables, previously found in the VAR literature. Section 2 gives a brief survey of theoretical, methodological and empirical arguments regarding the interaction between asset prices and monetary policy. Section 3 presents the identification scheme used for the VAR study in identifying the interdependence between the monetary policy and the stock market. Section 4 presents and discusses our empirical results, including issues pertaining to robustness. Section 5 concludes. 2. Monetary policy and stock prices interaction: a short overview Economic theory suggests several reasons why there should be interaction effects between monetary policy and asset prices, in particular, stock prices. Through its effect on both the current and the expected future real interest rate, the central bank influences the timing of both household consumption and business investment decisions. It is commonly assumed that asset prices and, in particular, stock prices are determined in a forward-looking manner, thereby reflecting the private sector expected future discounted sum of return on the assets. Changes in asset prices can then either be due to changes in expected future dividends, the expected future interest rate that serves as a discount rate, or changes in the stock returns premium. If goods markets are dominated by monopolistic competition and mark-up pricing, 3 profits will, at least in the short run, be affected by all factors influencing aggregate demand. Moreover, the change in the path of profit may influence the expected dividends. Monetary policy, and in particular surprise policy moves, is therefore not only likely to influence stock prices through the interest rate (discount) channel, but also indirectly through its influence on the determinants of dividends and the stock returns premium by influencing the degree of uncertainty faced by agents. Asset prices may influence consumption through a wealth channel and investments through the Tobin Q effect and, moreover, increase a firm’s ability to fund operations (credit channel). Furthermore asset prices may include relevant information that is not available elsewhere. The monetary policymaker that manages aggregate demand in an effort to control inflation and output thus has incentives to monitor asset prices in general, and stock prices in particular, and use them as short run indicators for the appropriate stance of monetary policy.1 Therefore, there is likely to be considerable interdependence between stock price formation and monetary policymaking.2 Empirical modelers should thus be open to the potential influence of asset prices on monetary policymaking. 2.1 Empirical evidence Compared to the vast amount of papers analyzing the influence of the monetary policy actions on the macroeconomic environment, there are relatively few papers trying to model interactions between monetary policy and asset prices. Early attempts, like Geske and Roll (1983) and Kaul (1987), examine the causal chain between monetary policy and stock market returns separately (see Sellin (2001) for a comprehensive survey). More recently, empirical studies have tended to use a joint estimation scheme like the vector autoregressive (VAR) 1 See Vickers (2000) for overview of the use of asset prices in monetary policy in inflation-targeting countries. 2 The form of interaction is further complicated by issues of whether asset prices should be included in the central bank loss function (see e.g. Bernanke and Gertler, 1999 and Carlstrom and Fuerst , 2001), on how to use asset price information efficiently and whether assets prices convey information that is not available elsewhere (e.g., Faia and Monacelli, 2008), whether the credit channel is important (see Bernanke, Gertler and Gilchrist, 2000, and Bernanke and Gertler, 1989) and whether asset prices include expectations-driven sunspot components that may influence target variables more than what is reflected by the fundamental part of the asset price (see e.g. Cecchetti et al., 2000, and Bernanke and Gertler, 2001). 4 approach, since it involves the joint interaction of all variables, see e.g. Lee (1992), Patelis (1997), Thorbecke (1997), Millard and Wells (2003) and Neri (2004) among others. All these find monetary policy shocks to account for only a small part of the variations in stock returns. Furthermore, stock prices frequently display a puzzling development which is difficult to understand from the perspective of financial market theory. More importantly, the above papers identify monetary policy and stock market shocks using the Cholesky decomposition, imposing a recursive ordering of the identified shocks. In many of these papers, stock prices are ordered last, thus implying that it can react contemporaneously to all other shocks, but that the variables identified before the stock market (i.e. monetary policy stance) react with a lag to stock market news. Hence, simultaneous interdependence is ruled out by assumption. Lastrapes (1998) and Rapach (2001) identify instead monetary shocks in a VAR model using solely long-run (neutrality) restrictions. Both find considerably stronger effects of the monetary shock on the stock market. However, the reverse causation; from the stock market to systematic monetary policy is either ignored or addressed more rudimentarily.3 Recently, the simultaneity problem has been addressed using high frequency observation (i.e., daily data), to analyze how asset prices are associated with particular policy actions in the short run. In an influential paper, Rigobon and Sack (2004) use an identification technique based on the heteroscedasticity of shocks that is present in high frequency data to analyze the impact effect of monetary policy on the stock marked. They find that following a surprise interest rate increase, stock prices decline significantly. Furthermore, using the same method, but analyzing the reverse causation, Rigobon and Sack (2003) find that stock market movements have a significant impact on short term interest rates, driving them in the same direction as the change in stock price. These results are somewhat stronger than results found in more conventional “event studies” like Bernanke and Kuttner (2005). 3 Another strand of literature estimates the contribution of asset prices in (Taylor type) interest rate reaction functions (i.e. Chadha et al. (2003)) but is subject to the same simultaneity problem as in the conventional VARs (see Rigobon and Sack (2003) for a more critical review). 11 interpretations of this result should be made with care, a potential explanation might be that as the interest rate gradually falls, the discounted value of expected future dividends increases while output and profits build up, leading to a normalization of real stock prices. The lower panels of Figure 2 show that a positive stock price shock increases both inflation and output in the short run. This is consistent with the view that the rise in real stock prices increases consumption through a wealth effect and investment through a Tobin Q effect, thus affecting aggregate demand. Due to nominal rigidities, prices react slowly and inflation rises in the intermediate run. The increase in inflation may, however, also be partly driven by the increase in the interest rate itself due to the initial price puzzle in the model. In any case, the response of the interest rate is consistent with an inflation-targeting central bank raising interest rates to curb the inflationary effects of increased aggregate demand. Stock price shocks are important indicators for the interest rate setting. A shock that increases real stock prices by one percent causes the interest rates to increase immediately by just less than four basis points, increasing to seven basis points within a year. By increasing the interest rate, the FOMC achieves the reduction in aggregate demand through the usual interest rate channels and reducing the positive impact on real stock prices. Again, our results are very much in line with studies that focus on short run responses (i.e., Rigobon and Sack, 2003),8 but larger than those found in the traditional VAR analysis. How can we interpret the stock price shock? Under the “news” interpretation, the shock contains information about the future that is not yet incorporated in current macroeconomic variables leading to a delayed but permanent change in productivity (see Beaudry and Portier, 2006). If the shock is non-fundamental (sunspot), the innovation in real stock prices is driven purely by expectations with no permanent effects on output caused by changes in technology. There may still, however, be short-run responses to output due to 8 Rigobon and Sack (2003) find that a “5 percent rise in stock prices over a day causes the probability of a 25 basis point interest rate hike to increase by a half, while a similar-sized movement over a week has a slightly larger effect on anticipated policy actions.” Similar findings are also found in Furlanetto (2008), although when focusing on the very recent time, he finds the response to have declined somewhat. 12 wealth effects on aggregate demand. Under both of these interpretations, the shock may contain vital information to the central bank for reasons outlined in Section 2. From Figure 3, we see that the stock market shock has only a temporary effect on output. Given, however, that we are including the output gap in the VAR, we have already effectively removed the long run trend and no permanent effects are possible. Hence we cannot draw any conclusions. In the appendix, however, we test robustness to other specifications, including measuring output in first differences (thereby allowing for a potential long run impact of shocks). The dynamic effects of the shock on output remain similar and there is no long-run effect on output. We therefore cautiously conclude that our results are consistent with the stock price shock being a sunspot shock rather than an anticipated technology shock. In this respect, our results differ from that of Beaudry and Portier. We note, however, that the confidence bands are wide and we are cautious in making any strong conclusions about the nature of the shock. The issue of as to what drives the stock market deserves further research. One objection to our identification of the stock price shock is that the shock could have an immediate effect on other variables like production and consumption (i.e., Jaimovich and Rebelo, 2006). This was ruled out by our identification scheme. However, it can be argued that it is not unlikely that the greater part of consumer prices together with consumption and investment decisions are subject to implementation lags of length similar to the model’s monthly frequency (see, Woodford, 2003, and Svensson and Woodford (2005), for arguments).9 The results reported so far suggest a great interdependence between the effects of the shocks.10 How is it possible to reconcile the zero interdependence found using the Cholesky decomposition above, with that of large interdependence found in the present structural 9 We have experimented with an alternative identification where output is ordered below real stock prices (i.e., allowing for the immediate impact of the stock price shock on output, but restricting real stock prices from responding on impact to output shocks instead). We find no significant impact effects on output of a stock price shock, taking this as support of our assumption about implementation lags in output. 10 The error variance decomposition (not reported, but can be obtained at request) suggests that the monetary and stock price shocks together account for almost all variation in the federal funds rate and stock prices on impact, leaving the other shocks to influence these variables only in the longer run. model? To see this, assume for simplicity a system in two variables, the interest rate (it) and the real stock price (st). The reduced form residuals will be linear relationship of the structural orthogonal shocks, that is, the monetary policy shock and the stock price shock, SP t MP tts SP t MP tti u u εβε αεε += += , , with covariance given by [ ] 22 ,, ))((),cov( SPMP SP t MP t SP t MP ttsti Euu αωβωεβεαεε +=++= . Hence, a covariance close to zero either implies that the interdependence is zero, , implying 0),cov( ,, = tsti uu 0 = = β α (as imposed using the Cholesky decomposition), or that the effects are opposite in signs and cancel out ( ) 2 2 SP MP ω ω β α =− . Only the structural identification scheme suggested here allows the latter to be the case. 4.3 Robustness We study the robustness of the results by using plausible alternative models. We first study alternative monthly specifications of the model, varying sample period, lags, allowing for dummies (for specific events like the stock crashes in 1987 and 2001), using different transformations of the variables (first differences, Hodrick Prescott filter, no detrending etc) or changing the order of the variables. We then estimate the same model using quarterly data, allowing us to substitute industrial production with GDP, as well as expanding the dimension of the model by including consumption and investment. All the results are presented and discussed in more detail in the appendix. We find the results to remain robust to all of these variations. Regarding the monthly specifications, the baseline model has about the average response across the models. There may be some evidence that the impact effect of both shocks have decreased somewhat over 13 14 time, although these results will depend on the specific VAR model specified. The responses are marginally smaller in the quarterly model, but also there robust to various transformations. 5. Conclusion We find that there is a substantial simultaneous interaction between interest rate setting in the US and shocks to real stock prices. Just as monetary policy is important for the determination of stock prices, the stock market is an important source of information for the conduct of monetary policy. This result is found in many plausible and alternative model specifications that allows for the possibility of simultaneous interaction. 15 References Beaudry, Paul and Franck Portier (2006), “Stock Prices, News and Economic Fluctuations”, American Economic Review 96(4), pp. 1293-1307. Bernanke, Ben S. and Mark Gertler (1989), “Agency Costs, Net Worth, and Business Fluctuations,” American Economic Review 79 (1), pp. 14-31. Bernanke, Ben S. and Mark Gertler (1999), “Monetary Policy and Asset Volatility,” Federal Reserve Bank of Kansas City Economic Review, Fourth Quarter 1999, 84(4), pp. 17-51. Bernanke, Ben S. and Mark Gertler (2001), “Should Central Banks Respond to Movements in Asset Prices?” American Economic Review, May 2001, 91(2), pp. 253-57. Bernanke, Ben S., Mark Gertler and Simon Gilchrist (2000), “The Financial Accelerator in a Quantitative Business Cycle Framework,” in John B. Taylor and Michael Woodford, eds., Handbook of Macroeconomics. Volume 1C. 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Woodford, Michael (2003), Interest and Prices, Princeton and Oxford: Princeton University Press. 18 Appendix In this appendix we study the robustness of the result that there is strong simultaneous interaction between the stock market and monetary policy by using plausible alternative models. In the first section we study the robustness properties with respect to alternative monthly specifications of the model. We then estimate the same model using quarterly data, allowing us to substitute industrial production with GDP, as well as expanding the dimension of the model by including consumption and investment. Robustness to alternative monthly specifications In checking for robustness of our findings, it is important to establish whether the strong interdependence found is driven by a few extreme events of strong and simultaneous responses between stock prices and monetary policy. Throughout the period examined, there have been a few periods were the stock market fell severely (without the fundamentals changing significantly) while, at the same time, monetary policy became accommodating to counteract the negative effects of the stock market fall. The stock market crash in October 1987 is one example and the September 11, 2001 terror attack is another. Furthermore, it is important to establish whether our results have changed in the period starting in 1987 from which Alan Greenspan took office. Regarding model specification, is the choice of lag length in the VAR model important for our results? Further, will the results prevail if the variables in the VAR are specified differently (i.e. taking first differences of the variables, using a Hodrick Prescott filter to de-trend inflation and GDP, no-detrending etc.) and finally, are the results robust to alternative ordering of the variables? To investigate the robustness of our results along these dimensions, the upper panel of Figure A1 reports the impulse responses of a normalized monetary policy shock (that increases the interest rate with 100 basis points) on stock prices when the baseline VAR is reestimated (i) with two dummies for the suggested stock price collapses (Dummy) (ii) using 19 more recent time, i.e. the Greenspan period 1987M1 to 2002M12 (1987) (iii) with 6 instead of 4 lags (6 lags) (iv) first differencing all variables but the interest rate (First differences) (v) using Hodrick Prescott (HP) filter to detrend inflation and output (HP trend) (vi) using a linear trend to de-trend output and inflation (Linear trend), and, finally, (vii) using an alternative order of the first four variables in the VAR. That is, we order output below the real stock price, i.e. we allow for an immediate impact of the stock price shock on output, but restricting instead real stock prices from responding on impact to output shocks (Order). The lower panel of Figure A1 reports the effect of a normalized stock price shock (that increases stock prices by one percent) on the federal funds rate to the same robustness tests. Starting with the top panel, we see that across the models, there is a substantial and immediate reduction in stock prices due to the monetary policy shock. The baseline model has about the average response across the models. In particular, removing the first part of the sample, re-estimating with a dummy for major events or using first differences, all reduce the impact; whereas alternative de-trending or using more lags increase the impact. All models suggest that real stock prices return to the steady state at approximately the same speed. Finally, note that the impulse responses using an alternative order remains indistinguishable from baseline, as the effect of the monetary policy shock on stock prices remains exactly identical. The results allows for a generalizing of Christiano et al. (1999; Proposition 4.1) to also include a variable that is identified using a (zero) long run restriction.11 Turning to the response of a stock price shock, the lower panel emphasizes that there is a robust picture with respect to how the federal funds rate reacts to the stock price shock. The baseline model has about the average response across the models. Again, removing the first part of the sample, re-estimating with a dummy for major event or using first differences 11 Christiano et al. (1999; Proposition 4.1) states that using a Cholesky decomposition with the monetary policy variable (the interest rate) ordered last, the responses to the monetary policy shock will be invariant to the ordering of the variables above the interest rate. The real bite here is the assumption that the variables in the VAR don't respond contemporaneously to a monetary policy shock. Figure A1. Impulse responses under alternative monthly model specifications to a monetary policy shock (upper panel) and a stock price shock (lower panel). 1 5 9 131721252933374145495357616569 −16 −14 −12 −10 −8 −6 −4 −2 0 Percent Months Baseline Dummy 1987 6 lags Linear trend HP trend First differences Order Stock prices Federal funds ra te 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 −0,02 0 0,02 0,04 0,06 0,08 0,1 Percentage points Months Note: The upper panel shows the impulse response of real stock prices to a normalized monetary policy shock that increases the nominal interest rate on impact by one percentage point. The lower panel shows the impulse response of the federal funds rate to a normalized stock price shock that increases stock prices by one percent. See the main text for an explanation of the different monthly specifications. reduce the immediate response somewhat; whereas more lags and alternative de-trending increase the response. Using an alternative order of the variables reduce the impact somewhat Hence, we are confident in reporting that all models suggest that the interaction is quantitatively important.12 There may be some evidence that the impact effect of both shocks 20 12 Several other model specifications were also tried out. For instance, specifying all variables in levels or adding a trend to the VAR increased the impact somewhat. However, these responses are not reported as we believe this to yield an improper representation of data. We also tested robustness to substituting some of the variables with plausible alternatives in the VAR. We found that this did not change the results much. The most effect was when we included oil prices instead of a commodity price index in the VAR, which magnified all the results. 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Farooq Akram, Gunnar Bårdsen and Kjersti-Gro Lindquist Pursuing financial stability under an inflation-targeting regime Research Department, 29 p 2006/9 Yuliya Demyanyk, Charlotte Ostergaard and Bent E. Sørensen U.S. banking deregulation, small businesses, and interstate insurance of personal income Research Department, 57 p 2006/10 Q. Farooq Akram, Yakov Ben-Haim and Øyvind Eitrheim Managing uncertainty through robust-satisficing monetary policy Research Department, 33 p 2006/11 Gisle James Natvik: Government spending and the Taylor pinciple Research Department, 41 p 2006/12 Kjell Bjørn Nordal: Banks’ optimal implementation strategies for a risk sensitive regulatory capital rule: a real options and signalling approach Research Department, 36 p 2006/13 Q. Farooq Akram and Ragnar Nymoen Model selection for monetary policy analysis – importance of empirical validity Research Department, 37 p 2007/1 Steinar Holden and Fredrik Wulfsberg Are real wages rigid downwards? Research Department, 44 p 2007/2 Dagfinn Rime, Lucio Sarno and Elvira Sojli Exchange rate forecasting, order flow and macroeconomic information Research Department, 43 p 2007/3 Lorán Chollete, Randi Næs and Johannes A. Skjeltorp What captures liquidity risk? A comparison of trade and order based liquidity factors Research Department, 45 p 2007/4 Moshe Kim, Eirik Gaard Kristiansen and Bent Vale Life-cycle patterns of interest rate markups in small firm finance Research Department, 42 p 2007/5 Francesco Furlanetto and Martin Seneca Rule-of-thumb consumers, productivity and hours Research Department, 41 p 2007/6 Yakov Ben-Haim, Q. Farooq Akram and Øyvind Eitrheim Monetary policy under uncertainty: Min-max vs robust-satisficing strategies Research Department, 28 p 2007/7 Carl Andreas Claussen and Øistein Røisland Aggregating judgments on dependent variables: an (im)possibility result 28 Research Department, 17 p 2007/8 Randi Næs, Johannes Skjeltorp og Bernt Arne Ødegaard Hvilke faktorer driver kursutviklingen på Oslo Børs? Forskningsavdelingen, 68 s 2007/9 Knut Are Astveit and Tørres G. Trovik Nowcasting Norwegian GDP: The role of asset prices in a small open economy Research Department, 29 p 2007/10 Hilde C. Bjørnland, Kai Leitemo and Junior Maih Estimating the natural rates in a simple new Keynesian framework Economics Department, 33 p 2007/11 Randi Næs and Bernt Arne Ødegaard Liquidity and asset pricing: Evidence on the role of investor holding period Research Department, 31 p 2007/12 Ida Wolden Bache Assessing estimates of the exchange rate pass-through Research Department, 60 p 2007/13 Q. Farooq Akram What horizon for targeting inflation? Research Department, 45 p 2007/14 Q. Farooq Akram, Yakov Ben-Haim and Øyvind Eitrheim Robust-satisficing monetary policy under parameter uncertainty Research Depatrment, 33 p 2007/15 Ida Wolden Bache and Bjørn E. Naug Estimating New Keynesian import price models Research Department, 40 p 2008/1 Anne Sofie Jore, James Mitchell and Shaun P. Vahey Combining forecast densities from VARs with uncertain instabilities Economics Department, 26 p 2008/2 Henrik Andersen Failure prediction of Norwegian banks: A logit approach Financial Markets Department, 49 p 2008/3 Lorán Chollete, Randi Næs and Johannes A. Skjeltorp The risk components of liquidity Research Department, 28 p 2008/4 Hilde C. Bjørnland and Kai Leitemo Identifying the interdependence between US monetary policy and the stock market Economics Department, 28 p Hilde C. Bjørnland and Kai Leitemo: Identifying the interdependence between US monetary policy and the stock market Working Paper 2008/4 KEYWORDS: VAR Monetary policy Asset prices Identification - 44696