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Really uncertain business cycles. (2013). Terry, Stephen ; Saporta Eksten, Itay ; Jaimovich, Nir ; Flötotto, Max ; bloom, nicholas ; Saporta-Eksten, Itay ; Floetotto, Max .
In: LSE Research Online Documents on Economics.
RePEc:ehl:lserod:51526.

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    In: ifo Beiträge zur Wirtschaftsforschung.
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  2. Interest rate hysteresis in macroeconomic investment under uncertainty. (2019). Göcke, Matthias ; Belke, Ansgar ; Gocke, Matthias.
    In: Ruhr Economic Papers.
    RePEc:zbw:rwirep:801.

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  3. Interest Rate Hysteresis in Macroeconomic Investment under Uncertainty. (2019). Göcke, Matthias ; Belke, Ansgar ; Gocke, Matthias.
    In: GLO Discussion Paper Series.
    RePEc:zbw:glodps:377.

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  4. Interest Rate Hysteresis in Macroeconomic Investment under Uncertainty. (2019). Göcke, Matthias ; Belke, Ansgar ; Gocke, Matthias.
    In: ROME Working Papers.
    RePEc:rmn:wpaper:201902.

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  5. Interest Rate Hysteresis in Macroeconomic Investment under Uncertainty. (2019). Göcke, Matthias ; Belke, Ansgar ; Gocke, Matthias.
    In: IZA Discussion Papers.
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  6. The importance of the financial system for the current account in Sweden: A sectoral approach. (2019). Shahnazarian, Hovick ; Spnberg, Erik.
    In: International Economics.
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  7. Capital flows to emerging market and developing economies: global liquidity and uncertainty versus country-specific pull factors. (2018). Volz, Ulrich ; Belke, Ansgar.
    In: Discussion Papers.
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  8. A Microfounded Model of Money Demand Under Uncertainty, and some Empirical Evidence. (2018). Tarassow, Artur ; Grol, Ingrid .
    In: Macroeconomics and Finance Series.
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  9. The impact of uncertainty on macro variables: An SVAR-based empirical analysis for EU countries. (2017). Belke, Ansgar ; Kronen, Dominik .
    In: Ruhr Economic Papers.
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  10. International effects of euro area versus US policy uncertainty: A FAVAR approach. (2017). Belke, Ansgar ; Osowski, Thomas.
    In: Ruhr Economic Papers.
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  11. International Effects of Euro Area versus US Policy Uncertainty: A FAVAR Approach. (2017). Belke, Ansgar ; Osowski, Thomas.
    In: GLO Discussion Paper Series.
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  12. The impact of uncertainty on macro variables - An SVAR-based empirical analysis for EU countries. (2017). Belke, Ansgar ; Kronen, Daniel.
    In: ROME Working Papers.
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  13. The impact of uncertainty on macro variables - An SVAR-based empirical analysis for EU countries. (2017). Belke, Ansgar ; Kronen, Dominik .
    In: ROME Working Papers.
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  14. International Effects of Euro Area versus US Policy Uncertainty: A FAVAR Approach. (2017). Belke, Ansgar ; Osowski, Thomas.
    In: ROME Working Papers.
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  15. The Finance-Uncertainty Multiplier. (2017). Lin, Xiaoji ; bloom, nicholas ; Alfaro, Ivan.
    In: 2017 Meeting Papers.
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  16. The Impact of Policy Uncertainty on Macro Variables – An SVAR-Based Empirical Analysis for EU Countries. (2017). Belke, Ansgar ; Ansgar, Belke ; Dominik, Kronen.
    In: Review of Economics.
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  17. Forecasting growth of U.S. aggregate and household-sector M2 after 2000 using economic uncertainty measures. (2017). Tarassow, Artur.
    In: Macroeconomics and Finance Series.
    RePEc:hep:macppr:201702.

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  18. Measuring uncertainty in the stock market. (2017). Uribe, Jorge ; Chuliá, Helena ; Guillen, Montserrat ; Chulia, Helena.
    In: International Review of Economics & Finance.
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  19. Measuring the financial soundness of U.S. firms, 1926–2012. (2017). Weill, Pierre-Olivier ; Eisfeldt, Andrea L ; Atkeson, Andrew G.
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  20. Uncertainty and Employment Dynamics in the Euro Area and the US. (2016). Netšunajev, Aleksei ; Glass, Katharina ; Netsunajev, Aleksei .
    In: SFB 649 Discussion Papers.
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  21. Real-time Macroeconomic Data and Uncertainty. (2015). Glass, Katharina ; Fritsche, Ulrich.
    In: Macroeconomics and Finance Series.
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  22. Growth uncertainty, generalized disappointment aversion and production-based asset pricing. (2015). Miao, Jianjun ; Liu, Hening.
    In: Journal of Monetary Economics.
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  23. Microeconomic Uncertainty, International Trade, and Aggregate Fluctuations. (2014). Midrigan, Virgiliu ; Kaboski, Joseph ; Choi, Horag ; Alessandria, George.
    In: NBER Working Papers.
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  24. Microeconomic uncertainty, international trade, and aggregate fluctuations. (2014). Kaboski, Joseph ; Choi, Horag ; Alessandria, George ; Midrigan, Virgiliu.
    In: Working Papers.
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  25. The UK Productivity and Jobs Puzzle: Does the Answer Lie in Labour Market Flexibility?. (2013). van Reenen, John ; Pessoa, João Paulo ; VanReenen, John .
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  25. data using the Compustat CUSIP identi…er. The bridge includes a mapping (m:m) between FIRMID (which can be found in the CM and ASM) and CUSIP8 (which can be found in Compustat and CRSP). The bridge covers the years 1976 to 2005. To extend the bridge to the entire sample of our analysis (1972-2010), we assigned each FIRMID after 2001 to the last observed CUSIP8 and before 1976 to the …rst observed CUSIP843 . From the CRSP data set we obtain daily and monthly returns at the …rm level (RET). From Compustat we obtain …rm-level quarterly sales (SALEQ) as well as data on equity (SEQQ) and debt (DLTTQ and DLCQ) which is used to construct the leverage ratio (in book values).
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  30. For annual frequency we add the ASM …les to the CM …les constructing a panel of establishments from 1972 to 2010 (using the LBDNUM identi…er).41 Starting 1973, the ASM is conducted every year in which a CM is not conducted. The ASM covers all establishments which were recorded in the CM above a certain size and a sample of the smaller establishments. The ASM includes 50,000 to 75,000 observations per year. Both the CM and the ASM provide detailed data on sales, value added, labor inputs, labor cost, cost of materials, capital expenditures, inventories and more. We give more details on the variables we use in the variables construction subsection below. A.1.2 Firm Level We use Compustat and CRSP to calculate volatility of sales and returns at the …rm level.42 The Compustat-SSEL bridge is used to match Census establishment data to Compustat and CRSP …rm’ s
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  31. For each 4-digit industry this variable stores the trade weighted proportion of product categories that were covered by a quota in 2005 by 4-digit industry categories.54 The instrument is then constructed as the interaction between the quota level and a dummy which takes the value of 1 for all years starting 2005. We limit the analysis to industries which are similar to the treated group, thus focusing on the textile and related industry (SIC codes 22, 23, 28 and 29, which were the 2-digit industries including sub-industries impacted by the quotas). We restrict the analysis to a 7-year window around the change (2002-2008).
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  45. However, starting in 1997, the CM does not separately report capital stocks for equipment and structures. For plants which existed in 1992, we can use the investment data to back out capital stocks for equipment and structures separately after 1992. For plants born after 1992, we assign the share of capital stock to equipment and structures to match the share of investment in equipment and structures. A.3.4 TFP and TFP Shocks For establishment j in industry i at year t we de…ne value added based total factor productivity (TFP) b zj;i;t as log (b zj;i;t) = log(vj;i;t) S i;t log(kS j;i;t) E i;t log(kE j;i;t) N i;t log(nj;i;t); where vj;i;t denotes value added (output less materials and energy inputs), kS j;i;t represents the capital stock of structures, kE j;i;t represents the capital stock of equipment and nj;i;t the total hours worked as described above. S
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  60. Our last sample (used in Figures 1 and 2), is based on the …rst sample, but includes only establishments that were active in 2005, 2006, 2008 and 2009. When calculating annual dispersion measures using CRSP and Compustat (see Table 1), we use the same sampling criteria as in the baseline ASM-CM sample, keeping only …rms which appear for at least 25 years. A.3 Variable Construction A.3.1 Value Added We use the Census value added measure which is de…ned for establishment j at year t as vj;t = Qj;t Mj;t Ej;t; where Qj;t is nominal output, Mj;t is cost of materials and Ej;t is cost of energy and fuels. Nominal output is calculated as the sum of total value of shipments and the change in inventory from previous year (both …nished inventory and work in progress inventory). In most of the analysis we use real value added. In this case, we de‡ ate value added by the 4-digit industry price of shipment (PISHIP) given in the NBER-CES data set.
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  73. Textile Quotas Instrument: The relaxation of quotas for China started when it joined the WTO in 2001, and peaked in 2005 when the quotas were completely removed. The removal of the quotas generated an increase in the imports of Chinese goods in the categories for which the quotas were removed. We use the 2005 quota variable constructed by Bloom, Draca, and Van Reenen (2011).
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  74. The fourth sample uses a balanced panel of establishments which were active for all years between 1972 and 2009. This sample consists of 3,449 establishments and 127,182 establishment-year observations.
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  75. The NBER-CES data are available only through 2009. 2010 industry-level data are therefore projected using an AR(4) regression for all external datasets. See http://www.bls.gov/mfp/mprdload.htm. See http://www.bea.gov/national/FA2004/SelectTable.asp. See http://www.federalreserve.gov/releases/G17/Current/default.htm.
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  76. The NBER-CES Manufacturing Industry Database is the main source for industry-level price indices for total value of shipments (PISHIP), and capital expenditures (PIINV).44 It is also the main source for industry-level total cost of inputs for labor (PAY). The total cost variable is used in the construction of industry cost shares. We match the NBER data to the establishment data using 4-digit SIC87 codes for the years 1972-1996 and 6-digit NAICS codes starting 1997.45 We complete our set of price indices using FRB capital investment de‡ ators, with separate de‡ ators for equipment and structures, kindly provided to us by Randy Becker.
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  77. The second sample, which is our baseline sample, keeps establishments which appear for at least 25 years between 1972 and 2009. This sample consists of 15,673 establishments and 453,704 establishment-year observations.52 The third sample we use is based on the baseline sample limited to establishments for which …rms have CRSP and Compustat records, with nonmissing values for stock returns, sales, equity and debt. The sample includes 10,498 establishments with 172,074 establishment-year observations.
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  78. This is available in an ECB press release from December 31, 1998, available at http://www.ecb.int/press/pr/date/1998/html/pr981231_2.en.html.
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  79. Thomas, J.K. (2002), Is Lumpy Investment Relevant for the Business Cycle?, Journal of Political Economy, 110(3), 508-534.

  80. Van Nieuwerburgh, S. and Veldkamp, L. (2006),Learning asymmetries in real business cycles, Journal of Monetary Economics, 53(4), 753-772.

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  83. We do this assignment for 2002-2005, since the bridge has many missing matches for these years. import-export data and industrial production.
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  7. Agency Conflicts, Investment, and Asset Pricing. (2005). Wang, Neng ; Albuquerque, Rui.
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  9. Agency Conflicts, Investment and Asset Pricing. (2005). Wang, Neng ; Albuquerque, Rui.
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  14. Uncertainty and investment: an empirical investigation using data on analysts profits forecasts. (2004). Cummins, Jason ; Bond, Stephen R..
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  17. Public Investment and Different Sources of Uncertainty. (2003). Menegatti, Mario.
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  18. Real exchange rate uncertainty and private investment in developing countries. (2002). Servén, Luis.
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  20. Equilibrium Cross-Section of Returns. (2002). Zhang, Lu ; Gomes, João.
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  21. Investment Decisions under Real Exchange Rate Uncertainty. (2001). Erdal, Bahar.
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  24. Direct Effects of Base Money on Aggregate Demand: Theory and Evidence. (2001). Nelson, Edward.
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  25. The real interest rate gap as an inflation indicator. (2001). Nelson, Edward ; Neiss, Katharine.
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  27. A Generalised Model of Investment under Uncertainty: Aggregation and Estimation. (2000). bloom, nicholas.
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  28. Discovering the Link Between Uncertainty and Investment - Microeconometric Evidence from Germany. (2000). Siegfried, Nikolaus A ; Funke, Michael ; Boehm, Hjalmar.
    In: Econometric Society World Congress 2000 Contributed Papers.
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  29. Direct effects of base money on aggregate demand: theory and evidence. (2000). Nelson, Edward.
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  30. The Role of a Variable Input in the Relationship between Investment and Uncertainty. (2000). Shin, Kwanho ; Lee, Jaewoo.
    In: American Economic Review.
    RePEc:aea:aecrev:v:90:y:2000:i:3:p:667-680.

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  31. Adjustment Costs, Learning-by-Doing, and Technology Adoption under Uncertainty. (1999). Pavlova, Anna.
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  32. The Timing and Scale of Investment Under Uncertainty. (1999). Small, John P..
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  33. What Do We Know About Investment Under Uncertainty?. (1998). Henley, Andrew ; Dickerson, Andrew ; Carruth, Alan.
    In: Studies in Economics.
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  34. Investment With Uncertain Tax Policy: Does Random Tax Policy Discourage Investment?. (1998). Metcalf, Gilbert ; hassett, kevin.
    In: Discussion Papers Series, Department of Economics, Tufts University.
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  35. Investment, capacity, and output: a putty-clay approach. (1998). Williams, John ; Gilchrist, Simon.
    In: Finance and Economics Discussion Series.
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  36. Uncertainty, instability, and irreversible investment : theory, evidence, and lessons for Africa. (1997). Servén, Luis.
    In: Policy Research Working Paper Series.
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  39. Volatility, Investment and Disappointment Aversion. (1995). Marion, Nancy ; Aizenman, Joshua.
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  40. The Effects of Irreversibility and Uncertainty on Capital Accumulation. (1995). Eberly, Janice ; Abel, Andrew.
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  42. Real Investment Decisions Under Information Constraints. (1995). Long, Ngo ; Lasserre, Pierre ; Gaudet, Gérard.
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