Central bank research hub - Papers by Frank Schorfheide
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Research hub papers by author Frank SchorfheideenTempered Particle Filtering
http://www.federalreserve.gov/econresdata/feds/2016/files/2016072pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Edward Herbst and Frank SchorfheideTempered Particle Filtering2016-09-07T17:38:00ZThe accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter this distribution is generated by the state-transition equation. While straightforward to implement, the practical performance is often poor. We develop a self-tuning particle filter in which the proposal distribution is constructed adaptively through a sequence of Monte Carlo steps. Intuitively, we start from a measurement error distribution with an inflated variance, and then gradually reduce the variance to its nominal level in a sequence of steps that we call tempering. We show that the filter generates an unbiased and consistent approximation of the likelihood function. Holding the run time fixed, our filter is substantially more accurate in two DSGE model applications than the bootstrap particle filter.Tempered Particle FilteringFull texthttp://www.federalreserve.gov/econresdata/feds/2016/files/2016072pap.pdfEdward HerbstFrank SchorfheideEdward Herbst and Frank Schorfheide2016-09Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesC11C15E10Macroeconomic Dynamics Near the ZLB: A Tale of Two Countries
http://www.federalreserve.gov/econresdata/ifdp/2016/files/ifdp1163.pdf
Board of Governors of the Federal Reserve System International Financial Discussion Papers by S. Boragan Aruoba, Pablo Cuba-Borda, and Frank SchorfheideMacroeconomic Dynamics Near the ZLB: A Tale of Two Countries2016-05-26T18:23:59ZWe compute a sunspot equilibrium in an estimated small-scale New Keynesian model with a zero lower bound (ZLB) constraint on nominal interest rates and a full set of stochastic fundamental shocks. In this equilibrium a sunspot shock can move the economy from a regime in which inflation is close to the central bank?s target to a regime in which the central bank misses its target, inflation rates are negative, and interest rates are close to zero with high probability. A nonlinear filter is used to examine whether the U.S. in the aftermath of the Great Recession and Japan in the late 1990s transitioned to a deflation regime. The results are somewhat sensitive to the model specification, but on balance, the answer is affirmative for Japan and negative for the U.S.Macroeconomic Dynamics Near the ZLB: A Tale of Two CountriesFull texthttp://www.federalreserve.gov/econresdata/ifdp/2016/files/ifdp1163.pdfPablo Cuba-BordaFrank SchorfheideS. Boragan AruobaS. Boragan Aruoba, Pablo Cuba-Borda, and Frank Schorfheide2016-05Board of Governors of the Federal Reserve System International Financial Discussion PapersC5E4E5Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance
http://www.newyorkfed.org/research/staff_reports/sr695.pdf
New York Fed Staff reports by Marco Del Negro, Raiden B. Hasegawa, and Frank SchorfheideDynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance2014-10-17T17:34:59ZWe provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call dynamic pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, for output growth and inflation in the period 1992 to 2011. We find strong evidence of time variation in the pool's weights, reflecting the fact that the DSGE model with financial frictions produces superior forecasts in periods of financial distress but doesn't perform as well in tranquil periods. The dynamic pool's weights react in a timely fashion to changes in the environment, leading to real-time forecast improvements relative to other methods of density forecast combination, such as Bayesian model averaging, optimal (static) pools, and equal weights. We show how a policymaker dealing with model uncertainty could have used a dynamic pool to perform a counterfactual exercise (responding to the gap in labor market conditions) in the immediate aftermath of the Lehman crisis.Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting PerformanceAbstracthttp://www.newyorkfed.org/research/staff_reports/sr695.htmlFull texthttp://www.newyorkfed.org/research/staff_reports/sr695.pdfFrank SchorfheideRaiden B. HasegawaMarco Del NegroMarco Del Negro, Raiden B. Hasegawa, and Frank Schorfheide2014-10Federal Reserve Bank of New York Staff ReportsC53E31E32E37Sequential Monte Carlo Sampling for DSGE Models
http://www.federalreserve.gov/pubs/feds/2013/201343/201343pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Edward P. Herbst and Frank SchorfheideSequential Monte Carlo Sampling for DSGE Models2013-07-22T18:46:59ZHerbst and Frank Schorfheide. We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models, wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using three examples--an artificial state-space model, the Smets and Wouters (2007) model, and Schmitt-Grohe and Uribe's (2012) news shock model--we show that the SMC algorithm is better suited for multimodal and irregular posterior distributions than the widely-used random-walk Metropolis-Hastings algorithm. We find that a more diffuse prior for the Smets and Wouters (2007) model improves its marginal data density and that a slight modification of the prior for the news shock model leads to important changes in the posterior inference about the importance of news shocks for fluctuations in hours worked. Unlike standard Markov chain Monte Carlo (MCMC) techniques, the SMC algorithm is well suited for parallel computing.Sequential Monte Carlo Sampling for DSGE ModelsAbstracthttp://www.federalreserve.gov/pubs/feds/2013/201343/201343abs.htmlFull texthttp://www.federalreserve.gov/pubs/feds/2013/201343/201343pap.pdfEdward P. HerbstFrank SchorfheideEdward P. Herbst and Frank Schorfheide2013-07-22Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesDSGE Model-Based Forecasting
http://www.newyorkfed.org/research/staff_reports/sr554.pdf
New York Fed Staff reports by Marco Del Negro and Frank SchorfheideDSGE Model-Based Forecasting2012-11-30T17:38:00ZDynamic stochastic general equilibrium (DSGE) models use modern macroeconomic theory to explain and predict comovements of aggregate time series over the business cycle and to perform policy analysis. We explain how to use DSGE models for all three purposes-forecasting, story telling, and policy experiments-and review their forecasting record. We also provide our own real-time assessment of the forecasting performance of the Smets and Wouters (2007) model data up to 2011, compare it with Blue Chip and Greenbook forecasts, and show how it changes as we augment the standard set of observables with external information from surveys (nowcasts, interest rate forecasts, and expectations for long-run inflation and output growth). We explore methods of generating forecasts in the presence of a zero-lower-bound constraint on nominal interest rates and conditional on counterfactual interest rate paths. Finally, we perform a postmortem of DSGE model forecasts of the Great Recession and show that forecasts from a version of the Smets-Wouters model augmented by financial frictions, and using spreads as an observable, compare well with Blue Chip forecasts.DSGE Model-Based ForecastingAbstracthttp://www.newyorkfed.org/research/staff_reports/sr554.htmlFull texthttp://www.newyorkfed.org/research/staff_reports/sr554.pdfFrank SchorfheideMarco Del NegroMarco Del Negro and Frank Schorfheide2012-03Federal Reserve Bank of New York Staff ReportsEvaluating DSGE Model Forecasts of Comovements
http://www.federalreserve.gov/pubs/feds/2012/201211/201211pap.pdf
Board of Governors of the Federal Reserve System FEDS series by Edward Herbst and Frank SchorfheideEvaluating DSGE Model Forecasts of Comovements2012-05-01T06:23:59ZThis paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. We construct posterior predictive checks to evaluate conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation DSGE model as well as the Smets and Wouters (2007) model using real-time data. We find that the additional features incorporated into the Smets-Wouters model do not lead to a uniform improvement in the quality of density forecasts and prediction of comovements of output, inflation, and interest rates.Evaluating DSGE Model Forecasts of ComovementsAbstracthttp://www.federalreserve.gov/pubs/feds/2012/201211/201211abs.htmlFull texthttp://www.federalreserve.gov/pubs/feds/2012/201211/201211pap.pdfEdward HerbstFrank SchorfheideEdward Herbst and Frank Schorfheide2012-03-02Board of Governors of the Federal Reserve System Finance and Economics Discussion SeriesC11C32C53E27E47Estimation and Evaluation of DSGE Models: Progress and Challenges
http://www.philadelphiafed.org/research-and-data/publications/working-papers/2011/wp11-7.pdf
Philadelphia Fed Working Papers by Frank SchorfheideEstimation and Evaluation of DSGE Models: Progress and Challenges2011-03-29T12:41:00ZEstimated dynamic stochastic equilibrium (DSGE) models are now widely used for empirical research in macroeconomics as well as for quantitative policy analysis and forecasting at central banks around the world. This paper reviews recent advances in the estimation and evaluation of DSGE models, discusses current challenges, and provides avenues for future research.Estimation and Evaluation of DSGE Models: Progress and ChallengesFull texthttp://www.philadelphiafed.org/research-and-data/publications/working-papers/2011/wp11-7.pdfFrank SchorfheideFrank Schorfheide2011-03Federal Reserve Bank of Philadelphia Working PapersEvaluating DSGE Model Forecasts of Comovements
http://www.philadelphiafed.org/research-and-data/publications/working-papers/2011/wp11-5.pdf
Philadelphia Fed Working Papers by Frank Schorfheide and Edward HerbstEvaluating DSGE Model Forecasts of Comovements2011-03-29T12:41:00ZThis paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. The authors construct posterior predictive checks to evaluate the calibration of conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation DSGE model as well as the Smets and Wouters (2007) model using real-time data. They find that the additional features incorporated into the Smets-Wouters model do not lead to a uniform improvement in the quality of density forecasts and prediction of comovements of output, inflation, and interest rates.Evaluating DSGE Model Forecasts of ComovementsFull texthttp://www.philadelphiafed.org/research-and-data/publications/working-papers/2011/wp11-5.pdfEdward HerbstFrank SchorfheideFrank Schorfheide2011-03Federal Reserve Bank of Philadelphia Working PapersMonetary Policy Analysis with Potentially Misspecified Models
http://www.newyorkfed.org/research/staff_reports/sr321.pdf
New York Fed Staff reports by Marco Del Negro and Frank SchorfheideMonetary Policy Analysis with Potentially Misspecified Models2008-11-07T07:22:00ZMonetary Policy Analysis with Potentially Misspecified ModelsAbstracthttp://www.newyorkfed.org/research/staff_reports/sr321.htmlFull texthttp://www.newyorkfed.org/research/staff_reports/sr321.pdfFrank SchorfheideMarco Del NegroMarco Del Negro and Frank Schorfheide2008-03Federal Reserve Bank of New York Staff ReportsC32Inflation Dynamics in a Small Open-Economy Model under Inflation Targeting: Some Evidence from Chile
http://www.newyorkfed.org/research/staff_reports/sr329.pdf
New York Fed Staff reports by Marco Del Negro and Frank SchorfheideInflation Dynamics in a Small Open-Economy Model under Inflation Targeting: Some Evidence from Chile2008-11-07T07:22:00ZInflation Dynamics in a Small Open-Economy Model under Inflation Targeting: Some Evidence from ChileAbstracthttp://www.newyorkfed.org/research/staff_reports/sr329.htmlFull texthttp://www.newyorkfed.org/research/staff_reports/sr329.pdfFrank SchorfheideMarco Del NegroMarco Del Negro and Frank Schorfheide2008-06Federal Reserve Bank of New York Staff ReportsC11C32E52F41Inflation Dynamics in a Small Open Economy Model Under Inflation Targeting: Some Evidence From Chile
http://www.bcentral.cl/eng/studies/working-papers/pdf/dtbc486.pdf
Central Bank of Chile Working Papers by Marco del Negro; Frank SchorfheideInflation Dynamics in a Small Open Economy Model Under Inflation Targeting: Some Evidence From Chile2008-09-22T17:34:59ZThe paper estimates a small open economy DSGE model, specified along the lines of Galí and Monacelli (REStud 2005) and Lubik and Schorfheide (JME 2007), on Chilean data for the full inflation targeting period (1999-2007). We study the specification of the policy rule followed by the Central Bank, the dynamic response of inflation to domestic and external shocks, and how these dynamics change under different policy parameters. We use the DSGE-VAR methodology (Del Negro and Schorfheide 2007) to assess the robustness of the conclusions to the presence of model misspecification.Inflation Dynamics in a Small Open Economy Model Under Inflation Targeting: Some Evidence From ChileAbstracthttp://www.bcentral.cl/eng/studies/working-papers/486.htmFull texthttp://www.bcentral.cl/eng/studies/working-papers/pdf/dtbc486.pdfFrank SchorfheideMarco del NegroMarco del Negro; Frank Schorfheide2008-09Central Bank of Chile Working PapersDSGE Model-Based Forecasting of Non-Modelled Variables
http://www.philadelphiafed.org/research-and-data/publications/working-papers/2008/wp08-17.pdf
Philadelphia Fed Working Papers by Frank SchorfheideDSGE Model-Based Forecasting of Non-Modelled Variables2008-09-18T12:39:59ZThis paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). The authors use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to the state variables of the DSGE model. Predictions for the non-core variables are obtained by applying their measurement equations to DSGE model- generated forecasts of the state variables. Using a medium-scale New Keynesian DSGE model, the authors apply their approach to generate and evaluate recursive forecasts for PCE inflation, core PCE inflation, and the unemployment rate along with predictions for the seven variables that have been used to estimate the DSGE model.DSGE Model-Based Forecasting of Non-Modelled VariablesFull texthttp://www.philadelphiafed.org/research-and-data/publications/working-papers/2008/wp08-17.pdfFrank SchorfheideFrank Schorfheide2008-09Federal Reserve Bank of Philadelphia Working PapersForming Priors for DSGE Models (and How It Affectsthe Assessment of Nominal Rigidities)
http://www.newyorkfed.org/research/economists/delnegro/index.html
New York Fed Staff reports by Marco Del Negro and Frank SchorfheideForming Priors for DSGE Models (and How It Affectsthe Assessment of Nominal Rigidities)2008-03-13T07:18:00ZForming Priors for DSGE Models (and How It Affectsthe Assessment of Nominal Rigidities)Abstracthttp://www.newyorkfed.org/research/staff_reports/sr320.htmlFull texthttp://www.newyorkfed.org/research/economists/delnegro/index.htmlFrank SchorfheideMarco Del NegroMarco Del Negro and Frank Schorfheide2008-03Federal Reserve Bank of New York Staff ReportsC32E30Forming Priors for DSGE Models (and How It Affects the Assessment of Nominal Rigidities)
http://www.frbatlanta.org/filelegacydocs/wp0616.pdf
Atlanta Fed Working papers by Marco Del Negro and Frank SchorfheideForming Priors for DSGE Models (and How It Affects the Assessment of Nominal Rigidities)2006-11-01T07:10:59ZForming Priors for DSGE Models (and How It Affects the Assessment of Nominal Rigidities)Abstracthttp://www.frbatlanta.org/invoke.cfm?objectid=7FC5BBB0-5056-9F12-128B598102936801&method=displayFull texthttp://www.frbatlanta.org/filelegacydocs/wp0616.pdfFrank SchorfheideMarco Del NegroMarco Del Negro and Frank Schorfheide2006-10Federal Reserve Bank of Atlanta Working PapersBayesian Analysis of DSGE Models
http://www.philadelphiafed.org/research-and-data/publications/working-papers/2006/wp06-5.pdf
Philadelphia Fed Working Papers by Sungbae An and Frank SchorfheideBayesian Analysis of DSGE Models2006-02-01T12:00:00ZBayesian Analysis of DSGE ModelsFull texthttp://www.philadelphiafed.org/research-and-data/publications/working-papers/2006/wp06-5.pdfFrank SchorfheideSungbae AnSungbae An and Frank Schorfheide2006Federal Reserve Bank of Philadelphia Working PapersNon-Stationary Hours in a DSGE Model
http://www.philadelphiafed.org/research-and-data/publications/working-papers/2006/wp06-3.pdf
Philadelphia Fed Working Papers by Yongsung Chang, Taeyoung Doh, and Frank SchorfheideNon-Stationary Hours in a DSGE Model2006-02-01T12:00:00ZNon-Stationary Hours in a DSGE ModelFull texthttp://www.philadelphiafed.org/research-and-data/publications/working-papers/2006/wp06-3.pdfTaeyoung DohYongsung ChangFrank SchorfheideYongsung Chang, Taeyoung Doh, and Frank Schorfheide2006Federal Reserve Bank of Philadelphia Working PapersMonetary Policy Analysis with Potentially Misspecified Models
http://www.philadelphiafed.org/research-and-data/publications/working-papers/2006/wp06-4.pdf
Philadelphia Fed Working Papers by Marco Del Negro and Frank SchorfheideMonetary Policy Analysis with Potentially Misspecified Models2006-02-01T12:00:00ZMonetary Policy Analysis with Potentially Misspecified ModelsFull texthttp://www.philadelphiafed.org/research-and-data/publications/working-papers/2006/wp06-4.pdfFrank SchorfheideMarco Del NegroMarco Del Negro and Frank Schorfheide2006Federal Reserve Bank of Philadelphia Working PapersMonetary Policy Analysis with Potentially Misspecified Models
http://www.frbatlanta.org/filelegacydocs/wp0526.pdf
Atlanta Fed Working papers by Marco Del Negro and Frank SchorfheideMonetary Policy Analysis with Potentially Misspecified Models2005-12-02T07:08:59ZMonetary Policy Analysis with Potentially Misspecified ModelsAbstracthttp://www.frbatlanta.org/invoke.cfm?objectid=E29F4F21-5056-9F06-999D9012E95A68FC&method=displayFull texthttp://www.frbatlanta.org/filelegacydocs/wp0526.pdfFrank SchorfheideMarco Del NegroMarco Del Negro and Frank Schorfheide2005-12Federal Reserve Bank of Atlanta Working PapersC32On the fit and forecasting performance of New-Keynesian models
http://www.ecb.int/pub/pdf/scpwps/ecbwp491.pdf
European Central Bank Working papers by Marco Del Negro, Frank SchorfheideOn the fit and forecasting performance of New-Keynesian models2005-06-02T07:10:00ZOn the fit and forecasting performance of New-Keynesian modelsECBFull texthttp://www.ecb.int/pub/pdf/scpwps/ecbwp491.pdfFrank SchorfheideMarco Del NegroMarco Del Negro, Frank Schorfheide2005-06European Central Bank Working PapersC11C32C53Monetary policy analysis with potentially misspecified models
http://www.ecb.int/pub/pdf/scpwps/ecbwp475.pdf
European Central Bank Working papers by Marco Del Negro and Frank SchorfheideMonetary policy analysis with potentially misspecified models2005-04-27T17:32:00ZMonetary policy analysis with potentially misspecified modelsECBFull texthttp://www.ecb.int/pub/pdf/scpwps/ecbwp475.pdfFrank SchorfheideMarco Del NegroMarco Del Negro and Frank Schorfheide2005-04European Central Bank Working PapersC32Policy Predictions If the Model Doesn¿t Fit
http://www.frbatlanta.org/filelegacydocs/wp0438.pdf
Atlanta Fed Working papers by Marco Del Negro and Frank SchorfheidePolicy Predictions If the Model Doesn¿t Fit2005-01-14T12:35:00ZPolicy Predictions If the Model Doesn¿t FitAbstracthttp://www.frbatlanta.org/invoke.cfm?objectid=68644CD3-5056-B72C-D62353C58DFF04F1&method=displayFull texthttp://www.frbatlanta.org/filelegacydocs/wp0438.pdfFrank SchorfheideMarco Del NegroMarco Del Negro and Frank Schorfheide2004-12Federal Reserve Bank of Atlanta Working PapersC32On the Fit and Forecasting Performance of New Keynesian Models
http://www.frbatlanta.org/filelegacydocs/wp0437.pdf
Atlanta Fed Working papers by Marco Del Negro, Frank Schorfheide, Frank Smets, and Raf WoutersOn the Fit and Forecasting Performance of New Keynesian Models2005-01-14T12:35:00ZOn the Fit and Forecasting Performance of New Keynesian ModelsAbstracthttp://www.frbatlanta.org/invoke.cfm?objectid=68615B3C-5056-B72C-D6D0143E0BA58664&method=displayFull texthttp://www.frbatlanta.org/filelegacydocs/wp0437.pdfFrank SchorfheideFrank SmetsMarco Del NegroRaf WoutersMarco Del Negro, Frank Schorfheide, Frank Smets, and Raf Wouters2004-12Federal Reserve Bank of Atlanta Working PapersC11C32C53Labor Supply Shifts and Economic Fluctuations
http://www.rich.frb.org/publications/economic_research/working_papers/pdfs/wp03-7.pdf
Richmond Fed Working Papers by Yongsung Chang, Frank SchorfheideLabor Supply Shifts and Economic Fluctuations2004-04-07T07:08:59ZLabor Supply Shifts and Economic FluctuationsAbstracthttp://www.rich.frb.org/publications/economic_research/working_papers/paper.cfm?link=03-07Full texthttp://www.rich.frb.org/publications/economic_research/working_papers/pdfs/wp03-7.pdfYongsung ChangFrank SchorfheideYongsung Chang, Frank Schorfheide2003-07Federal Reserve Bank of Richmond Working PapersC52E32J22Learning and Monetary Policy Shifts
http://www.frbatlanta.org/file_invoke.cfm?objectid=E7E45FC8-3547-459B-B6B596E10FC8DADF&method=display_filelegacydoc
Atlanta Fed Working papers by Frank SchorfheideLearning and Monetary Policy Shifts2003-10-08T07:06:59ZLearning and Monetary Policy ShiftsAbstracthttp://www.frbatlanta.org/invoke.cfm?objectid=306BF707-5C1A-484B-A4AD84C6A54D5D1E&method=displayFull texthttp://www.frbatlanta.org/file_invoke.cfm?objectid=E7E45FC8-3547-459B-B6B596E10FC8DADF&method=display_filelegacydocFrank SchorfheideFrank Schorfheide2003-10Federal Reserve Bank of Atlanta Working PapersPriors from General Equilibrium Models for VARs
http://www.frbatlanta.org/filelegacydocs/wp0214.pdf
Atlanta Fed Working papers by Marco Del Negro and Frank SchorfheidePriors from General Equilibrium Models for VARs2003-01-31T17:58:00ZPriors from General Equilibrium Models for VARsAbstracthttp://www.frbatlanta.org/invoke.cfm?objectid=5F19A83E-700C-4CE6-9519286083DB5577&method=displayFull texthttp://www.frbatlanta.org/filelegacydocs/wp0214.pdfFrank SchorfheideMarco Del NegroMarco Del Negro and Frank Schorfheide2002-08Federal Reserve Bank of Atlanta Working PapersC11C32C53