Global Economic Intersection
Advertisement
  • Home
  • Economics
  • Finance
  • Politics
  • Investments
    • Invest in Amazon $250
  • Cryptocurrency
    • Best Bitcoin Accounts
    • Bitcoin Robot
      • Quantum AI
      • Bitcoin Era
      • Bitcoin Aussie System
      • Bitcoin Profit
      • Bitcoin Code
      • eKrona Cryptocurrency
      • Bitcoin Up
      • Bitcoin Prime
      • Yuan Pay Group
      • Immediate Profit
      • BitQH
      • Bitcoin Loophole
      • Crypto Boom
      • Bitcoin Treasure
      • Bitcoin Lucro
      • Bitcoin System
      • Oil Profit
      • The News Spy
      • Bitcoin Buyer
      • Bitcoin Inform
      • Immediate Edge
      • Bitcoin Evolution
      • Cryptohopper
      • Ethereum Trader
      • BitQL
      • Quantum Code
      • Bitcoin Revolution
      • British Trade Platform
      • British Bitcoin Profit
    • Bitcoin Reddit
    • Celebrities
      • Dr. Chris Brown Bitcoin
      • Teeka Tiwari Bitcoin
      • Russell Brand Bitcoin
      • Holly Willoughby Bitcoin
No Result
View All Result
  • Home
  • Economics
  • Finance
  • Politics
  • Investments
    • Invest in Amazon $250
  • Cryptocurrency
    • Best Bitcoin Accounts
    • Bitcoin Robot
      • Quantum AI
      • Bitcoin Era
      • Bitcoin Aussie System
      • Bitcoin Profit
      • Bitcoin Code
      • eKrona Cryptocurrency
      • Bitcoin Up
      • Bitcoin Prime
      • Yuan Pay Group
      • Immediate Profit
      • BitQH
      • Bitcoin Loophole
      • Crypto Boom
      • Bitcoin Treasure
      • Bitcoin Lucro
      • Bitcoin System
      • Oil Profit
      • The News Spy
      • Bitcoin Buyer
      • Bitcoin Inform
      • Immediate Edge
      • Bitcoin Evolution
      • Cryptohopper
      • Ethereum Trader
      • BitQL
      • Quantum Code
      • Bitcoin Revolution
      • British Trade Platform
      • British Bitcoin Profit
    • Bitcoin Reddit
    • Celebrities
      • Dr. Chris Brown Bitcoin
      • Teeka Tiwari Bitcoin
      • Russell Brand Bitcoin
      • Holly Willoughby Bitcoin
No Result
View All Result
Global Economic Intersection
No Result
View All Result

Combining Models For Forecasting And Policy Analysis

admin by admin
March 23, 2015
in Uncategorized
0
0
SHARES
0
VIEWS
Share on FacebookShare on Twitter

by Marco Del Negro, Raiden Hasegawa, and Frank Schorfheide – Liberty Street Economics, Federal Reserve Bank of New York

First in a two-part series. Model uncertainty is pervasive. Economists, bloggers, policymakers all have different views of how the world works and what economic policies would make it better. These views are, like it or not, models. Some people spell them out in their entirety, equations and all. Others refuse to use the word altogether, possibly out of fear of being falsified. No model is “right,” of course, but some models are worse than others, and we can have an idea of which is which by comparing their predictions with what actually happened. If you are open-minded, you may actually want to combine models in making forecasts or policy analysis. This post discusses one way to do this, based on a recent paper of ours (Del Negro, Hasegawa, and Schorfheide 2014).


We call our approach “dynamic prediction pools,” where “pools” is just another word for model combination and “dynamic” sounds a lot better than “static.” Seriously, we want an approach to combining models that recognizes the fact that the world changes, that we have no perfect model, and that models that worked well in some periods may not work well in others. We apply our approach to two New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models, one with and one without financial frictions. (We call them SWπ and SWFF, respectively, where the SW underlines the fact that they are both based on the seminal work of Smets and Wouters.) So much for model diversity, you may be thinking. Aren’t these two models like Tweedledee and Tweedledum? Not quite, as the chart below shows.

Two DSGEs Forecasts of Output Growth in the Great Recession

The chart (which is taken from a different paper of ours, Del Negro and Schorfheide [2013]) shows the two DSGE model forecasts for output growth and inflation obtained with information available on January 10, 2009, at the apex of the financial crisis (2008:Q4 information was not yet available on that date, so this forecast is effectively based on National Income and Product Account data up to 2008:Q3). Specifically, each panel shows GDP growth (upper panels) or inflation (lower panels) data available at the time (the solid black line), the DSGE model’s mean forecasts (the red line), and bands of the forecast distribution (the shaded blue areas representing the 50, 60, 70, 80, and 90 percent bands for the forecast distribution in decreasing shade). The chart also shows the Blue Chip forecasts (diamonds) released on January 10, and the actual realizations, according to a May 2011 vintage of data (the dashed black line). All the numbers are quarter-over-quarter percent change.

It appears that an econometrician using the SWπ model would have had no clue of what was happening to the economy. The SWFF model, though, predicts output in 2008:Q4 just as well as the Blue Chip forecasters and a subsequent very sluggish recovery and low inflation, which is more or less what happened afterwards. (To better understand the behavior of inflation in the last five years, see a previous Liberty Street Economics post entitled “Inflation in the Great Recession and New Keynesian Models.”) The key difference between the two models is that SWFF uses spreads as a piece of information for forecasting (the spread between the Baa corporate bond yield and the yield on ten-year Treasuries, to be precise) while the SWπ model does not. The reason why we look at these two models is that before the crisis many macroeconomists thought that spreads and other financial variables were not of much help in forecasting (see this article by Stock and Watson).

The chart above suggests that the issue of which of the two models forecasts better is pretty settled. Yet the chart below shows that this conclusion would not be correct. It shows the forecasting accuracy from 1991 to 2011 for the SSWπ (the blue line) and the SWFF (the red line) models, as measured by the log-likelihood of predicting what actually happened in terms of four-quarter averages of output growth and inflation, with lower numbers meaning that the model was less useful in predicting what happened. For long stretches of time, the model without financial frictions actually forecasts better than the model with frictions. This result is in line with Stock and Watson’s findings that financial variables are useful at some times, but not all times. Not surprisingly, these stretches of time coincide with “financially tranquil” periods, while during “financially turbulent” periods such as the dot-com bust period and, most notably, the Great Recession, the SWFF model is superior.

Forecasting Accuracy Over Time

The relative forecasting accuracy of the two models changes over time and so we would like a procedure that puts more weight on the model that is best at a given point in time. Does our dynamic pools procedure accomplish that? You can judge for yourself from the chart below, which shows in three dimensions the distribution of the weights over time (the weights are computed using an algorithm called “particle filter“; we make both the codes and the data – the log-likelihoods of the two models shown in the chart above – available). We want to stress that this exercise is done using real-time information only – that is, for projections made in 2008:Q3, no data past that date were used in constructing the weights. The distribution of the weights moves like water in a bucket. When there is little information, such as at the beginning of the sample when the difference in forecasting ability between the models is small, the distribution is flat. During periods when one model is clearly better than the other, the bucket tilts to one side, and the water/mass piles up at the extremes.

Ch3_Dynamic-Pools-Weights-Distribution-over-Time

Fortunately, the mass seems to move from one side to the other of the bucket in a fairly timely way. The chart below compares the forecasting accuracy of the SWπ and SWFF models with that of dynamic pools (in black). As we noted before, the ideal model combination is the one that puts all the weight on the model that has the most accurate projection. The problem is, you know which one that is only afterwards. A measure of success is the extent to which your real-time combination produces a forecast that is as accurate as possible to that of the best model. The chart shows that the forecast accuracy of the dynamic pools approach is quite close to that of the best model most of the time, and in particular during the Great Recession. This confirms that the bucket tilted early enough in the game and shifted the weight toward the model with financial frictions. We show in the paper that, because of this timeliness, our procedure does generally better than the competition in terms of out-of-sample forecasting performance.

Forecasting Accuracy Over Time

Now, imagine you are a policymaker and are contemplating the implementation of a given policy. Consider also that this policy achieves the desired outcome in one model, but not in the other. Clearly the decision of whether to implement the policy depends on which model is the right one. You do not quite know that, but you do know the weights. Using them to come up with an assessment of the pros and cons of a given policy is the topic of our next post.

Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Source: http://libertystreeteconomics.newyorkfed.org/2015/03/combining-models-for-forecasting-and-policy-analysis.html#.VQ_2E_nF9K4


About the Authors

Del_negro_marco Marco Del Negro is an assistant vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Raiden Hasegawa is a Ph.D. student in statistics at the Wharton School, University of Pennsylvania.

Frank Schorfheide is a professor of economics at the University of Pennsylvania.

Previous Post

Early Headlines: Taiwan Looks for New Export Markets, Russia Threatens to Nuke Denmark, Greece Says Debt Payment Impossible and More

Next Post

Is Greece Planning to Print Energy?

Related Posts

US Institutions Account For 85% Of Bitcoin Acquisition In ‘Very Positive Sign’ – Matrixport
Economics

US Institutions Account For 85% Of Bitcoin Acquisition In ‘Very Positive Sign’ – Matrixport

by John Wanguba
January 28, 2023
U.S. Tackles Google Online Ad Business Monopoly In Latest Big Tech Lawsuit
Business

U.S. Tackles Google Online Ad Business Monopoly In Latest Big Tech Lawsuit

by John Wanguba
January 28, 2023
Tesla Plans $3.6B Nevada Expansion To Produce Semi Truck, Battery Cells
Business

Tesla Plans $3.6B Nevada Expansion To Produce Semi Truck, Battery Cells

by John Wanguba
January 28, 2023
Fed Policy Aiming To Align Bank Oversight Might Restrict Crypto Activities By State Banks
Business

Fed Policy Aiming To Align Bank Oversight Might Restrict Crypto Activities By State Banks

by John Wanguba
January 28, 2023
Microsoft Cloud Business Keeps Profits Flowing In Challenging Times
Business

Microsoft Cloud Business Keeps Profits Flowing In Challenging Times

by John Wanguba
January 27, 2023
Next Post

Is Greece Planning to Print Energy?

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Browse by Category

  • Business
  • Econ Intersect News
  • Economics
  • Finance
  • Politics
  • Uncategorized

Browse by Tags

adoption altcoins banking banks Binance Bitcoin Bitcoin adoption Bitcoin market Bitcoin mining blockchain BTC business China crypto crypto adoption cryptocurrency crypto exchange crypto market crypto regulation decentralized finance DeFi Elon Musk ETH Ethereum Europe finance FTX inflation investment market analysis markets Metaverse mining NFT nonfungible tokens oil market price analysis recession regulation Russia technology Tesla the UK the US Twitter

Archives

  • January 2023
  • December 2022
  • November 2022
  • October 2022
  • September 2022
  • August 2022
  • July 2022
  • June 2022
  • May 2022
  • April 2022
  • March 2022
  • February 2022
  • January 2022
  • December 2021
  • November 2021
  • October 2021
  • September 2021
  • August 2021
  • July 2021
  • June 2021
  • May 2021
  • April 2021
  • March 2021
  • February 2021
  • January 2021
  • December 2020
  • November 2020
  • October 2020
  • September 2020
  • August 2020
  • July 2020
  • June 2020
  • May 2020
  • April 2020
  • March 2020
  • February 2020
  • January 2020
  • December 2019
  • November 2019
  • October 2019
  • September 2019
  • August 2019
  • July 2019
  • June 2019
  • May 2019
  • April 2019
  • March 2019
  • February 2019
  • January 2019
  • December 2018
  • November 2018
  • October 2018
  • September 2018
  • August 2018
  • July 2018
  • June 2018
  • May 2018
  • April 2018
  • March 2018
  • February 2018
  • January 2018
  • December 2017
  • November 2017
  • October 2017
  • September 2017
  • August 2017
  • July 2017
  • June 2017
  • May 2017
  • April 2017
  • March 2017
  • February 2017
  • January 2017
  • December 2016
  • November 2016
  • October 2016
  • September 2016
  • August 2016
  • July 2016
  • June 2016
  • May 2016
  • April 2016
  • March 2016
  • February 2016
  • January 2016
  • December 2015
  • November 2015
  • October 2015
  • September 2015
  • August 2015
  • July 2015
  • June 2015
  • May 2015
  • April 2015
  • March 2015
  • February 2015
  • January 2015
  • December 2014
  • November 2014
  • October 2014
  • September 2014
  • August 2014
  • July 2014
  • June 2014
  • May 2014
  • April 2014
  • March 2014
  • February 2014
  • January 2014
  • December 2013
  • November 2013
  • October 2013
  • September 2013
  • August 2013
  • July 2013
  • June 2013
  • May 2013
  • April 2013
  • March 2013
  • February 2013
  • January 2013
  • December 2012
  • November 2012
  • October 2012
  • September 2012
  • August 2012
  • July 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • May 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • August 2010
  • August 2009

Categories

  • Business
  • Econ Intersect News
  • Economics
  • Finance
  • Politics
  • Uncategorized
Global Economic Intersection

After nearly 11 years of 24/7/365 operation, Global Economic Intersection co-founders Steven Hansen and John Lounsbury are retiring. The new owner, a global media company in London, is in the process of completing the set-up of Global Economic Intersection files in their system and publishing platform. The official website ownership transfer took place on 24 August.

Categories

  • Business
  • Econ Intersect News
  • Economics
  • Finance
  • Politics
  • Uncategorized

Recent Posts

  • US Institutions Account For 85% Of Bitcoin Acquisition In ‘Very Positive Sign’ – Matrixport
  • U.S. Tackles Google Online Ad Business Monopoly In Latest Big Tech Lawsuit
  • Tesla Plans $3.6B Nevada Expansion To Produce Semi Truck, Battery Cells

© Copyright 2021 EconIntersect - Economic news, analysis and opinion.

No Result
View All Result
  • Home
  • Contact Us
  • Bitcoin Robot
    • Bitcoin Profit
    • Bitcoin Code
    • Quantum AI
    • eKrona Cryptocurrency
    • Bitcoin Up
    • Bitcoin Prime
    • Yuan Pay Group
    • Immediate Profit
    • BitIQ
    • Bitcoin Loophole
    • Crypto Boom
    • Bitcoin Era
    • Bitcoin Treasure
    • Bitcoin Lucro
    • Bitcoin System
    • Oil Profit
    • The News Spy
    • British Bitcoin Profit
    • Bitcoin Trader
  • Bitcoin Reddit

© Copyright 2021 EconIntersect - Economic news, analysis and opinion.

en English
ar Arabicbg Bulgarianda Danishnl Dutchen Englishfi Finnishfr Frenchde Germanel Greekit Italianja Japaneselv Latvianno Norwegianpl Polishpt Portuguesero Romanianes Spanishsv Swedish