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A dynamic log-linear regression model to forecast numbers of future filings at the European Patent Office
6 references·1 citing·37 second hop
A dynamic log-linear regression model to forecast numbers of future filings at the European Patent Office (2015)A dynamic log-linear regression model to …Financial Conditions Indexes: A Fresh Look after the Financial Crisis (2010)Financial Conditions Indexes:…Financial Stress: What Is It, How Can It Be Measured, and Why Does It Matter? (2009)Financial Stress: What Is It,…Dynamic probit models and financial variables in recession forecasting (2009)Dynamic probit models and fin…Predicting U.S. Recessions with Dynamic Binary Response Models (2008)Predicting U.S. Recessions wi…Methods for forecasting numbers of patent applications at the European Patent Office (2004)Methods for forecasting numbe…Postwar U.S. Business Cycles: An Empirical Investigation (1997)Postwar U.S. Business Cycles:…RETRACTED ARTICLE: Determination of the most influential factors for number of patents prediction by adaptive neuro-fuzzy technique (2016)RETRACTED ARTICLE: Determinat…The Impact of Uncertainty Shocks (2007)Market Liquidity and Funding Liquidity (2008)Deciphering the Liquidity and Credit Crunch 2007–2008 (2009)Financial Stress, Downturns, and Recoveries (2009)CISS - A Composite Indicator of Systemic Stress in the Financial System (2010)Does Systemic Risk in the Financial Sector Predict Future Economic Downturns? (2012)Capital Theory and Investment Behavior (1963)Monetary Policy Shocks: What Have We Learned and to What End? (1998)Determining the Number of Factors in Approximate Factor Models (2002)The financial cycle and macroeconomics: What have we learnt? (2013)A new index of financial conditions (2014)Time Series Analysis, Forecasting and Control. (1971)Book Review: Time Series Analysis: Forecasting and Control (1977)Principles of Forecasting (2001)Trends disrupted—patent information in an era of change (2005)Numbers and sizes of applicants at the European Patent Office (2009)Optimization of prediction methods for patents and trademarks in Spain through the use of exogenous variables (2013)The General Theory of Employment, Interest and Money, by John Maynard Keynes (1936)The General Theory of Employment, Interest and Money. (1936)Macroeconomics and Reality (1980)The Band Pass Filter (1999)On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations (2002)Measuring Uncertainty (2015)Scoring the Leading Indicators (1989)Predicting U.S. Recessions: Financial Variables as Leading Indicators (1998)A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series (2005)Calling recessions in real time (2010)Change Detection and the Causal Impact of the Yield Curve (2018)Stock Returns, Expected Returns, and Real Activity (1990)The Term Structure as a Predictor of Real Economic Activity (1991)Forecasting Output and Inflation: The Role of Asset Prices (2003)What predicts US recessions? (2016)Predicting recessions with boosted regression trees (2017)Stock market reactions to COVID-19 lockdown: A global analysis (2021)ELECTRICITY CONSUMPTION IN THE INDUSTRIAL SECTOR OF JORDAN: APPLICATION OF MULTIVARIATE LINEAR REGRESSION AND ADAPTIVE NEURO-FUZZY TECHNIQUES (2009)Modeling patent legal value by Extension Neural Network (2009)Forecasting technology success based on patent data (2015)
this work references cited by second hop

References

  • 1997Postwar U.S. Business Cycles: An Empirical Investigation7,358 cited
  • 2009Financial Stress: What Is It, How Can It Be Measured, and Why Does It Matter?440 cited
  • 2010Financial Conditions Indexes: A Fresh Look after the Financial Crisis398 cited
  • 2008Predicting U.S. Recessions with Dynamic Binary Response Models315 cited
  • 2009Dynamic probit models and financial variables in recession forecasting162 cited
  • 2004Methods for forecasting numbers of patent applications at the European Patent Office28 cited

Cited by

  • 2016RETRACTED ARTICLE: Determination of the most influential factors for number of patents prediction by adaptive neuro-fuzzy technique4 cited

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