Forecasting with mixed-frequency time series models

[1]  Roberto Golinelli,et al.  The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries , 2007 .

[2]  Bharat Trehan,et al.  Using monthly data to predict quarterly output , 1996 .

[3]  Eric Ghysels,et al.  Regression Models with Mixed Sampling Frequencies , 2010 .

[4]  Phoebus J. Dhrymes,et al.  Distributed Lags. Problems of Estimation and Formulation. , 1973 .

[5]  Eric Ghysels,et al.  Forecasting Professional Forecasters , 2006 .

[6]  Peter Zadrozny,et al.  Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies , 1988, Econometric Theory.

[7]  George Kapetanios,et al.  A parametric estimation method for dynamic factor models of large dimensions , 2006 .

[8]  J. Stock,et al.  Forecasting Using Principal Components From a Large Number of Predictors , 2002 .

[9]  James Mitchell,et al.  An Indicator of Monthly GDP and an Early Estimate of Quarterly GDP Growth , 2005 .

[10]  Frank T. Denton,et al.  Adjustment of Monthly or Quarterly Series to Annual Totals: An Approach Based on Quadratic Minimization , 1971 .

[11]  E. Ghysels,et al.  Série Scientifique Scientific Series Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies , 2022 .

[12]  R. Golinelli,et al.  Bridge models to forecast the euro area GDP , 2004 .

[13]  Chung-Hua Shen,et al.  Forecasting macroeconomic variables using data of different periodicities , 1996 .

[14]  T. Abeysinghe.  Forecasting Singapore's quarterly GDP with monthly external trade , 1998 .

[15]  Serena Ng,et al.  Determining the Number of Primitive Shocks in Factor Models , 2007 .

[16]  M. Friedman.  The Interpolation of Time Series by Related Series , 1962 .

[17]  Massimiliano Marcellino,et al.  Interpolation and Backdating with a Large Information Set , 2003, SSRN Electronic Journal.

[18]  A. Timmermann.  Forecast Combinations , 2005 .

[19]  Jane Haltmaier,et al.  Minimum variance pooling of forecasts at different levels of aggregation , 1987 .

[20]  Nikolay Robinzonov,et al.  Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models , 2008 .

[21]  Richard H. Jones,et al.  Maximum Likelihood Fitting of ARMA Models to Time Series With Missing Observations , 1980 .

[22]  Klaus Wohlrabe,et al.  Makroökonomische Prognosen mit gemischten Frequenzen , 2009 .

[23]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986, Encyclopedia of Big Data.

[24]  S. Henry,et al.  The Use of Financial Spreads as Indicator Variables: Evidence for the United Kingdom and Germany , 1994 .

[25]  Tommaso Proietti,et al.  Dynamic factor analysis with non‐linear temporal aggregation constraints , 2006 .

[26]  David Veredas,et al.  Temporal Aggregation of Univariate and Multivariate Time Series Models: A Survey , 2008 .

[27]  Michael P. Clements,et al.  Macroeconomic Forecasting with Mixed Frequency Data: Forecasting Us Output Growth and Inflation , 2006 .

[28]  J. Florens,et al.  GENERALIZATION OF GMM TO A CONTINUUM OF MOMENT CONDITIONS , 2000, Econometric Theory.

[29]  T. Liu,et al.  A Monthly Recursive Econometric Model of United States: A Test of Feasibility , 1969 .

[30]  Sandra Eickmeier,et al.  How Successful are Dynamic Factor Models at Forecasting Output and Inflation? A Meta-Analytic Approach , 2008 .

[31]  G. Judge,et al.  The Theory and Practice of Econometrics , 1981 .

[32]  Á. León,et al.  The relationship between risk and expected return in Europe. , 2007 .

[33]  R. Mariano,et al.  A New Coincident Index of Business Cycles Based on Monthly and Quarterly Series , 2002 .

[34]  Richard Schmalensee,et al.  Advertising and aggregate consumption: an analysis of causality , 1980 .

[35]  R. Kohn,et al.  Estimation, Filtering, and Smoothing in State Space Models with Incompletely Specified Initial Conditions , 1985 .

[36]  Anil K. Bera,et al.  ARCH Models: Properties, Estimation and Testing , 1993 .

[37]  E. Meijering.  A chronology of interpolation: from ancient astronomy to modern signal and image processing , 2002, Proc. IEEE.

[38]  Dongmin Kong,et al.  Is there a risk-return trade-off? Evidences from Chinese stock markets , 2008 .

[39]  Takeshi Amemiya,et al.  The Effect of Aggregation on Prediction in the Autoregressive Model , 1972 .

[40]  Stefan Mittnik,et al.  Forecasting Quarterly German GDP at Monthly Intervals Using Monthly Ifo Business Conditions Data , 2004, SSRN Electronic Journal.

[41]  M. Hashem Pesaran,et al.  REAL-TIME ECONOMETRICS , 2004, Econometric Theory.

[42]  Jeremy Piger,et al.  The Use and Abuse of Real-Time Data in Economic Forecasting , 2003, Review of Economics and Statistics.

[43]  Jean Boivin,et al.  Monetary Policy in a Data-Rich Environment , 2001 .

[44]  Jörg Breitung,et al.  Dynamic Factor Models , 2006 .

[45]  John Kitchen,et al.  Real-Time Forecasting in Practice: The , 2010 .

[46]  Real-Time Measurement of Business Conditions, Second Version , 2008 .

[47]  Robert Fildes,et al.  Journal of business and economic statistics 5: Garcia-Ferrer, A. et al., Macroeconomic forecasting using pooled international data, (1987), 53-67 , 1988 .

[48]  Estimating Monthly GDP In A General Kalman Filter Framework: Evidence From Switzerland , 1999 .

[49]  K. Carstensen,et al.  IFOCAST: Methoden der ifo-Kurzfristprognose , 2009 .

[50]  R. Kohn,et al.  Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data , 1986 .

[51]  Todd E. Clark,et al.  Tests of Equal Forecast Accuracy and Encompassing for Nested Models , 1999 .

[52]  Gebhard Kirchgaässner,et al.  Monetary Policy and Forecasts for Real GDP Growth: An Empirical Investigation for the Federal Republic of Germany , 1998 .

[53]  Roque B Fernandez,et al.  A Methodological Note on the Estimation of Time Series , 1981 .

[54]  Mark W. Watson,et al.  Advances in Economics and Econometrics: Macroeconomic Forecasting Using Many Predictors , 2003 .

[55]  E. Philip Howrey,et al.  Merging monthly and quarterly forecasts: Experience with mqem , 1991 .

[56]  Stephen K. McNees.  Forecasting accuracy of alternative techniques: A comparison of US macroeconomic forecasts, with comment: Stephen K. McNees, with comment, Journal of Business and Economic Statistics 4 (1986) 5-23 , 1986 .

[57]  Georg Goldrian.  Handbook of Survey-Based Business Cycle Analysis , 2007 .

[58]  Jörg Breitung,et al.  Real-Time Forecasting of GDP Based on a Large Factor Model with Monthly and Quarterly Data , 2007, SSRN Electronic Journal.

[59]  Francis X. Diebold,et al.  Modeling and Forecasting Realized Volatility , 2001 .

[60]  David H. Small,et al.  Nowcasting: the real time informational content of macroeconomic data releases , 2008 .

[61]  E. J. Hannan,et al.  Regression for time series with errors of measurement , 1963 .

[62]  Jeremy Piger,et al.  The Use and Abuse of 'Real-Time' Data in Economic Forecasting , 2000 .

[63]  Melvin J. Hinich,et al.  Time Series Analysis by State Space Methods , 2001 .

[64]  Clifford M. Hurvich,et al.  Regression and time series model selection in small samples , 1989 .

[65]  E. P. Howrey,et al.  The Use of Outside Information in Econometric Forecasting , 1982 .

[66]  P. Hansen,et al.  Realized Variance and Market Microstructure Noise , 2005 .

[67]  T. Crone.  Consistent Economic Indexes for the 50 States , 2002 .

[68]  Robert B. Litterman.  A random walk, Markov model for the distribution of time series , 1983 .

[69]  John W. Galbraith,et al.  Content horizons for univariate time-series forecasts , 2003 .

[70]  E. Ghysels,et al.  Why Do Absolute Returns Predict Volatility So Well , 2006 .

[71]  Eric Ghysels,et al.  Série Scientifique Scientific Series the Midas Touch: Mixed Data Sampling Regression Models the Midas Touch: Mixed Data Sampling Regression Models* , 2022 .

[72]  Steven G. Lanning,et al.  Missing Observations: A Simultaneous Approach versus Interpolation by Related Series , 1986 .

[73]  D. Stram,et al.  A METHODOLOGICAL NOTE ON THE DISAGGREGATION OF TIME SERIES TOTALS , 1986 .

[74]  Jean-Marie Dufour,et al.  Short run and long run causality in time series , 2003 .

[75]  G. Chow,et al.  Best Linear Unbiased Estimation of Missing Observations in an Economic Time Series , 1976 .

[76]  Robert Fildes,et al.  Journal of forecasting 7: Robert F. Engle, Scott J. Brown and Gary Stern, “A comparison of adaptive structural forecasting methods for electricity sales”, (1988) 149–172 , 1989 .

[77]  Mark W. Watson,et al.  Indexes of Coincident and Leading Economic Indicators , 1989 .

[78]  Anindya Banerjee,et al.  Are There Any Reliable Leading Indicators for U.S. Inflation and GDP Growth? , 2003 .

[79]  Preston J. Miller,et al.  A simple way to estimate current-quarter GNP , 1989 .

[80]  Forecasting the Italian industrial production index in real time , 1991 .

[81]  F. Palm,et al.  Parameter identification in ARMA-processes in the presence of regular but incomplete sampling. , 1990 .

[82]  Dean Croushore,et al.  A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter? , 1999 .

[83]  Namwon Hyung.  Linking series generated at different frequencies and its applications , 1998 .

[84]  J. Sandee,et al.  Derivation of Quarterly Figures from Annual Data , 1964 .

[85]  R. C. Merton,et al.  AN INTERTEMPORAL CAPITAL ASSET PRICING MODEL , 1973 .

[86]  A. Maravall,et al.  Estimation, Prediction, and Interpolation for Nonstationary Series with the Kalman Filter , 1994 .

[87]  Michael R. Donihue,et al.  Using mixed frequency data to improve macroeconomic forecasts of inventory investment , 1992 .

[88]  G. Chow,et al.  Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series , 1971 .

[89]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[90]  Marie Diron,et al.  Short-Term Forecasts of Euro Area Real GDP Growth: An Assessment of Real-Time Performance Based on Vintage Data , 2006, SSRN Electronic Journal.

[91]  Bharat Trehan.  Predicting contemporaneous output , 1992 .

[92]  J. Boot,et al.  Further Methods of Derivation of Quarterly Figures from Annual Data , 1967 .

[93]  J. Stock,et al.  A Comparison of Direct and Iterated Multistep Ar Methods for Forecasting Macroeconomic Time Series , 2005 .

[94]  T. Liu,et al.  A Monthly Econometric Model of the U.S. Economy , 1974 .

[95]  Forecasting growth in current quarter real GNP , 1989 .

[96]  On the limitations of comparing mean square forecast errors: Comment , 1993 .

[97]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[98]  Halbert White,et al.  Tests of Conditional Predictive Ability , 2003 .

[99]  Eric Ghysels,et al.  News - Good or Bad - and its Impact on Volatility Predictions over Multiple Horizons , 2008 .

[100]  Stephen G. Hall,et al.  Creating high‐frequency national accounts with state‐space modelling: a Monte Carlo experiment , 2001 .

[101]  The Use of High Frequency Data to Improve Macroeconometric Forecast , 1996 .

[102]  Helmut Lütkepohl,et al.  Forecasting aggregated vector ARMA processes , 1987 .

[103]  Leading indicators for euro area government deficits , 2007 .

[104]  Marta Bańbura,et al.  A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP , 2007, SSRN Electronic Journal.

[105]  George Kapetanios,et al.  An Automatic Leading Indicator of Economic Activity: Forecasting GDP Growth for European Countries , 1999 .

[106]  Preston J. Miller,et al.  Using Monthly Data to Improve Quarterly Model Forecasts , 1996 .

[107]  Siem Jan Koopman,et al.  Time Series Analysis by State Space Methods , 2001 .

[108]  F. Dias,et al.  Determining the number of factors in approximate factor models with global and group-specific factors , 2008 .

[109]  N. Gregory Mankiw,et al.  News or Noise : An Analysis of GNP Revisions , 2007 .

[110]  K. French,et al.  Expected stock returns and volatility , 1987 .

[111]  J. Bai,et al.  Inferential Theory for Factor Models of Large Dimensions , 2003 .

[112]  Massimiliano Marcellino.  Factor nowcasting of German GDP with ragged-edge data : A model comparison using MIDAS projections , 2007 .

[113]  Marco Lippi,et al.  New Eurocoin: Tracking Economic Growth in Real Time , 2007 .

[114]  Fulvio Corsi,et al.  A Simple Long Memory Model of Realized Volatility , 2004 .

[115]  Byeongchan Seong,et al.  Cointegration Analysis with Mixed-Frequency Data , 2007, SSRN Electronic Journal.

[116]  G. Parigi,et al.  Quarterly forecasts of the italian business cycle by means of monthly economic indicators , 1995 .

[117]  Ray C. Fair,et al.  Comparing Information in Forecasts from Econometric Models , 1990 .

[118]  Enno Mammen,et al.  Estimating Semiparametric Arch (∞) Models by Kernel Smoothing Methods , 2003 .

[119]  G. C. Tiao,et al.  Asymptotic behaviour of temporal aggregates of time series , 1972 .

[120]  Russell P. Robins,et al.  Forecasting quarterly data using monthly information , 1993 .

[121]  Fabio H. Nieto.  Ex post and ex ante prediction of unobserved multivariate time series: a structural-model based approach , 2007 .

[122]  Tommaso Proietti,et al.  Temporal Disaggregation by State Space Methods: Dynamic Regression Methods Revisited , 2006 .

[123]  Andrew Harvey,et al.  Estimating Missing Observations in Economic Time Series , 1984 .

[124]  Todd E. Clark,et al.  Approximately Normal Tests for Equal Predictive Accuracy in Nested Models , 2005 .

[125]  Lawrence R. Klein,et al.  Economic forecasting at high-frequency intervals , 1993 .

[126]  Lawrence J. Christiano.  P*: not the inflation forecaster's holy grail , 1989 .

[127]  Luís Catela Nunes,et al.  Nowcasting quarterly GDP growth in a monthly coincident indicator model , 2005 .

[128]  E. Ghysels,et al.  MIDAS Regressions: Further Results and New Directions , 2006 .

[129]  L. Klein,et al.  A Comparison of Eleven Econometric Models of the United States , 1973 .

[130]  Peter Schmidt,et al.  The Theory and Practice of Econometrics , 1985 .

[131]  Phoebus J. Dhrymes,et al.  Distributed Lags: Problems of Estimation and Formulation , 1972 .

[132]  J. Hogrefe.  Forecasting data revisions of GDP: a mixed frequency approach , 2008 .

[133]  Boriss Siliverstovs,et al.  On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence , 2006 .

[134]  Steven Braun,et al.  Estimation of Current-Quarter Gross National Product by Pooling Preliminary Labor-Market Data , 1990 .

[135]  Wolfgang Müller,et al.  Autoregressive Approaches to Disaggregation of Time Series Data , 1971 .

[136]  Glenn D. Rudebusch,et al.  Forecasting Output with the Composite Leading Index: A Real-Time Analysis , 1991 .

[137]  L. Klein,et al.  Combinations of High and Low Frequency Data in Macroeconometric Models , 1989 .