Nowcasting with Dynamic Data Masking and Regularized Regression

Nowcasting with Dynamic Data Masking and Regularized Regression Erik Alpkvist 1 Jonas Hallgren 2 Timo Koski 2 Johannes Siven 1 Ard den Reijer 4 1 GSA Capital London, UK Corresponding and presenting author 2 KTH, Royal Institute of Technology Stockholm, Sweden 4 Sveriges Riksbank (Central Bank of Sweden) Stockholm, Sweden CFE 2015 Dec 12, 2015, University of London 1 / 22

In nowcasting, one wants to forecast a low frequency time series, y, using observations of several higher frequency time series, Z. A typical setting is: y quarterly real GDP growth. Z a broad cross section of higher frequency economic indicators (e.g. weekly and monthly.) Task: Find E[y N Z t ]. 2 / 22

5 4 3 US GDP QoQ Nonfarm payrolls Chicago Purchasing Manager Durable Goods Orders 2 Standardzied observations 1 0 1 2 3 4 5 1980 1985 1990 1995 2000 2005 2010 2015 Time 3 / 22

DATA Release time stamps are essential. ALFRED is used in the study. 4 / 22

MODELS Models are on the form We seek the projections F. y N = w T F(Z t ) + ɛ = w T X + ɛ. Two popular approaches: MIDAS-type and Dynamic Linear Factor models (DLM). The suggested approach is related to MIDAS. 5 / 22

MIDAS Mixed-data sampling (MIDAS) 1 projects the time series onto each quarter F MIDAS (Z t ) = X MIDAS t by e.g. averaging the values. The prediction is ŷ N t = w T X MIDAS t. Fine for historical regression. Forecasting is more difficult due to the ragged edge problem. 1 Ghysels et al. (2004) 6 / 22

MIDAS Z N 2 X N 2 y N 2 Z N 1 X N 1 y N 1 Z t X N y N 7 / 22

DYNAMIC MASKING Z N 2 Z M t N 2 X N 2 y N 2 Z N 1 Z M t N 1 X N 1 y N 1 Z t X N y N 8 / 22

DYNAMIC MASKING Simple idea: Construct regression features dynamically. Similar to MIDAS but the ragged edge problem disappears! Features are constructed for immediate use. The model is updated when data is updated. In both DLM and MIDAS the model is constant throughout the quarter and only data is updated. 9 / 22

ALGORITHM 1. Find shape of the data available today. 2. Mask out unavailable data from old data-vintages. 3. Use MIDAS-type projection of masked data onto each quarter. 4. Regress on the masked data to get w. 5. Predict: ŷ N t = w T F Mask t 10 / 22

ALGORITHM 1. Find shape of the data available today. 2. Mask out unavailable data from old data-vintages. 3. Use MIDAS-type projection of masked data onto each quarter. 4. Regress on the masked data to get w. 5. Predict: ŷ N t = w T F Mask t = w T X Mask t. 11 / 22

TIME t Z N 2 Z M t N 2 X N 2 y N 2 Z N 1 Z M t N 1 X N 1 y N 1 Z t X N y N 12 / 22

TIME t + 1 Z N 2 Z M t+1 N 2 X N 2 y N 2 Z N 1 Z M t+1 N 1 X N 1 y N 1 Z t+1 X N y N 13 / 22

Why Masking? 14 / 22

Why Masking? Theoretical motivation Flexible and easy in implementation Good results 15 / 22

Time t Time t + 1 GDP N 1 NFPAY N 1 GDP N 1 NFPAY N 1 GDP N NFPAY N GDP N NFPAY N USD EUR t USD EUR t+1 p θ (GDP N NFPAY N 1, USD EUR t ) p θ(gdp N NFPAY N ) In general: θ θ! 16 / 22

METHODS Given X Mask we predict ŷ = w T Φ(X Mask ). Regression: Φ is the identity and the estimates are w OLS arg min w y w T F Mask 2, w Ridge arg min w y w T F Mask 2 + λ w 2. Neural Network ŷ = w T Φ NN (X Mask ) = M w k h(θk T XMask ), k=1 where h is the activation function. Kernel methods predict using training data: Φ Kernel (X Mask ) = k(x Mask, X Mask Train ). 17 / 22

0.64 0.62 0.6 US GDP 1995 2010 Masked Ridge Factor model Masked NN Ridge 0.58 RMSE 0.56 0.54 0.52 0.5 0.48 0.46 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Fraction of quarter 18 / 22

WHY MASKING? Theoretical motivation Flexible and easy in implementation Good results 19 / 22

Any remark, question or suggestion is welcomed! jonas@math.kth.se http://people.kth.se/ jhallg/ 20 / 22

REFERENCES Doz, Catherine and Giannone, Domenico and Reichlin, Lucrezia A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1):,188 205, 2011. Kees E Bouwman and Jan PAM Jacobs. Forecasting with real-time macroeconomic data: the ragged-edge problem and revisions. Journal of Macroeconomics, 33(4):784 792, 2011. Eric Ghysels, Pedro Santa-Clara, and Rossen Valkanov. The MIDAS touch: Mixed data sampling regression models. Finance, 2004. Kenneth F Wallis. Forecasting with an econometric model: The ragged edge problem. Journal of Forecasting, 5(1):1 13, 1986. 21 / 22

RAGGED EDGE 3 1,6 are published with lags. 2,3,5 are preliminary. 4 is given: interest rate. Revision can take time: In 2004 US revised the money supply (M2) series from January 1959 onwards 2! 2 Bouwman and Jacobs (2011) 3 Wallis (1986) 22 / 22