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Constructing Leading Economic Indicators for the Philippine Economy using Dynamic Factor Models Dennis Mapa, Joselito Magadia, Manuel Leonard Albis School of Statistics University of the Philippines, Diliman 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Outline of the Presentation I. II. The GDP and LEIS Mixed Frequency Models for GDP a. Model 1: Hybrid DFM-VAR b. Model 2: DF-Mixed Frequency Model c. Forecasting Performance IV. Conclusion 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City The Gross Domestic Product • Gross Domestic Product (GDP), published by the Philippine Statistics Authority (PSA), is the broadest measure of the overall economic activity • The official GDP estimates are released by the PSA about 60 days after the reference quarter for the 1st, 2nd and 3rd Quarters and 30 days after for the 4th Quarter • This delay is the reason why researchers are interested in alternative methodologies to provide insights on the “real time economic activity” using economic indicators that are available at a higher frequency (e.g. monthly, weekly, daily) than the quarterly GDP 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City The Leading Economic Indicator • A timely assessment on the movements of the GDP is important to be able to guide policy makers to come up with appropriate policies to mitigate, say the impact of a shock • Leading Economic Indicator System (LEIS), developed jointly by the PSA and the National Economic and Development Authority (NEDA) • It provides a one-quarter-ahead forecast of the movement of the GDP and seeks to answer the question whether the GDP is expected to go up or go down in the succeeding quarter 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Challenges Encountered • Short Time Series Horizon - Practitioners have hundreds of series at their disposal, although most of them are not desirably long enough (e.g. 20 to 40 years of quarterly data) • Mismatched frequencies of data – Available economic indicators have different frequencies, i.e. CPI is reported on a monthly basis, exchange rate reported daily, and GDP reported quarterly • Aggregation – How to create a composite index of economic movement from incomplete and mismatched frequencies of data? 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Objectives of Study • Provide alternative models to temporal aggregation by proposing a multi-frequency model. • Specifically, estimate a model with quarterly series on the left-hand side (GDP growth) and monthly economic indicators as explanatory variables (right-hand side) • To develop and evaluate potential models in nowcasting both the movements and the growth rates of the country’s GDP: 1. Hybrid Dynamic Factor-Vector AutoRegressive (DFVAR) model 2. Dynamic Factor-Mixed Frequency (DF-MF) model 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Contributions to the LEIS • Interaction of multi-frequency variables can be assessed without resorting to data aggregation (e.g. monthly exports can be used to predict intra-quarter value of GDP) • A multi-frequency model can generate monthly forecasts (nowcast) using monthly economic variables. 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City II. Mixed Frequency Models for GDP 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Indicators of Economic Movement Variable Gross Domestic Product (Official Seasonally Adjusted Series) National Government Expenditures National Government Revenues Government Spending under GFCE (COE less Interest Payments less Subsidy) Public Construction Spending (Infrastructure & other capital outlays + Capital transfers to LGUs) Exports Exchange Rate Gross International Reserves Imports Terms of Trade Peso/Euro exchange rate Peso/SGD exchange rate Peso/yen exchange rate Remittances (Cash) 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Indicators of Economic Movement Variable PSEI Consumer Price Index Deposit rate: Savings Dubai Crude Libor 3m M2: Money Supply Manufacturing: Value of Production Index Retail Sale: Price of Rice (Regular-milled) Sibor 3M Tbill rate:364 Tbill rate: 91 Time deposit rate (Long-term) Time deposit rate(Short-term) 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Indicators of Economic Movement Variable Visitor arrival Wholesale Price Index Meralco Sales Bank Average Lending Rate UKB Loans Outstanding Registered Stock Corporations and Partnership Business Expectation Survey (Current Quarter) Business Expectation Survey (Next Quarter) Consumer Expectation Survey (Current Quarter) Consumer Expectation Survey (Next Quarter) 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Preliminary Tests: Unit Root and Seasonality • Prior to building the models, the time series data are tested for the presence of unit root/s using the Augmented Dickey-Fuller and the Dickey-FullerGeneralized Least Squares tests • For series with seasonality, the corresponding seasonally-adjusted values may be generated using the X-13/12 procedure • Corresponding transformations were applied on the series if necessary 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Model 1: Hybrid DFM-VAR 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Hybrid Dynamic Factor VAR Model • The procedure closely follows the approach of Chow and Choy (2009) in analyzing business cycles in Singapore that used Principal Components Analysis in the extraction of the latent dynamic factors • The Hybrid DFM-VAR model involves a two-step process: Step 1: Generate the dynamic factors using Principal Components Analysis (PCA) – basically to reduce the dimension of the data Step2 : Run the Vector AutoRegressive (VAR) Model using the aggregated factors, additional “stand alone” variables, and GDP 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Factors and Stand Alone Variables • The PCA suggested six factors generally named as: (1) Prices, (2) Exchange Rates, (3) Interbank Offered Rates, (4) Investments, (5) Trade, (6) Spending • Three stand-alone variables were selected: (1) Remittances, (2) Visitors, (3) Business Expectations Survey outlook Factor 1 WPI, Price of Dubai Crude, CPI, Price of Rice Factor 2 Exchange Rates: Yen, Euro, USD, SGD Factor 3 LIBOR, SIBOR TBILL 364, TBILL, Short-Term TD Rate, SEC Registered Corporations, Manufacturing Value of Production Index Terms of Trade, Exports, Money Supply, Imports, PSEI Factor 4 Factor 5 Factor 6 National Government Expenditure, Public Construction Spending Government Spending 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Vector Autoregressive Model • The approach of Sims (1980) was to view all variables as endogenous • Each endogenous variables by regressing its past values and the past values of the other variables in the system • VAR(1) 𝐺𝐷𝑃𝑡 = 𝑎10 + 𝑎11 𝐺𝐷𝑃𝑡−1 + 𝐚12 𝐗 𝑡−1 + 𝑒1𝑡 Model: 𝐗 𝑡 = 𝐚20 + 𝑎21 𝐺𝐷𝑃𝑡−1 + 𝐚22 𝐗 𝑡−1 + 𝑒2𝑡 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Performance of Hybrid DFM-VAR • Percentage of correctly predicted movement of GDP (seasonally adjusted): 77% • Mean Error in Forecasting GDP growth (seasonally adjusted): 0.38 percentage point 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Model 2: DF-Mixed Frequency Model 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City The DF-Mixed Frequency Model • The procedure closely follows the approach of Gerlach and Yiu (2004) in nowcasting the GDP of Hong Kong • The DFM-Mixed Frequency model involves a three-step process: Step 1: Determine the potential variable groupings using PCA Step 2: Extract the dynamic factors from each of the variable groupings using State-Space DFM Step 3: Estimate a State-Space Time Varying Model for GDP 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City State-Space Model • The extraction of the latent dynamic factors and the prediction of GDP was done using the State-Space model • The State-space model (Kalman 1960) is a general timeseries model for expressing dynamic systems that involve unobserved state variables • A state-space model consists of two equations: – Measurement Equation: Describes the relation between observed variables (data) and unobserved state variables – Transition Equation: Describes the dynamics of the state variables; it has the form of a first-order difference equation in the state vector 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City State-Space Model: Dynamic-Factor Model 𝑦1𝑡 𝑦2𝑡 𝑐𝑡 𝑧1𝑡 𝑧2𝑡 Measurement Equation 𝑐𝑡 𝛾1 1 0 𝑧 = 1𝑡 , 𝑦𝑡 = 𝐻𝑡 𝛽𝑡 𝛾2 0 1 𝑧 2𝑡 Transition Equation 𝑐𝑡−1 𝑣𝑡 𝜙1 0 0 = 0 𝛼1 0 𝑧1,𝑡−1 + 𝑒1𝑡 , 𝛽𝑡 = 𝑒2𝑡 0 0 𝛼2 𝑧2,𝑡−1 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City State-Space Model: Time-Varying-Parameter Model Measurement Equation 𝐺𝐷𝑃𝑡 = 𝐹1𝑡 𝜙1 0 𝛽1𝑡 𝛽2𝑡 = 0 𝜙2 … ⋮ ⋮ 𝛽𝑘𝑡 0 0 𝐹2𝑡 … 𝐹𝑘𝑡 𝛽1𝑡 𝛽2𝑡 + 𝑒𝑡 , 𝑦𝑡 = 𝑥𝑡 𝛽𝑡 + 𝑒𝑡 ⋮ 𝛽𝑘𝑡 Transition Equation 𝑣1𝑡 … 0 𝛽1,𝑡−1 𝑣2𝑡 … 0 𝛽2,𝑡−1 + ⋮ , 𝛽𝑡 = 𝜇 + 𝐹𝛽𝑡−1 + 𝑣𝑡 ⋱ ⋮ ⋮ 𝑣𝑘𝑡 … 𝜙𝑘 𝛽𝑘,𝑡−1 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Predicting GDP Growth Month QTR 2001Q1 2001Q2 2001Q3 2001Q4 Seasonally Adjusted GDP 0.3 1.2 1.2 0.3 2001JAN Data Conversion 2001FEB 2001MAR 2001APR GDP growth rates are treated 2001MAY 2001JUN as a monthly 2001JUL series with 2001AUG missing 2001SEP observations 2001OCT 2001NOV 2001DEC 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Seasonally Adjusted GDP . . 0.3 . . 1.2 . . 1.2 . . 0.3 The Kalman Filter Algorithm will estimated the missing GDP values from the dynamic factors and stand-alone variables The estimated model is also used in forecasting Performance of DF-Mixed Frequency • Percentage of correctly predicted movement of GDP (seasonally adjusted): 94% • Mean Error in Forecasting GDP growth (seasonally adjusted): 0.59 percentage point 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Conclusions • This paper proposes models in nowcasting the movement and growth rates of the country’s quarterly Gross Domestic Product (GDP) using 32 monthly variables and 1 quarterly indicator, in addition to the one- and two-factor DF model • The DF-VAR and DF-MF are alternative models to the usual time series econometric models used in forecasting GDP growth rates utilizing temporal aggregation • The assessment of the models, in terms of the percentage of correctly tracking the movement of the GDP, suggests that hybrid models are promising as nowcasting tools and can serve as alternative (and better) models to the current LEIS used by the NEDA and PSA. 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City Acknowledgements • The authors acknowledge the support of the National Economic and Development Authority (NEDA) and the Bangko Sentral ng Pilipinas (BSP) for this research • The author is indebted to the research support provided by the members of the Poverty and Hunger Research Laboratory of the School of Statistics, University of the Philippines. 13th National Convention on Statistics October 3-4, 2016, EDSA Shangri-La Hotel, Mandaluyong City