Cover image: © Ekspansio/iStockphoto, elly99/iStockphoto Cover design: Andrew Liefer Copyright © 2014 by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard All rights reserved Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the Web at www.copyright.com Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose No warranty may be created or extended by sales representatives or written sales materials The advice and strategies contained herein may not be suitable for your situation You should consult with a professional where appropriate Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 7622974, outside the United States at (317) 572-3993 or fax (317) 572-4002 Wiley publishes in a variety of print and electronic formats and by print-on-demand Some material included with standard print versions of this book may not be included in e-books or in print-on-demand If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com For more information about Wiley products, visit www.wiley.com Library of Congress Cataloging-in-Publication Data: Silvia, John Economic and business forecasting : analyzing and interpreting econometric results / John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, Sam Bullard pages cm (Wiley & SAS business series) ISBN 978-1-118-49709-8 (cloth); ISBN 978-1-118-56980-1 (ebk); ISBN 978-1-118-56954-2 (ebk) Economic forecasting Business forecasting Decision-making Econometrics I Title HB3730.S484 2014 330.01'5195—dc23 2013039764 Printed in the United States of America 10 To Tiffani Kaliko, Penny and Sherman Shahkora and Mohammad Iqbal, Nargis, Saeeda, Shahid and Noreen And to the family and friends who remain our wellsprings of inspiration If a man will begin with certainties, he shall end in doubts, but if he will content to begin with doubts, he shall end in certainties —Francis Bacon, The Advancement of Learning1605 Contents Preface Acknowledgments Chapter Creating Harmony Out of Noisy Data Effective Decision Making: Characterize the Data Chapter First, Understand the Data Growth: How Is the Economy Doing Overall? Personal Consumption Gross Private Domestic Investment Government Purchases Net Exports of Goods and Services Real Final Sales and Gross Domestic Purchases The Labor Market: Always a Core Issue Establishment Survey Data Revision: A Special Consideration The Household Survey Marrying the Labor Market Indicators Together Jobless Claims Inflation Consumer Price Index: A Society's Inflation Benchmark Producer Price Index Personal Consumption Expenditure Deflator: The Inflation Benchmark for Monetary Policy Interest Rates: Price of Credit The Dollar and Exchange Rates: The United States in a Global Economy Corporate Profits Summary Chapter Financial Ratios Profitability Ratios Summary Chapter Characterizing a Time Series Why Characterize a Time Series? How to Characterize a Time Series Application: Judging Economic Volatility Summary Chapter Characterizing a Relationship between Time Series Important Test Statistics in Identifying Statistically Significant Relationships Simple Econometric Techniques to Determine a Statistical Relationship Advanced Econometric Techniques to Determine a Statistical Relationship Summary Additional Reading Chapter Characterizing a Time Series Using SAS Software Tips for SAS Users The DATA Step The PROC Step Summary Chapter Testing for a Unit Root and Structural Break Using SAS Software Testing a Unit Root in a Time Series: A Case Study of the U.S CPI Identifying a Structural Change in a Time Series The Application of the HP Filter Application: Benchmarking the Housing Bust, Bear Stearns, and Lehman Brothers Summary Chapter Characterizing a Relationship Using SAS Useful Tips for an Applied Time Series Analysis Converting a Dataset from One Frequency to Another Application: Did the Great Recession Alter Credit Benchmarks? Summary Chapter The 10 Commandments of Applied Time Series Forecasting for Business and Economics Commandment 1: Know What You Are Forecasting Commandment 2: Understand the Purpose of Forecasting Commandment 3: Acknowledge the Cost of the Forecast Error Commandment 4: Rationalize the Forecast Horizon Commandment 5: Understand the Choice of Variables Commandment 6: Rationalize the Forecasting Model Used Commandment 7: Know How to Present the Results Commandment 8: Know How to Decipher the Forecast Results Commandment 9: Understand the Importance of Recursive Methods Commandment 10: Understand Forecasting Models Evolve over Time Summary Chapter 10 A Single-Equation Approach to Model-Based Forecasting The Unconditional (Atheoretical) Approach The Conditional (Theoretical) Approach Recession Forecast Using a Probit Model Summary Chapter 11 A Multiple-Equations Approach to Model-Based Forecasting The Importance of the Real-Time Short-Term Forecasting The Individual Forecast versus Consensus Forecast: Is There an Advantage? The Econometrics of Real-Time Short-Term Forecasting: The BVAR Approach Forecasting in Real Time: Issues Related to the Data and the Model Selection Case Study: WFC versus Bloomberg Summary Appendix 11A: List of Variables Chapter 12 A Multiple-Equations Approach to Long-Term Forecasting The Unconditional Long-Term Forecasting: The BVAR Model The BVAR Model with Housing Starts The Model without Oil Price Shock The Model with Oil Price Shock Summary Chapter 13 The Risks of Model-Based Forecasting: Modeling, Assessing, and Remodeling Risks to Short-Term Forecasting: There Is No Magic Bullet Risks of Long-Term Forecasting: Black Swan versus a Group of Black Swans Model-Based Forecasting and the Great Recession/Financial Crisis: Worst-Case Scenario versus Panic Summary Chapter 14 Putting the Analysis to Work in the Twenty-First-Century Economy Benchmarking Economic Growth Industrial Production: Another Case of Stationary Behavior Employment: Jobs in the Twenty-First Century Inflation Interest Rates Imbalances between Bond Yields and Equity Earnings A Note of Caution on Patterns of Interest Rates Business Credit: Patterns Reminiscent of Cyclical Recovery Profits Financial Market Volatility: Assessing Risk Dollar Economic Policy: Impact of Fiscal Policy and the Evolution of the U.S Economy The Long-Term Deficit Bias and Its Economic Implications Summary Appendix: Useful References for SAS Users About the Authors Index Preface Due to the Great Recession (2007–2009) and the accompanying financial crisis, the premium on effective economic analysis, especially the identification of time series and then accurate forecasting of economic and financial variables, has significantly increased Our approach provides a comprehensive yet practical process to quantify and accurately forecast key economic and financial variables Therefore, the timing of this book is appropriate in a post-2008 world, where the behavior of traditional economic relationships must be reexamined since many appear out of character with the past The value proposition is clear: The framework and techniques advanced here are the techniques we use as practitioners These techniques will help decision makers identify and characterize the patterns of behavior in key economic series to better forecast these essential economic series and their relationships to other economic series This book is for the broad audience of practitioners as well as undergraduate and graduate students with an applied economics focus This book introduces statistical techniques that can help practitioners characterize the behavior of economic relationships Chapters to provide a review of basic economic and financial fundamentals that decision makers in both the private and public sectors need to know Our belief is that before an analyst attempts any statistical analysis, there should be a clear understanding of the data under study Chapter provides the tools that an analyst will employ to effectively characterize an economic series One relationship of interest is the ability of leading indicators to predict the pattern of the business cycle, particularly the onset of a recession Another way to characterize economic relationships is to reflect on the current trend of any economic series of interest relative to the average behavior over prior cycles In a third approach, we may be interested in identifying the possibility of a structural change in an economic time series to test if the past history of a variable would be different over time Different economic and financial variables exhibit differential behavior over the business cycle and over time In this book we focus on a select set of major economic and financial variables, such as economic growth, final sales, employment, inflation, interest rates, corporate profits, financial ratios, and the exchange value of the dollar Our analysis then extends the text into the relationships between different time series This analysis begins with Chapter 5, and then in Chapters and we take a look at the SAS® software employed in our analysis We also examine these variables' patterns over the business cycle, with an emphasis (ARIMA), 17–18, 23–24 Availability bias, 331 Average forecast error, 236–237 Bayesian vector autoregression (BVAR): case study, 274–275 efficacy of, 278 evaluation of, 271–274 Great Recession and, 309 housing starts and, 296–298, 299–300 overview of, 263, 268–271 unconditional forecasting and, 293–298 Bear Stearns and overnight market for risk, 173–175 Benchmarking economic growth, 318–321 Beveridge curve, 75, 329–331 BG-LM (Breusch-Godfrey serial correlation LM) test, 117–118 Bias: anchoring, 3, 14, 318–319, 325–326, 337, 346–347, 361 availability, 331 confirmation, 12–13, 322–324, 327 deficit, 358 housing market and, 346–347 interest rate expectations and, 337 normalization of deviance, 348 overconfidence, 354 recency, 9–10, 14, 64, 326–327, 331–332 sunk cost, 347 Binomial (either/or) outcomes, 24–25 B-J method See Box-Jenkins (B-J) forecasting method Black swans, 311–314 Bloomberg real-time consensus forecast, 263, 267, 280–288, 309 Bond yields and equity earnings, imbalances between, 338–345 Box-Jenkins (B-J) forecasting method: application of, 245–250 steps in, 244–245 Box-Pierce Q-statistic (Q_BP), 88–89 Breusch-Godfrey serial correlation LM (BG-LM) test, 117–118 Bubble forecast, 225 Budget limits, 356–357 Business credit, patterns of, 347 Business cycle: division of data into, 142 long-term forecasting and, 230–231 macroeconomic variables and, 292, 310–311 response to macroeconomic news and, 286–287 stages in, BVAR (Bayesian vector autoregression): case study, 274–275 efficacy of, 278 evaluation of, 271–274 Great Recession and, 309 housing starts and, 296–298, 299–300 overview of, 263, 268–271 unconditional forecasting and, 293–298 Cash for Clunkers program, 309–310 Cash ratio, 68–69 Causality and correlation, 182 Causality test, 20–21 See also Granger causality test CDS (credit default swap) premiums, 338–340 Census effect, 310 Characteristics of time series: cycles in, 85–89 judging economic volatility, 101–109 means for, 77–79 overview of, 75–76 purpose of, 76–77 separating cycle and trend, 98 simple statistical measures for, 79–81 structural breaks in, 95–98 testing for unit root, 89–95 trends in, 81–85 Characterization of data: causality, 20–21 cointegration and error correction model, 18–20 cycles in time series, 5–11 modeling cycles, 17–18 overview of, structural breaks in time series, 14–15 subcycles of economic behavior, 11–14 trends in time series, 2–5 unit root tests, 15–17 Charge-off rates, patterns in, 218, 219 Chow test: overview of, 14–15 with SAS software, 164–166 testing for structural breaks in time series, 98 Coefficient, standard error of, 146–147, 190 Cointegration: Engle-Granger test, 121–122, 197–199 Johansen test, 121–122, 202–206 overview of, 18–20, 114, 196 Cointegration analysis, 120–122 Conditional forecasting model: long-term forecasting, 293 with oil price shock, 304–306 overview of, 241–242, 251–252 strong growth and, 256–257 Taylor rule case study, 252–256 without oil price shock, 298, 300–304 Confirmation bias, 12–13, 322–324, 327 Consensus forecast compared to individual forecast, 266–268 Consumer price index (CPI), 50–53, 333–334 Consumption, government-financed, 361 Controlled forecasting experiments, 238–239 Core CPI, 52 Corporate profits: as key economic indicator, 60–62 as percentage of GDP, 67 stability of, 348–349, 350 volatility of, 105, 106 Correlation analysis: causality and, 182 for determining statistical relationships, 119, 120 growth rates of variables of interest in, 186–187 overview of, 113 with SAS software, 183–187 Cost: of forecast error, 226–229 of variables, 231–232 Coverage ratio, 70 CPI (consumer price index), 50–53, 333–334 Credit benchmarks and Great Recession, 215–221 Credit default swap (CDS) premiums, 338–340 Credit imbalance, 359–362 Credit markets, functioning of, 340–341 Current ratio, 68, 69 Cycle for time series: identifying, 5–11, 85–89 identifying with SAS software, 151–156 modeling, 17–18 Cyclical component in financial ratios, 64 Data See also Characterization of data; Key economic indicators availability and release timing of, 278–280 descriptive statistics of, 77, 79–81, 102–105, 139–142, 143 inputting into statistical software, 179–180 plotting, 77–79, 101–102 real-time forecasting and, 275–277 revising, 42–43 understanding, 27–29 Dataset: choice of, 231 converting from one frequency to another, 182–183 DATA step of time series analysis, 131–135 Debt ratio, 70 Debt-to-equity ratio, 70–71 Deficit bias, 358 Deficits: credit imbalance and, 359–362 interest rates and, 358–359 large and persistent, 354–356 Deflation, 50 Degrees of freedom for error (DFE), 146 Delinquency rates on loans, 215–218 Density forecast, 234, 235 Dependent variable: functional form of, 276–277, 289 overview of, 231 release timing of, 278–280 selection of, for short-term forecasting models, 277–278 traditional and nontraditional forms of, 242 Descriptive statistics of data: calculating in PROC step of time series analysis, 139–142, 143 for characterizing time series, 79–81, 102–105 overview of, 77 DFE (degrees of freedom for error), 146 DF (Dickey-Fuller) test, 91, 92–94 Difference stationary (DS) behavior, 91, 93, 343 Directional accuracy: in deciphering results, 236–237 in forecast evaluation, 273–274 forecast evaluation and, 25 Disinflation, 50 Dollar and exchange rates, 58–60, 351–353 DS (difference stationary) behavior, 91, 93, 343 Dummy variable approach, 14–15, 163–164 Durable goods, 31 Durbin ‘h’ test, 192–193 Durbin-Watson “d” test, 117–118, 146, 192 ECM See Error correction model Econometrics: applied research compared to, of real-time short-term forecasting, 268–275 Economic growth See Employment growth; Growth Economic indicators See Key economic indicators Economic policy: budget limits, 356–357 deficit bias in, 358 impact of, 353–354 large and persistent deficits, 354–356 Economic recovery, trends in, 1–2, 317–318 Economic trends and financial ratios, 64 E-G See Engle-Granger (E-G) cointegration test Either/or (binomial) outcomes, 24–25 Employment forecast, 274–275 Employment growth: Beveridge curve and, 329–331 economic recovery and, 1, 317–318 overview of, 324–325 as stationary, 326–328 unemployment rate, 325–326 Employment-population ratio, 330 Employment Situation Summary: establishment survey, 39–43 household survey, 43–48 Endogenous break date, 98 Engle, Robert, 120–121 Engle-Granger (E-G) cointegration test: for determining statistical relationships, 121–122 overview of, 20 with SAS software, 197–199 Equity earnings and bond yields, imbalances between, 338–345 Error correction model (ECM) See also Vector error correction model for determining statistical relationships, 122–123 overview of, 18–20, 114 with SAS software, 199–202 Establishment (payroll) survey, 39–43 Estimating time trends, 84–85 Euro crisis, 358 Evaluating forecasts, 25, 271–274 Exchange rates, 58–60, 351–353 Exponential trend, 82–84 FIML (Full Information Maximum Likelihood), 149 Financial crisis, 175–177 See also Great Recession Financial leverage, 70 Financial ratios: investment valuation, 72–73 leverage, 70–71 liquidity, 67–69 overview of, 63–64, 73 profitability, 64–67 First-order autocorrelation, detecting, 192–193 Fiscal policy See Economic policy Fixed investments, 33–34 Forecast error: cost of, 226–229 nature of, 307 representing by time series, 235–236 Forecasting See also Long-term forecasting; Model-based forecasting; Short-term forecasting acknowledging cost of errors, 226–229 deciphering results, 235–238 knowing objective, 224–225 models for, as evolving over time, 239–240 overview of, 223–224, 240 presenting results, 234–235 rationalizing horizon, 229–231 rationalizing model used, 232–233 understanding cost of variables, 231–232 understanding purpose of, 226 understanding recursive methods, 238–239 F-test, 116 Full Information Maximum Likelihood (FIML), 149 Functional form of variables, 276–277, 289 GARCH (generalized autoregressive conditional heteroskedasticity), 21–22, 125–126 GDP See Gross domestic product GNP (gross national product), 30 Godfrey LM test of autocorrelation, 193–194 Gold prices, 334, 335, 336 Government consumption, 35–36 Granger, Clive, 120–121 Granger causality test: for determining statistical relationships, 123–124 overview of, 21, 114 with SAS software, 209–211 Great Recession: credit benchmarks and, 215–221 depth of, 296 housing sector and, 298 long-term forecasting and, 311–314 model-based forecasting and, 314–315 Okun's law and, 242 performance of models and, 309 Gross domestic product (GDP): benchmarking, 320–321 corporate profits as percentage of, 67 government consumption and, 35–36 gross private domestic investment and, 33–35 identifying trend in time series and, 2–5 net exports and, 36–37 overview of, 30–31 personal consumption and, 31–32 real final sales and, 37, 38 relationship to unemployment rate, 75–76 trends in, 317 Gross national product (GNP), 30 Gross private domestic investment, 33–35 Group of black swans, 311, 312–314 Growth See also Employment growth; Gross domestic product benchmarking, 318–321 conditional forecasting model and, 256–257 indicators of, 30–31 of labor force, 45–46 labor market and, 38 in productivity, structural periods of, 96 Heteroskedasticity, 115 Higher-order autocorrelation, detecting, 194–196 Hodrick-Prescott (HP) filter: identifying subcycles with, 11–14 recency bias and, 64 with SAS software, 169–171 to separate cycle and trend in time series, 98–101 Homoskedasticity, 124 Horizon for forecast, rationalizing, 229–231 Household survey, 43–48 Housing market See also S&P/Case-Schiller home price index bust, as structural break, 172–173 distortions in, 346–347 starts, 296–298, 299–300 Housing-related data, forecasting from, 287 HP filter See Hodrick-Prescott (HP) filter Identification problem, 294, 301 Importing datasets into SAS, 133–134 Independent variables, 231, 232 Individual forecast compared to consensus forecast, 266–268 Industrial production, 322–324 Industrial Production (IP) data, 311–314 Inflation: bias to upside and, 333–337 consumer price index and, 50–53 inflation expectations and, 332–333, 334, 335 overview of, 49–50, 331–332 personal consumption expenditure deflator and, 55–56, 112–113 producer price index and, 53–55 Initial jobless claims, 245 I (d) notation, 243 Integration (I), 18 Interest rates: patterns of, 345–347 as price of credit, 56–58, 337–338 on Treasury securities, 341–345 trend reversal, 358–359 Interval forecast, 234, 235 Inventories: private, changes to, 34–35 real final sales and, 37, 38 Investment spending, 33–35 Investment valuation ratio, 72–73 IP (Industrial Production) data, 311–314 Job growth and economic recovery, 1, 317–318 See also Employment growth Jobless claims, 48–49, 245 Johansen cointegration test: maximum, 205–206 overview of, 20, 121–122 with SAS software, 202–206 trace, 202–205 Key economic indicators: consumer price index, 50–53 corporate profits, 60–62 dollar and exchange rates, 58–60 establishment survey, 39–43 government purchases, 35–36 gross domestic purchases, 37 gross private domestic investment, 33–35 growth, 30–31 household survey, 43–48 inflation, 49–56 interest rates, 56–58 jobless claims, 48–49 labor market, 37–38 net exports, 36–37 overview of, 27–29 personal consumption, 31–33 personal consumption expenditure deflator, 55–56 producer price index, 53–55 real final sales, 37, 38 KISS principle, 233 Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) test: described, 91–92 for identifying unit root, 16 SAS software and, 161–162 of time series, 95 Labor force participation rate, 46–48, 330–331 Labor market: attention to, 37–38 as benchmark, 319–321 change in character of, 324–331 combining indicators, 48 establishment survey, 39–43 household survey, 43–48 jobless claims, 48–49, 245 nonfarm payrolls forecasts, 280–284, 326–328 unemployment rate and, 27–28, 29, 43–45, 75–76, 325–326 volatility of, 105–107 Lag order, finding, 194–196 Large-scale macro models, limitations of, 293–294 Lehman Brothers, 175–177, 315 Level of significance, 115 Leveraged buy-outs, 340–341 Leverage ratios, 70–71 LIBOR-OIS spreads, 175–177 Linear loss functions, 228–229 Linear trends: estimating, 145 overview of, 3–4 reliability of forecasts and, 81 Liquidity ratios, 67–69 Litterman prior, 269–270, 294 Ljung-Box Q-Statistic (Q_LB), 88–89 LM test of autocorrelation, 193–194 Loan delinquency rates, 215–218 Log-difference form of variables, 180–181 Logistic regression model, 242, 257–261 Log-linear trend, 82–84 Long-term forecasting: conditional model with oil price shock, 304–306 conditional model without oil price shock, 298, 300–304 overview of, 230–231, 291–293, 306 risks related to, 307–308, 310–314 Loss functions: formula for, 227 linear and nonlinear, 228–229 overview of, 226 symmetric and asymmetric, 227–228 Lucas, Robert E., 345 MA (moving average), 18 Macroeconomic news announcements: business cycle and response to, 286–287 revisions to, 287–288 volatility and, 265–266 MAE (mean absolute error), 25, 146 MA (q) notation, 243 MAPE (mean absolute percentage error), 146 Maximum likelihood (ML) method, 168 Maximum test of cointegration, 202–203, 205–206 Mean: calculating in PROC step of time series analysis, 139–142, 143 overview of, 79 Mean absolute error (MAE), 25, 146 Mean absolute percentage error (MAPE), 146 Mean reverting, series as, 319–321 Mean square error (MSE), 146 Measuring volatility: forecast evaluation, 25 forecasting recession/regime switch as either/or outcomes, 24–25 forecasting with regression model, 22–24 forecasting with vector autoregression, 25 overview of, 21–22 Minnesota prior, 268, 269, 294 Model-based forecasting: case study, 280–288 conditional approach, 251–257, 293, 298, 300–306 data and model selection, 275–280 Great Recession and, 314–315 individual compared to consensus, 266–268 overview of, 241–243, 261–262, 263–265 phases of, 308, 315 Probit (logistic regression) model, 24–25, 242, 257–261 real-time short-term, 265–266 risks of, 307–308 unconditional approach, 242–250, 293–298, 299–300 Modeling cycle for time series, 17–18, 154–156 Models See also Model-based forecasting; specific models as evolving over time, 239–240 large-scale macro, limitations of, 293–294 monitoring performance of, 308–309 selection criteria, 118–119, 275, 277–278 Monetary policy, inflationary bias of, 332 Monetary policy transmission mechanism, 218–221 Money neutrality, 75, 111–112, 113 Moving average (MA), 18 MSE (mean square error), 146 Multiple-equations forecasting: case study, 280–288 conditional model with oil price shock, 304–306 conditional model without oil price shock, 298, 300–304 data and model selection, 275–280 individual compared to consensus, 266–268 long-term, 291–293, 306 overview of, 263–265 real-time short-term, 265–266 National Income and Product Accounts (NIPA), 60–62 Net exports of goods and services, 36–37 Nondurable goods, 31–32 Nonfarm payrolls forecasts, 280–284, 326–328 Nonlinear loss functions, 228–229 Nonlinear trends, 3–4, 147 Nonstationarity, 81, 91, 343 Nonstationary time series, 89–90 Objective of forecasts: knowing, 224–225 techniques based on, 232–233 Observations, time periods for, 180, 182–183 Oil price shock: conditional model with, 304–306 conditional model without, 298, 300–304 Okun's law, 75, 76, 111, 242 Operating leverage, 70 Ordinary least squares (OLS) analysis, 157, 196 Outcome uncertain, timing certain forecast, 224–225 Overconfidence bias, 354 Overnight market for risk, 173–175 Panics, 315 Partial autocorrelation function (PACF), 86–89 Payroll (establishment) survey, 39–43 PCE (personal consumption expenditure) deflator, 55–56, 112–113 Pearson correlation coefficient, 184–185 Performance of models, monitoring, 308–309 Personal consumption, 31–32, 104–105 Personal consumption expenditure (PCE) deflator, 55–56, 112–113 Phillips curve, 111, 240 Phillips-Perron (PP) test: for identifying unit root, 16–17 overview of, 91–92 SAS software and, 160 of time series, 94–95 Plotting data versus time, 77–79, 101–102 Point forecast, 234, 235 Policy changes and long-term forecasting, 292, 311 PPI (producer price index), 53–55 PP test See Phillips-Perron (PP) test Predictors in forecast model, 251 See also Variables Presenting forecast results, 234–235 Prices: consumer price index, 50–53 producer price index, 53–55 Price-to-earnings (P/E) ratio, 72–73 Probability forecast, 234–235 Probit model, 24–25, 242, 257–261 PROC step of time series analysis: calculating volatility, 139–142, 143 identifying cyclical behavior, 151–156 identifying time trend, 142, 144–151 overview of, 131, 135–136 seasonal adjustment, 136–138 Producer price index (PPI), 53–55 Productivity growth, structural periods of, 96 Profitability ratios, 64–67 Profits: corporate, 60–62 overview of, 348 as percentage of GDP, 67 stability of, 348–349, 350 volatility of, 105, 106 Public sector deficits, 2–5 Purpose of forecasting, 226 P-values, 115, 119 Q_BP (Box-Pierce Q-statistic), 88–89 Q_LB (Ljung-Box Q-Statistic), 88–89 Quadratic trend model, 82 Quadratic (nonlinear) trends, 3–4, 137 Quick ratio, 68 R , 117 Random walk model, 93, 158 Random walk with drift model, 93, 159 Rationalizing: forecast horizon, 229–231 forecast model, 232–233 Real final sales, 37, 38, 104–105 Real-time short-term forecasting: BVAR approach, 268–275 comparison of methods for, 280–288 data and model selection, 275–280 importance of, 265–266 overview of, 263–264, 288 Real yields and inflation, 338 Recency bias, 9–10, 14, 64, 326–327, 331–332 Recession/regime switch, forecasting, 24–25 Recessions See also Great Recession dating of, 39 forecasting, 257–261 Recursive methods, 238–239 Regression analysis: autocorrelation tests and, 192–196 for determining statistical relationships, 119–120 forecasting with, 22–24 overview of, 113 with SAS software, 187–190 spurious regression, 190–192 using OLS, 196 Relationship characterization with SAS software: application, 215–221 ARCH/GARCH model, 211–215 cointegration and ECM analysis, 196–209 converting dataset from one frequency to another, 182–183 correlation analysis, 183–187 Granger causality test, 209–211 overview of, 179, 221–222 regression analysis, 187–196 Relationships between time series See also Relationship characterization with SAS software additional reading on, 127 ARCH/GARCH model, 124–126 cointegration analysis, 120–122 correlation analysis, 119 error correction model, 122–123 F-test, 116 Granger causality test, 123–124 level of significance and p-value, 115 model selection criteria, 118–119 overview of, 111–115, 126 R , 117 regression analysis, 119–120 t-value, 116 white noise/autocorrelation detection tests, 117–118 Results of forecasting: deciphering, 235–238 presenting, 234–235 Return on assets (ROA), 66–67 Return on equity (ROE), 64–66 Revisions to macroeconomic variables, 287–288 Risk: of leveraging activity, 341 of long-term forecasting, 310–314 of model-based forecasting, 307–308 overnight market for, 173–175 of short-term forecasting, 308–310 variance as proxy for, 211 volatility and, 107–108, 349–351 RMSE (root mean squared error): in deciphering results, 236–238 in forecast evaluation, 271–273 forecast evaluation and, 25 simulated out-of-sample, 292–293 ROA (return on assets), 66–67 ROE (return on equity), 64–66 Root mean squared error See RMSE SAS software See also PROC step; Relationship characterization with SAS software application, 172–177 ARCH/GARCH approach and, 126 asterisks and semicolons, 133 Box-Jenkins method, 245–250 BVAR approach, 274–275, 295–298 conditional model with oil price shock, 304–306 conditional model without oil price shock, 301–304 correlation coefficient, 119 DATA step, 131–135 estimating time trends, 84–85 Granger causality test, 124 HP filter and, 169–171 identifying cycles in time series, 86–87 identifying structural breaks, 162–169 Macro variable, 132–135 modifying data, 134–135 naming conventions, 131–132 OUTLIER tool, 166 overview of, 129–130, 156 Probit model application, 258–261 PROC ARIMA command, 151–156 PROC AUTOREG command, 120, 144–145, 149, 187–188 PROC CORR command, 184 PROC CORR Data keywords, 136 PROC EXPAND command, 170–171, 182 PROC EXPORT command, 171 PROC IMPORT Datafile keywords, 133–134 PROC LOGISTIC command, 260 PROC MEANS command, 139–142 PROC MODEL command, 149–151, 302 PROC VARMAX command, 202–203, 209–210, 274 PROC X12 command, 136–138 p-values, 115 references for users of, 365 SCAN method, 154–156 Taylor rule case study, 252–256 testing for structural breaks in time series, 97–98 testing unit root, 158–162 tips for, 130–131, 179–182 SBC See Schwarz Bayesian criterion Scenario-based analysis, 235, 251–252, 315 Schwarz Bayesian criterion (SBC): for characterizing time series, 147–149 to determine autocorrelation order, 194–196 formula for, 151 for model selection, 146 Schwarz information criterion (SIC), 118–119, 182 Seasonal adjustment in SAS, 136–138 Services: net exports of, 36–37 personal consumption of, 32 Short-term forecasting See also Conditional forecasting model; Probit Unconditional forecasting model BVAR approach, 268–275 comparison of methods for, 280–288 data and model selection, 275–280 evaluating performance of, 240 long-term forecasting compared to, 292 methods for, 262 overview of, 230 real-time, 263–264, 265–266, 288 risks related to, 307, 308–310 SIC (Schwarz information criterion), 118–119, 182 Single-equation forecasting: conditional approach, 251–257 overview of, 241–242, 261–262 Probit (logistic regression) model, 257–261 unconditional approach, 242–250 Small-scale macro model, 301–304 Software, 129–130 See also SAS software Solvency ratios, 70–71 S&P/Case-Schiller home price index (HPI), 96–97, 162, 172–173, 307–308, 313 Spurious regression, 190–192 Stability ratio: calculating in PROC step of time series analysis, 139–142, 143 of dollar, 352–353 overview of, 79, 80, 104 Stabilization policy, 345–346 Standard deviation: calculating in PROC step of time series analysis, 139–142, 143 overview of, 79, 80, 104 Standard error of coefficient, 146–147, 190 State-space approach: example of, 173–175 overview of, 14–15 to testing for structural breaks, 166–169 Stationarity, adjusting two-year Treasury yield to achieve, 343–344 Stationary time series, 89–90 Statistical relationships between time series See Relationships between time series Statistical significance, determining, 115–119 Strategic vision, need for, 318 Structural breaks in time series: Bear Stearns and, 173–175 black swans and, 311–312 Chow test for, 164–166 dummy variable approach to testing for, 163–164 housing bust as, 172–173 model; identifying, 95–98 identifying with SAS software, 162–169 Lehman Brothers and, 175–177 methods to identify, 157 overview of, 14–15 state-space approach to testing for, 166–169 Structural model, 233 Sunk cost bias, 347 Symmetric loss functions, 227–228 Taylor rule, 251, 252–256 TED spreads, 57–58, 173–175 Ten-year Treasury yields, 344–345 Testing See also Unit root tests for autocorrelation, 117–118, 192–196 for causality, 20–21, 114, 123–124, 209–211 Chow test, 14–15, 98, 164–166 for cointegration, 121–122, 197–199, 202–206 for structural breaks in time series, 97–98 for time trend, 82–84 Theoretical forecasting approach See Conditional forecasting model Theory, relying on for long-term forecasting models, 292–293 Time series See also Characteristics of time series; Relationship characterization with SAS software; Relationships between time series behavior of, 76–77 cycle for, 5–11 as mean reverting, 319–321 modeling cycle for, 17–18 structural breaks in, 14–15 trends in, 2–5 Time trend: estimating, 84–85 identifying, 2–5, 81 identifying with SAS software, 142, 144–151 testing for, 82–84 Timing uncertain, outcome known forecast, 225 Trace test of cointegration, 202–205 Trade, net exports of goods and services, 36–37 Trade-weighted dollar index, 352 Treasury market, 359–362 Treasury yields: inflation and, 338 ten-year, 344–345 two-year, 341–344 Trend stationary (TS) behavior, 91, 93, 343–344 T-test (t-value), 116, 190 Two-year Treasury yields, 341–344 Unconditional forecasting model: Box-Jenkins method, 244–250 BVAR approach and, 293–298, 299–300 overview of, 241–243 Underemployment, 331 Unemployment rate: household survey and, 43–45 overview of, 27–28, 29 relationship to GDP, 75–76 as stationary, 325–326 Unfunded liabilities of governments, 354–356, 361 Unit root problem, 113–114 Unit root tests: overview of, 15–17 purpose of, 157 with SAS software, 158–162 for time series, 89–95 Univariate forecasting, 241 VAR See Vector autoregression VAR/BVAR approach, 263 Variables: cost of, 231–232 functional form of, 276–277, 289 log-difference form of, 180–181 release timing of, 278–280 selection of for short-term forecasting models, 277–278 VECM See Vector error correction model Vector autoregression (VAR) See also Bayesian vector autoregression forecasting with, 25 multiple-equations forecasting and, 263 overview of, 269, 294 uses of, 233 Vector error correction model (VECM): overview of, 123 with SAS software, 206–209 Trace test and, 202, 203–205 Velocity, decline in, 335–337 VIX (volatility index), 176, 177 Volatility See also Measuring volatility ARCH/GARCH model and, 211–215 calculating in PROC step of time series analysis, 139–142, 143 in dollar over time, 352–353 of financial data series, 115 interpreting, judging, 101–109 macroeconomic news announcements and, 265–266 risk and, 107–108, 349–351 Volatility index (VIX), 176, 177 White noise detection tests, 117–118, 244–245 Worst-case scenarios, 315 Y ield curves, plotting, 56–57 ... The 10 Commandments of Applied Time Series Forecasting for Business and Economics Commandment 1: Know What You Are Forecasting Commandment 2: Understand the Purpose of Forecasting Commandment 3:... Silvia, John Economic and business forecasting : analyzing and interpreting econometric results / John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, Sam Bullard pages cm (Wiley & SAS business. .. Forecast Error Commandment 4: Rationalize the Forecast Horizon Commandment 5: Understand the Choice of Variables Commandment 6: Rationalize the Forecasting Model Used Commandment 7: Know How