Economic and Business Forecasting: Analyzing and Interpreting Econometric Results	7
	Copyright	8
	Contents	11
	Preface	15
	Acknowledgments	19
	Chapter 1: Creating Harmony Out of Noisy Data	21
	Effective Decision Making: Characterize the Data	22
	Part IA: Identifying Trend in a Time Series: GDP and Public Deficits	22
	Part IB: Identifying the Cycle for a Time Series	25
	Part IC: Identifying the Subcycles of Economic Behavior: Use of the HP Filter	31
	Part ID: Spotting Structural Breaks in a Time Series	34
	Part IE: Unit Root Tests	35
	Part IF: Modeling the Cycle	37
	Part IG: Cointegration and Error Correction Model	38
	Part IH: Causality—What Drives What?	40
	Part II: Measuring Volatility: ARCH/GARCH	41
	Part IIA: Forecasting with a Regression Model	42
	Part IIB: Forecasting Recession/Regime Switch as Either/or Outcomes	44
	Part IIC: Forecasting with Vector Autoregression	45
	Part IID: Forecast Evaluation	45
	Chapter 2: First, Understand the Data	47
	Growth: How is the Economy Doing Overall?	50
	Personal Consumption	51
	Gross Private Domestic Investment	53
	Government Purchases	55
	Net Exports of Goods and Services	56
	Real Final Sales and Gross Domestic Purchases	57
	The Labor Market: Always a Core Issue	57
	Establishment Survey	59
	Data Revision: A Special Consideration	62
	The Household Survey	63
	Marrying the Labor Market Indicators Together	68
	Jobless Claims	68
	Inflation	69
	Consumer Price Index: A Society’s Inflation Benchmark	70
	Producer Price Index	73
	Personal Consumption Expenditure Deflator: The Inflation Benchmark for Monetary Policy	75
	Interest Rates: Price of Credit	76
	The Dollar and Exchange Rates: The United States in a Global Economy	78
	Corporate Profits	80
	Summary	82
	Chapter 3: Financial Ratios	83
	Profitability Ratios	84
	Return on Equity	84
	Return on Assets	86
	Corporate Profits as a Percentage of GDP	87
	Liquidity Ratios	87
	Leverage Ratios	90
	Investment Valuation Ratio	92
	Summary	93
	Chapter 4: Characterizing a Time Series	95
	Why Characterize a Time Series?	96
	How to Characterize a Time Series	97
	Putting Simple Statistical Measures to Work	99
	Identifying a Time Trend in a Series	101
	Identifying the Cycle in a Time Series	105
	Testing for a Unit Root	109
	Structural Change: A New Normal?	115
	Separating Cycle and Trend in a Time Series: The Hodrick-Prescott Filter	118
	Application: Judging Economic Volatility	121
	Look at the Data	121
	Putting Simple Statistical Measures to Work	122
	Corporate Profits	125
	Focus on the Labor Market Using Monthly Data	125
	Financial Market Volatility: Assessing Risk	127
	Summary	129
	Chapter 5: Characterizing a Relationship between Time Series	131
	Important Test Statistics in Identifying Statistically Significant Relationships	135
	Level of Significance and p-value	135
	The t-Value or t-Test	136
	The F-Test	136
	R2 and Adjusted R2	137
	White Noise/Autocorrelation Detection Tests	137
	Model Selection Criteria: The AIC and SIC	138
	Simple Econometric Techniques to Determine a Statistical Relationship	139
	Correlation Analysis	139
	Regression Analysis	139
	Advanced Econometric Techniques to Determine a Statistical Relationship	140
	Cointegration Analysis	140
	The Error Correction Model	142
	The Granger Causality Test	143
	The ARCH/GARCH Model	144
	Summary	146
	Additional Reading	147
	Chapter 6: Characterizing a Time Series Using SAS Software	149
	Tips for SAS Users	150
	The Data Step	151
	The Proc Step	155
	Seasonal Adjustment in SAS	156
	Calculating the Mean, Standard Deviation, and Stability Ratio of a Variable	159
	Identifying a Time Trend in a Time Series	162
	Identifying Cyclical Behavior in a Time Series	171
	Summary	176
	Chapter 7: Testing for a Unit Root and Structural Break UsingSAS Software	177
	Testing a Unit Root in a Time Series: A Case Study of the U.S. CPI	178
	Identifying a Structural Change in a Time Series	182
	Testing for a Structural Break: The Dummy Variable Approach	183
	Testing for a Structural Break: The Chow Test	184
	Testing for a Structural Break: The State-Space Approach	186
	The Application of the HP Filter	189
	Application: Benchmarking the Housing Bust, Bear Stearns, and Lehman Brothers	192
	2006: The Housing Bust	192
	2007: Bear Stearns and the Overnight Market for Risk	193
	2008: Lehman and the Financial Crisis	195
	Summary	197
	Chapter 8: Characterizing a Relationship Using SAS	199
	Useful Tips for an Applied Time Series Analysis	199
	Converting a Dataset from One Frequency to Another	202
	The Correlation Analysis	203
	The Regression Analysis	207
	The Cointegration and ECM Analysis	216
	The Error Correction Model	219
	The Granger Causality Test	229
	The ARCH/GARCH Model	231
	Application: Did the Great Recession Alter Credit Benchmarks?	235
	Delinquency Rates: Identifying Change Post-Great Recession	235
	Patterns in Charge-off Rates: Identifying Differences in the Character of Trends	238
	Breakdown of the Monetary Policy Transmission Mechanism	238
	Summary	241
	Chapter 9: The 10 Commandments of Applied Time Series Forecasting forBusiness and Economics	243
	Commandment 1: Know What You Are Forecasting	244
	Commandment 2: Understand the Purpose of Forecasting	246
	Commandment 3: Acknowledge the Cost of the Forecast Error	246
	Symmetric versus Asymmetric Loss Function	247
	Linear versus Nonlinear Loss Function	248
	Commandment 4: Rationalize the Forecast Horizon	249
	Short-Term Forecasting	250
	Long-Term Forecasting	250
	Commandment 5: Understand the Choice of Variables	251
	Commandment 6: Rationalize the Forecasting Model Used	252
	Commandment 7: Know How to Present the Results	254
	Commandment 8: Know How to Decipher the Forecast Results	255
	Commandment 9: Understand the Importance of Recursive Methods	258
	Commandment 10: Understand Forecasting Models Evolve Over Time	259
	Summary	260
	Chapter 10: A Single-Equation Approach to Model-BasedForecasting	261
	The Unconditional (Atheoretical) Approach	262
	The Box-Jenkins Forecasting Methodology	264
	Application of the Box-Jenkins Methodology	265
	The Conditional (Theoretical) Approach	271
	A Case Study of the Taylor Rule	272
	What About Strong Growth?	276
	Recession Forecast Using a Probit Model	277
	Application of the Probit Model	278
	Summary	281
	Chapter 11: A Multiple-Equations Approach to Model-BasedForecasting	283
	The Importance of the Real-Time Short-Term Forecasting	285
	The Individual Forecast versus Consensus Forecast: Is There an Advantage?	286
	The Econometrics of Real-Time Short-Term Forecasting: The BVAR Approach	288
	The Bayesian Vector Autoregression Model	289
	Forecast Evaluation: Real-Time Measures	291
	A SAS Application of the BVAR Approach: A Case Study of the Employment Forecast	294
	Forecasting in Real Time: Issues Related to the Data and the Model Selection	295
	The Functional Form of the Variables	296
	The Selection of the Best Model Specification	297
	Timing of the Release: A Dependent Variable and Predictors	298
	Case Study: WFC versus Bloomberg	300
	Summary	308
	Appendix 11A: List of Variables	309
	Chapter 12: A Multiple-Equations Approach to Long-TermForecasting	311
	The Unconditional Long-Term Forecasting: The BVAR Model	313
	The BVAR Model with Housing Starts	316
	The Model without Oil Price Shock	318
	A Small-Scale Macro Model: Equation 12.1	321
	The Model with Oil Price Shock	324
	Summary	326
	Chapter 13: The Risks of Model-Based Forecasting: Modeling, Assessing,and Remodeling	327
	Risks to Short-Term Forecasting: There is No Magic Bullet	328
	Risks of Long-Term Forecasting: Black Swan versus a Group of Black Swans	330
	Model-Based Forecasting and the Great Recession/Financial Crisis: Worst-Case Scenario versus Panic	334
	Summary	335
	Chapter 14: Putting the Analysis to Work in the Twenty-First-CenturyEconomy	337
	Benchmarking Economic Growth	338
	Benchmarks: Economic Growth and the Labor Market	339
	Testing, Not Assuming, Economic Values for Good Decision Making	339
	Our Benchmark for Real GDP Growth: 2.75 Percent	340
	Industrial Production: Another Case of Stationary Behavior	342
	Employment: Jobs in the Twenty-first Century	344
	Unemployment Rate Measured by U-3: A Surprising Result of Stationarity	345
	Employment Growth: Surprisingly Stationary Despite Impressions	346
	The Beveridge Curve: Yet to Shift Inward	349
	Structural Change in the U.S. Labor Market: Two Illustrations	350
	Inflation	351
	Inflation and Inflation Expectations	352
	Inflation: A (Small) Bias to the Upside	353
	Interest Rates	357
	Inflation and Real Yields: A Signal of Financial Imbalances	358
	Imbalances between Bond Yields and Equity Earnings	358
	Healthy Bond Issuance Consistent with Functioning Credit Market Expansion	360
	Two-Year Treasury Yield: Benchmark for the Short End of Yield Curve	361
	Adjusting the Two-Year Treasury Yield to Achieve Stationarity	363
	10-Year Treasury Yields: Not Mean Reverting	364
	A Note of Caution on Patterns of Interest Rates	365
	Business Credit: Patterns Reminiscent of Cyclical Recovery	367
	Profits	368
	Corporate Profits: Surprising Stability	368
	Financial Market Volatility: Assessing Risk	369
	Dollar	371
	Dollar Exchange Rate: A Strong Dollar Is Not the Real Story	372
	Volatility in the Dollar over Time	372
	Economic Policy: Impact of Fiscal Policy and the Evolution of the U.S. Economy	373
	Large and Persistent Deficits: A Brave New World of Fiscal Policy	374
	Budget Limits with 2.75 Percent Trend Economic Growth	376
	The Long-Term Deficit Bias and Its Economic Implications	378
	Interest Rates Trend Reversal: Test to Come Ahead	378
	Credit Imbalance: The U.S. Treasury Market	379
	Summary	382
	Appendix: Useful References for SAS Users	385
	About the Authors	387
	Index	389