Index

adaptive smoothing, 60

add-in tools of Excel, 15–16

adjustments, 5

based on error evaluations, 51–52

Excel applications, 53–55

holdout-sample evaluations and, 55–56

arbitrage pricing theory (APT) model, 182–183

Aristotle, 243

associative analyses, 6, 86, 158, 173

autocorrelation, 100–101, 103–104, 107–108, 110–111, 127

autoregressive (AR) models, 244

concept, 158–160

Excel applications, 168–169

interval forecasts, 162–163

point forecasts, 160–161

standard error of the regression, 162–163

autoregressive integrated moving average (ARIMA) models, 158, 244

concept, 163–164

Excel applications, 166–167

testing for stationarity, 164–166

average consumer expectations for business conditions, 225

average forecast value, 59

average seasonal index (AS), 145–146

average weekly hours in manufacturing sector, 221–222

Bass model, 189–191

best linear unbiased estimators (BLUE), 87

bond markets. See also stock markets

expected yield to maturity (YTM) of a bond, 178–180

interest rate forecasting (IRF), 180

Box–Jenkins procedure, 164

Brown’s DE, 74–76

business forecast models. See diffusion models on sales and demand; financial forecasting; operational forecasting

capital asset pricing model (CAPM), 181–182

Excel applications, 187–188

cautions on forecast results

confusion between precision and accuracy, 247

global financial conditions, 248

mistaking correlation for causality, 246

overestimation or underestimation of forecast values, 247

theory-less forecasting, 247

center moving average (CMA), 145

central tendency, measures of, 8–11. See also probability

charting tools of Excel, 13–15

composite index, 6

composite seasonal index, 145–146

constant price, 35

consumer price index (CPI), 36–37

continuous variable, 7–8

convergence (or catching up), 231–236

at future level, 233–234

required growth rate to keep a constant gap, 235–236

required growth rate to reach a target, 234–235

covariance, 9

credit index, 224

critical value, 57–58

cross-sectional data, 12, 13

logarithm of zero and, 250

missing observations and, 249

current prices, 35

cyclical-random index, 147–148

data analyses, 4–5

changing units, 250

cleaning of data, 249

missing observations, 249–250

preliminary steps of, 251

data collection, 4–5, 248–249

data manipulations in Excel, 16–17

data randomness, 56–58

decomposition

calculating trend-random values (TR_t), 146–147

concept, 144

constructing a composite seasonal index, 145–146

constructing seasonal-random indices for individual periods, 144–145

cyclical component in, 147–148

Excel applications, 148–152

obtaining forecast values, 147

steps in, 144

three-component, 144

Delphi method, 6

de-randomizing process, 154

descriptive statistics, 18–20, 251

de-seasoning process, 154

determinants of land use, 211

deviation in percentage (DPE) measures, 46–47

diagnostic checking, 164

Dickey–Fuller test, 164, 165

diffusion indices, 225–228. See also turning point, forecasting

Excel applications, 228–231

diffusion models on sales and demand, 188–194. See also financial forecasting; operational forecasting

Bass model, 189–191

Excel application, 193–194

Lawrence–Lawton model, 191–193

discrete variable, 7

dispersion of a distribution, 9

dividend discount model (DDM), 183–184

double exponential smoothing (DE), 74–81, 244. See also Exponential smoothing (ES)

Brown’s approach, 74–76

Excel applications, 78–81

forecasts against actual demand, 80–81

Holt’s approach, 76–78

double moving averages (DMs), 65–74

calculate MAs, 66, 67

equation for one-period forecasts using, 67

Excel applications, 72–74

forecast error, 67–68

multiperiod forecasts, 70–72

trend line in, 68–70

early adopters, 190

early majority, 190

econometric model(s), 86–87

cross-sectional regressions, 116

linear regression analysis, 86–87

multiple linear regressions, 115–116

economic indicators, 6

economic models. See input–output demands model; production–allocation model

endogeneity, 128–129

error measurements

concept, 42–43

evaluations, 5

error. See Error measurements

holdout-sample, 55–56

Excel applications

add-in tools, 15–16, 206

adjustments, 53–55

autocorrelation, 107–108, 110–111

autoregressive (AR) models, 168–169

autoregressive integrated moving average (ARIMA) models, 166–167

capital asset pricing model (CAPM), 187–188

charting tools, 13–15

composite seasonal index, obtaining, 151

converting nominal values to real values, 39

cross-sectional data for regression analysis, 91–93, 120–122

cyclical component values, obtaining, 152

data manipulations, 16–17

data operations, 12–13

decomposition, 148–152

descriptive statistics, performing, 18–20

diffusion indices, 228–231

diffusion models on sales and demand, 193–194

double exponential smoothing (DE), 72–74

DPE, 50–51

expected yield to maturity (YTM) of a bond, 185–186

exponential smoothing (ES), 34–35

FB, 49

forecast values, obtaining, 151

formating cells, 17–18

GDPD calculation, 38–39

gravity models, 215–216

heteroskedasticity, 106–110

higher-order exponential smoothing (HOE), 156–158

Holt–Winters Exponential Smoothing, 155–156

interest rate forecasting (IRF), 186–187

interval forecasts, 83–84

investment choices, 237–240

MAE, 49

MAPE, 49–50

mathematical operations in, 11–12

MSE, 49

multiple linear regressions, 120–123, 131–134

operational forecasting, 176–177

panel-data forecasting, 137–140

production–allocation model, 205–210

SDF, 48–49

seasonal-random indexes for individual quarters, constructing, 150

simple MA, performing, 29–30

stock markets, 187–188

time-series data for regression analysis, 93–94, 122–123

trend-random values, calculating, 151

triple exponential smoothing, 155–158

weighted MA, performing, 30–31

expectation concept, 8–9

expected yield to maturity (YTM) of a bond, 178–180

Excel applications, 185–186

exponential smoothing (ES), 23, 244. See also Double exponential smoothing (DE)

calculating an average values, 32

concept, 31–34

equation of forecasts, 31–32

Excel application, 34–35

next-period forecast and current-period forecast, 33

obtaining forecast values, 34–35

F-critical value, 125–126

financial forecasting, 177–188, 196–197. See also diffusion models on sales and demand; operational forecasting

bond markets, 178–180

stock markets, 181–184

Fisher, M.E., 243

fixed effect estimators 135-136

focus forecasting, 60

forecast bias (FB) measures, 47

forecasting

basic steps in, 4–5

defined, 3–4

methods, 4, 253–256. See also cautions on forecast results

forecast models

combining, 58–60

with a quadratic term, 154

forecast of forecast technique, 91, 174

forecast results, cautions on

confusion between precision and accuracy, 247

global financial conditions, 248

mistaking correlation for causality, 246

overestimation or underestimation of forecast values, 247

theory-less forecasting, 247

F-statistic, 126, 139

F-tests, 124–125, 137

future information for forecasting, 4

future period, 4

Gauss–Markov theorem, 87

generalized least squares (GLS), 102

Global Indicator Program, 248

good forecaster, 5

goodness of fit, 130

gravity models, 210–216

Excel applications, 215–216

land-use forecasting, 211–212

trip distribution forecasts, 212–215

gross domestic product (GDP), 35

gross domestic product (GDP) deflator (GDPD), 36–38

heteroskedasticity, 98–100, 102–103, 106–110, 127, 129–130

higher-order exponential smoothing (HOE), 154–155

Excel applications, 156–158

Holt’s DE, 76–78

Holt–Winters exponential smoothing (HWE), 153–154

Excel applications, 155–156

individual judgment, 5

innovators, 190

input–output demands model, 199, 203–205. See also production–allocation model

applications, 204–205

Institute for Supply Management (ISM) index, 222–223

interest rate forecasting (IRF), 180

Excel applications, 186–187

interest rate spread of 10-year Treasury Less Federal Funds Target, 224–225

interval forecasts

concept, 81–83

Excel application, 83–84, 94–96

for t-distribution for a sample of N observations, 89

interval prediction, 90

investment choices, 236–237

Excel applications, 237–240

Kurtosis, 10–11

laggards, 190

Lagrange Multiplier (LM) test, 99, 101

land-use forecasting, 211–212

late majority, 190

Lawrence–Lawton model, 191–193

least squares dummy variable (LSDV) estimation, 135, 139

left-skewed (negatively skewed) distribution, 10

left-skewed sample, 10

left-tail test, 97–98

linear optimization, 200

linear regression analysis, 85, 244–245. See also multiple linear regressions

assumptions, 87

econometric models, 86–87

estimations, 88–89

evaluations and adjustments, 96–111

Excel operations, 91–94

forecasts, 90–91

of personal consumption (CONS), 86

predictions, 89–90

for time-series data, 87

logarithm of zero, 250

log-log model, 101

longitudinal (panel) data, 12

manufacturers’ new orders

of consumer goods and materials, 222

for nondefense capital goods, 223

mean absolute deviation (MAD), 44

mean absolute error (MAE), 44–45

approximations, 82

mean absolute percentage error (MAPE), 45–46, 188, 243

mean squared error (MSE), 44, 59

modified Hausman test for endogeneity, 132–133

moving averages (MA), 23

concept, 26

moving average of order one MA(1), 163

pros and cons, 243

simple, 26–28

using Excel, 29–31

weighted (WA), 28, 243

multicollinearity problem, 116

multiperiod forecasts, 91

multiple linear regressions. See also linear regression analysis

for cross-sectional data, 116

econometric model, 115–116

evaluations and adjustments, 123–134

Excel applications, 120–123, 131–134

forecasts, 119–120

interval estimates, 118

multiple regression model specifications, 130–131

of personal consumption (CONS), 115–116

point estimates, 117

predictions, 118–119

time-series data, 117, 122–123

negative bias, 47

Newton’s law of gravitational attraction, 210–211

nominal values, 35–36, 39

nonregression analysis, 6

normal distribution, 8

one-period interval forecasts, 91

operational forecasting. See also diffusion models on sales and demand; financial forecasting

Excel application, 176–177

running forecast technique, 174–176

ordinary least squares (OLS) estimation, 87, 88, 137

pooled, 134

panel-data forecasting

concept 134-135

detecting different characteristics, 137

Excel applications, 137–140

fixed effect estimators 135-136

panel of experts method, 5

point estimates, 88

pooled OLS estimation, 134

positive bias, 47

predictions, 89–90

preliminary steps of data analysis, 251

probability, 7–8. See also central tendency, measures of

of throwing a coin, 7–8

probability distribution function (pdf), 7–8

problem of endogenous regressors, 128

production–allocation model, 199–203. See also input–output demands model

constraints, 201–202

Excel applications, 205–210

linear function, 201

production limits, 202

p-value, 104

qualitative forecasting, 5–6

quantitative forecasting, 6–7

rate of change, estimation. See diffusion indices

rates of change, models on

convergence (or catching up), 231–234

real gross domestic product (RGDP), 220, 228

real values, 35–36, 39

regression analysis, 6

right-skewed (positively skewed) distribution, 10

right-skewed sample, 10

right-tail test, 97

running forecast technique, 174–176

run test, 56–57

R₂ value measures, 104–105

seasonal-random indices for individual periods, 144–145

serial correlation, 100

simple linear regressions. See linear regression analysis

simple moving averages (MA), 26–28

using Excel, 29–30, 53–55

skewness, 10

smoothing constant, 32

smoothing factor, 33–34

standard deviation (SD), 10

of forecast (SDF) measures, 43–44

standard error of the forecast, 82, 83, 90, 105–106, 162–163

standard error (SE), 10

Standard & Poor’s 500 (S&P 500), 224

τ-statistic (tau-statistic), 164

stock markets. See also Bond markets

arbitrage pricing theory (APT) model, 182–183

capital asset pricing model (CAPM), 181–182

dividend discount model (DDM), 183–184

Excel applications, 187–188

student’s GPA, 8–9

t-distribution, 89

test statistic, 57–58

three-component decomposition, 144

three-parameter exponential smoothing, 154

Thurman, W.N., 243

time series analyses, 6, 56

cyclical component of, 24–25

Excel application, 93–94

missing observations, 250

random component of, 24–25

seasonal component of, 24–25

trend component of, 23–24

time series data, 12

trend-random values (TR_t), 146–147

trip distribution forecasts, 212–215

triple exponential smoothing

concept, 153–154

Excel applications, 155–158

higher-order exponential smoothing (HOE), 154–155

Holt-Winters exponential smoothing (HWE), 153–154

t-statistics, 164–165

t-test, 96, 97, 101, 126

turning point, forecasting. See also diffusion indices

average consumer expectations for business conditions, 225

average weekly hours in manufacturing sector, 221–222

building permits and new houses, 223–224

concept, 220–225

credit index, 224

Institute for Supply Management (ISM) index, 222–223

interest rate spread of 10-year Treasury Less Federal Funds Target, 224–225

manufacturers’ new orders

of consumer goods and materials, 222

for nondefense capital goods, 223

measures of unemployment, 222

Standard & Poor’s 500 (S&P 500), 224

two stages least squares (2SLS) estimators, 129

two-tail test, 98

unemployment, measures of, 222

unit-root test, 164

variance measures, 9

weighted forecast value, 59

weighted moving averages (WA), 28, 243

using Excel, 30–31, 53–55

Z-critical value of a normal distribution, 57–58, 82

zero covariance, 9