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Business and Economic ForecastingMohammad Arief

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Overview

Demand Forecasting is a critical managerial activity

Quantitative Qualitative

Gives the precise amount or percentage

Gives the expected direction Up, down,

or about the same

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THE SIGNIFICANCE OF FORECASTING

Uncertainty Conditions

Predicting changes in cost, price, sales, and interest rates

limited

Accurate forecasting can help develop strategies to promote profitable trends and to avoid unprofitable ones

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SELECTING A FORECASTING TECHNIQUE

The forecasting technique used in any particular situation depends on a number of factors.a. Hierarchy of Forecastsb. Criteria Used to Select a Forecasting

Techniquec. Evaluating the Accuracy of Forecasting

Models

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Hierarchy of Forecasts

• The highest level of economic aggregation that is normally forecast is that of the national economy (GDP, interest rates, inflation, etc).»Sectors of the economy (durable

goods) Industry forecasts (all auto

manufacturers)> Firm forecasts (Ford Motor Company)

» Product forecasts (The Ford Focus)

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Forecasting Criteria

The choice of a particular forecasting method depends on several criteria:1. costs of the forecasting method compared with

its gains2. complexity of the relationships among

variables3. time period involved4. accuracy needed in forecast5. lead time between receiving information and

the decision to be made

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Accuracy of Forecasting• The accuracy of a forecasting model is measured by how

close the actual variable, Y, ends up to the forecasting variable, Y.

• Forecast error is the difference. (Y - Y)• Models differ in accuracy, which is often based on the

square root of the average squared forecast error over a series of N forecasts and actual figures

• Called a root mean square error, RMSE.

»RMSE = { (Y - Y)2 / N }

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The smaller the value of the RMSE, the greater the accuracy

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ALTERNATIVE FORECASTING TECHNIQUES

1. Deterministic trend analysis2. Smoothing techniques3. Barometric indicators4. Survey and opinion-polling techniques5. Macroeconometric models6. Stochastic time-series analysis

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1. Deterministic trend analysis

a. Time-series data, A sequence of the values of an economic variable at different points in time.

b. Cross-sectional data An array of the values of an economic variable observed at the same time, like the data collected in a census across many individuals in the population.

Secular trends

Cyclical variations

Seasonal effects

Random fluctuations

These are long-run trends that cause changes in an economic data series

These are major expansions and contractions in an economic series that are usually greater than a year in duration

Seasonal variations during a year tend to be more or less consistent from year to year.an economic series may be influenced by random factors that are unpredictable

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Secular trends (Trend Sekuler)

• Forecasting model trend sekuler dilakukan dengan menarik garis secara kasar atau serampang mengikuti kecenderungan permintaan yang terjadi secara siklus dari tahun ke tahun.

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Cyclical variations (Fluktuasi Siklus)

• Siklus perubahan atau naik turunnya volume permintaan selama tahun-tahun yang telah lalu dan yang akan dating,kita tarik kecenderungannya tentu disebabkan atau dipengaruhi oleh sejumlah faktor yang secara periodik dan tetap harus ada atau terjadi selam periode tahunan yang akan datang.

• Biasanya siklus bisa kita duga sebelumnya bahwa dengan datangnya permintaan yang meningkat pada periode tertentu sudah bisa kita prediksi kejadiannya.

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Seasonal effects(Metode Variasi Musim)

• Melakukan prakiraan volume permintaan konsumen di waktu-waktu yang akan datang dapat didasarkan pada gelombang musiman yang melekat pada kultur budaya atau kebiasaan dari masyarakat.

• Tetapi dapat juga karena faktor sifat dan keadaan alam yang melekat pada iklim atau cuaca. Misalnya produksi musim semi, gugur, dan musim hujan bahkan musim kemarau.

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Random fluctuations (Fluktuasi Siklus)

• Siklus perubahan atau naik turunnya volume permintaan selama tahun-tahun yang telah lalu dan yang akan dating,kita tarik kecenderungannya tentu disebabkan atau dipengaruhi oleh sejumlah faktor yang secara periodik dan tetap harus ada atau terjadi selam periode tahunan yang akan datang

• Biasanya siklus bisa kita duga sebelumnya bahwa dengan datangnya permintaan yang meningkat pada periode tertentu sudah bisa kita prediksi kejadiannya.

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Secular, Cyclical, Seasonal, and Random Fluctuations in Time Series Data

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Elementary Time Series Models for Economic Forecasting

1. Naïve Forecast

Yt+1 = Yt» Method best when there is

no trend, only random error

» Graphs of sales over time with and without trends

» When trending down, the Naïve predicts too high

NO Trend

Trend

^

time

time

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2. Naïve Forecast With Adjustments for Secular Trends

Yt+1 = Yt + (Yt - Yt-1 )»This equation begins with last period’s

forecast, Yt. »Plus an ‘adjustment’ for the change in the

amount between periods Yt and Yt-1.»When the forecast is trending up, this

adjustment works better than the pure naïve forecast method #1.

^

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3. Linear and 4. Constant growth rate

• Used when trend has a constant amount of changeYt = a + b•T, whereYt are the actual observations andT is a numerical time variable

• Used when trend is a constant percentage rateLog Yt = a + b•T,where b is the continuously compounded growth rate

Linear Trend Growth Uses a Semi-log Regression

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2. SMOOTHING TECHNIQUES• Smoothing techniques are another type of

forecasting model, which assumes that a repetitive underlying pattern can be found in the historical values of a variable that is being forecast.

• Smoothing techniques work best when a data series tends to change slowly from one period to the next with few turning points.

Yt+1 = [Yt + Yt-1 + Yt-2]/3

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Direction of sales can be indicated by other variables.

TIME

Index of Capital Goods

peakPEAK Motor Control Sales

4 Months

Example: Index of Capital Goods is a “leading indicator”There are also lagging indicators and coincident indicators

Qualitative Forecasting3. Barometric Techniques

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Qualitative Forecasting4. Surveys and Opinion Polling

Techniques

• Sample bias—» telephone, magazine

• Biased questions—» advocacy surveys

• Ambiguous questions• Respondents may lie on

questionnaires

New Products have NOhistorical data — Surveyscan assess interest in newideas.

Common Survey Problems

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Qualitative ForecastingExpert Opinion

The average forecast from several experts is a Consensus Forecast.» Mean» Median» Mode» Proportion positive or negative

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5. Econometric Models

• Specify the variables in the model• Estimate the parameters

» single equation or perhaps several stage methods

»Qd = a + b•P + c•I + d•Ps + e•Pc• But forecasts require estimates for future prices,

future income, etc.• Often combine econometric models with time series

estimates of the independent variable.»Garbage in Garbage out

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6. Stochastic Time Series• A little more advanced methods incorporate into time

series the fact that economic data tends to driftyt = a + byt-1 + et

• In this series, if a is 0 and b is 1, this is the naïve model. When a is 0, the pattern is called a random walk.

• When a is positive, the data drift. The Durbin-Watson statistic will generally show the presence of autocorrelation, or AR(1), integrated of order one.

• One solution to variables that drift, is to use first differences.