Question

Consider the yearly data of lumber production (in billions of board feet) in the United States given as follows:

year	production
1921	29
1922	35.2
1923	41
1924	39.5
1925	41
1926	39.8
1927	37.3
1928	36.8
1929	38.7
1930	29.4
1931	20
1932	13.5
1933	17.2
1934	18.8
1935	22.9
1936	27.6
1937	29
1938	24.8
1939	28.8
1940	31.2
1941	36.5
1942	36.3
1943	34.3
1944	32.9
1945	28.1
1946	34.1
1947	35.4
1948	37
1949	32.2
1950	38
1951	37.2
1952	37.5
1953	36.7
1954	36.4
1955	37.4
1956	38.2
1957	32.9
1958	33.4
1959	37.2
1960	32.9
1961	32
1962	33.2
1963	34.7
1964	36.6
1965	36.8
1966	36.6
1967	34.7
1968	36.5
1969	35.8
1970	34.7
1971	37
1972	37.7
1973	38.6
1974	34.6
1975	32.6
1976	36.3
1977	39.4
1978	40.5
1979	40.6
1980	35.4
1981	31.7
1982	30

A. Perform necessary analysis to construct an appropriate model for the series and plot the forecasts for the next four years and calculate 95% forecast limits.

Please use RStudio, and also send codes and packages/libraries used, for me to use and review, Thank you!

Answer 1

ans in rstudio with out put for forecasting

 > data=read.csv(file.choose(),header = T) > production=data[,2] > summary(data)  year production Min. :1921 Min. :13.50 1st Qu.:1936 1st Qu.:32.05 Median :1952 Median :35.40 Mean :1952 Mean :33.74 3rd Qu.:1967 3rd Qu.:37.20 Max. :1982 Max. :41.00 >  > times=ts(data$production) > times Time Series: Start = 1 End = 62 Frequency = 1 [1] 29.0 35.2 41.0 39.5 41.0 39.8 37.3 36.8 38.7 29.4 20.0 [12] 13.5 17.2 18.8 22.9 27.6 29.0 24.8 28.8 31.2 36.5 36.3 [23] 34.3 32.9 28.1 34.1 35.4 37.0 32.2 38.0 37.2 37.5 36.7 [34] 36.4 37.4 38.2 32.9 33.4 37.2 32.9 32.0 33.2 34.7 36.6 [45] 36.8 36.6 34.7 36.5 35.8 34.7 37.0 37.7 38.6 34.6 32.6 [56] 36.3 39.4 40.5 40.6 35.4 31.7 30.0 > plot.ts(production) > plot(production) > plot(times) >  > ### To check stationarity > Box.test(production)      Box-Pierce test data: production X-squared = 41.097, df = 1, p-value = 1.449e-10 > adf.test(production) # alternative hypothesis: stationary         Augmented Dickey-Fuller Test data: production Dickey-Fuller = -2.8426, Lag order = 3, p-value = 0.2339 alternative hypothesis: stationary > ## above test we conclude data is stationary >  >  > tsdata=ts (production, start=c(1921),end = c(1982),frequency = 62) > ddata <- decompose(tsdata, "multiplicative")# ddata: decomposition of data > plot(ddata) >  > library(tseries) > library(forecast) >  > myforecast=forecast(tsdata,level = c(95),h=4) > myforecast  Point Forecast Lo 95 Hi 95 1983 35.2 35.2 35.2 1984 41.0 41.0 41.0 1985 39.5 39.5 39.5 1986 41.0 41.0 41.0 > plot.ts(production)