rats dialog interface

Preparing the Data 1

RATS Dialog Interface

WINDOWS:It is most convenient to use RATS using two windows: one for input and the other for output. When you begin RATS, the ACTIVE, OUTPUT, and INPUT are identical. This Window is NONAME00.TXT If you open a second window without closing NONAME.TXT the new window will become the ACTIVE window, but not the input or output window. If yow want to use this new window for input and/or output, use the Window-Use for Input or the Window-Use for Output commands listed on the WINDOW menu. The Window-Tile command, makes the multiple windows visible. You can switch between windows using the mouse, menu or using <CTRL F6>


RATS Dialog Interface: II

LOCAL MODE: <Ctrl L>Allows you to enter commands or instructions, without execution taking place. Thus, you can use RATS as a wordprocessor. This is a toggle: the second <Ctrl L> returns you to ready mode. SELECT TEXT: CUT, COPY, PASTE:Use the mouse or use the SHIFT key in conjunction with the arrow keys or the PgUp or PgDn keys to mark text. Marked text can be cut, pasted, or copied using the EDIT menu. Note that <CTRL X> is equivalent to Cut, <CTRL C> is equivalent to Copy, and <CTRL V> is equivalent to Paste. COMPOUND INSTRUCTIONS:Certain instructions have supplementary cards (e.g., the GRAPH Command). If you make an error on a supplementary card, you must reenter the entire instruction from the beginning.


RATS Dialog Interface: III

ONLINE HELP:You can get online help from the HELP Pull-Down Menu. You can get help on the specific RATS Instructions using the F1 key. PRINTING GRAPHS: To print a graph, you use the PRINT command from the FILE menu. RATS will print only the material in the currently shown window. As such, the graph window must be shown on the screen. SAVING YOUR WORK:It is possible to save your program on to a disk using the SAVE or the SAVE AS command on the FILE menu. As such, you will not have to retype your commands the next time you use RATS. Simply use the OPEN command from the FILE menu to begin your next session. (Be sure the new program is in the active window!).


Preparing the Data The file labeled MONEY_DEM.XLS contains quarterly values of seasonally adjusted U.S. nominal GDP, real GDP in 1996 dollars (RGDP), the money supply as measured by M2 and M3, and the 3-month and 1-year treasury bill rates for the period 1959:1 – 2001:1. Both interest rates are expressed as annual rates and the other variables are in billions of dollars

cal 1959 1 4all 2001:1open data c:\money_dem.xlsdata(org=obs,format=xls)


Calendarcalendar year period frequencyorcal(q) 1959:1 where: year The year of the first entry in the data set period The period of the first entry in the data set

frequency The number of observations per year.Examples:For monthly data beginning with February 1973, use calendar 1973 2 12.For semiannual data beginning with July 1973, use calendar 1973 2 2.

For annual data it is permissible to use only the starting year. As such, you can omit the period and frequency and use the more compact calendar 1973 instead of calendar 1973 1 1.


Calendar II

CALENDAR(DAILY) year month day

Sets date of first entry for business-day (5-day per week) daily data.

CALENDAR(PERDAY=# of periods) year month day

Sets starting date, frequency, and the number of periods per day for intraday data

calendar(a) year:1 Annual calendar(q) year:quarter Quarterly calendar(b) year:month:day Biweeklycalendar(w) year:month:day Weeklycalendar(d) year:month:day Daily (5/week)calendar(7) year:month:day Daily (7/week)


Allocate

• In older versions, it was necessary to use ALLOCATE immediately following CALENDAR. However, in new versions it is convenient, but not necessary.

• Examples:– For monthly data ending with April 1990 use allocate 1990:4.

For monthly data ending with December 1990, use allocate 1990:12.


Open and DATAThe OPEN and DATA statements are used together.

data(format=xls,org=obs) start end serieswhere: start end Range of entries to read. series The list of series to read.

(If series is omitted, all variables in the data set are read into memory)

Notice the slash (/) in the DATA instruction. In RATS, you can set the range explicitly or use a slash to refer to the default range.

Example:cal(q) 1960:1all 2012:4open data quarterly(2012).xlsdata(org=obs,format=xls)


Form of the Data Sets

DATE Tb3mo Tb1yr RGDP Potent Deflator M2 PPI Curr1960Q1 3.87 4.57 2845.3 2824.2 18.521 298.7 33.2 31.81960Q2 2.99 3.87 2832.0 2851.2 18.579 301.1 33.4 31.91960Q3 2.36 3.07 2836.6 2878.7 18.648 306.5 33.4 32.21960Q4 2.31 2.99 2800.2 2906.7 18.700 310.9 33.7 32.61961Q1 2.35 2.87 2816.9 2934.8 18.743 316.3 33.6 32.11961Q2 2.30 2.94 2869.6 2962.9 18.785 322.1 33.3 32.11961Q3 2.30 3.01 2915.9 2991.3 18.843 327.6 33.3 32.71961Q4 2.46 3.10 2975.3 3019.9 18.908 333.3 33.4 33.4


Sample Statistics

• Table

• table(picture="*.##")

• statistics Tb3mo

• table / tb3mo tb1yr

• print / tb3mo tb1yr


SET

set series start end = function(T)1. A slash (/) or the omission of start end both default to the maximum

permissible range as indicated on ALLOCATE. 2. If you make a mistake, RATS will create missing values instead of an

error message whenever possible. THIS CAN BE A PROBLEM IF YOU ARE UNAWARE OF THE MISTAKE!

3. You must use a space before and after the equal sign. 4. RATS accepts addition (+), subtraction (-), multiplication (*),

division (/), and exponentiation (**) using the usual order of precedence.

5. ABS(X), EXP(X), LOG(X), and SQRT(X) are used to denote the absolute value, exponential value (i.e., ex), natural logarithm, and square root of the argument X, respectively.

6. All levels of parentheses ( ) are supported but you cannot use braces { } or brackets [ ] in place of parentheses.


SET: II

• RATS allows you to use braces { } as a shorthand way to represent time subscripts; you can use series{lag} in place of series(t-lag).

Examples:

– Instead of using y(t-1), you can use y{1}. The number placed in the braces { } indicates the lag number.

– A negative number in braces indicates a leading value of the variable so that y(t+2) and y{-2} are equivalent ways to write the second lead of y.

– Also, the notation {A to B} can be used to indicate lags A through B so that y{1 to 4} indicates the first four lags of ja.


Set: III• set dlrgdp = log(rgdp) - log(rgdp{1})

• set dlm2 = log(m2) - log(m2{1})

• set drs = tb3mo - tb3mo{1}

• set dr1 = tb1yr - tb1yr{1}

• set dlp = log(deflator) - log(deflator{1})

• set dlppi = log(ppi) - log(ppi{1})


Set: IV

• Shortcuts– log y / ly

– difference y / y• You must define the start end range or use the slash (/) on the

DIFFERENCE statement. Unlike the SET statement, if you explicitly define the range, you must allow for the number of lags created.

– exp y / ey

– seasonal seasons

– set trend = t


Graph

GRAPH(Options) number# series start end symbol choice (one for each series)number: number of series to graph

OptionsSTYLE=[LINE]/POLYGON/BAR/STACKEDBAR/OVERLAPBAR/

VERTICAL/STEP/SYMBOLPATTERNS/[NOPATTERNS]HEADER='string' SUBHEADER='label‘ HLABEL='label' VLABEL='label‘NODATES (RATS will label the horizontal axis unless the NODATES option is specified.)KEY=[NONE]/UPLEFT/UPRIGHT/LOLEFT/LORIGHT/ABOVE/BELOW/

LEFT/RIGHTOVERLAY=[LINE]/POLYGON/BAR/STACKEDBAR/OVERLAPBAR

/VERTICAL/STEP/SYMBOLOVCOUNT=Number of series for right-side scale [1]

Examples

graph(header="The Interest Rates",key=below) 2

# tb3mo ; # tb1yr

graph(hea="Real and Potential GDP",key=upleft) 2

# rgdp ; # potent



Multiple Graphs per Page

SPGRAPH(hfields= ,vfields= ,header = ‘ ‘ , subheader = ‘ ‘) :

Spgraph(Done)

Example:SPGRAPH(HFIELDS=2,VFIELDS=2) Graph 1 # x1….Graph 1 # x4SPGRAPH(DONE)


spgraph(footer="Graphs of the Series",hfields=2,vfields=2) graph(header="Panel 1: The Interest Rates",key=below,nokbox) 2 # tb3mo ; # tb1yr graph(header="Panel 2: Real and Potential GDP",key=upleft) 2 # rgdp ; # potent graph(header="Panel 3: Time path of money growth",noaxis) 1 # dlm2 graph(header="Panel 4: Time path of Inflation",noaxis) 1 # dlpspgraph(done)


Scatter Diagrams

SCATTER(Options) pairs # x-series y-series start end

The important options are the same as those for Graph Instruction

NOTE:ON COMPOUND STATEMENTS LIKE GRAPH AND SCATTER, IT YOU MAKE A MISTAKE ON THE SECOND LINE, YOU MUST RE-ENTER THE FIRST LINE.


Linear RegressionSuppose you want to estimate the regression

lin drs / resids# constant drs{1 to 7}

7

0 11

t i t ti

drs drs


LINREGlinreg(options) depvar start end residuals# list

where: depvar The dependent variable.start end The range to use in the regression. residuals (Optional) Series name for the residuals. list The list of explanatory variables.


Linear Regression - Estimation by Least SquaresDependent Variable DRSQuarterly Data From 1962:01 To 2012:04Usable Observations 204Degrees of Freedom 196Centered Rˆ2 0.2841953R-Barˆ2 0.2586309Uncentered Rˆ2 0.2843637Mean of Dependent Variable -0.011617647Std Error of Dependent Variable 0.759163288Standard Error of Estimate 0.653660810Sum of Squared Residuals 83.745401006Regression F(7,196) 11.1168Significance Level of F 0.0000000Log Likelihood -198.6489

Durbin-Watson Statistic 1.9709Variable Coeff Std Error T-Stat Signif************************************************************************************1. Constant -0.011903358 0.045799634 -0.25990 0.795213162. DRS{1} 0.390010248 0.069644459 5.60002 0.000000073. DRS{2} -0.380186642 0.074718282 -5.08827 0.000000844. DRS{3} 0.406843358 0.078304236 5.19567 0.000000515. DRS{4} -0.159123423 0.082740231 -1.92317 0.055908096. DRS{5} 0.193334248 0.078290297 2.46945 0.014387247. DRS{6} -0.089946745 0.074692035 -1.20423 0.229951078. DRS{7} -0.220768119 0.069358921 -3.18298 0.00169542


LINREG: IIThe most useful options are:DEFINE = Name the equation by setting DEFINE = ‘NAMENOPRINT Do not print the regression output. VCV Print the covariance/correlation matrix of the coefficients. LINREG creates a number of variables that you can use in subsequent computations. A partial list of these variables is:%BETA The coefficient vector. The first coefficient estimated is

%BETA(1), the second %BETA(2), and so on. %tstats%NDF Degrees of freedom%NOBS Number of observations.%NREG Number of regressors.%RSS Residual sum of squares.%RSQUARED Centered R2 (i.e, the usual measure of R2)%SEESQ Standard error of estimate squared


Correlations

CORRELATE: Calculates the autocorrelations (and the partial autocorrelations) of a specified series. The syntax and principal options are: correlate(options) series start endwhere: series The series used to compute the correlations.start endThe range of entries to use. The default is the entire series.

The principal options are: NUMBER= The number of autocorrelations to compute. PARTIAL= Series for the partial autocorrelations. QSTATS Use this option if you want the Ljung-Box Q-statistics.DFC= Degrees of freedom correction. SPAN= Use with qstats to set the width of the intervals tested. For example, with quarterly data, you can set span = 4, to obtain Q(4), Q(8), Q(12), and so forth.


correlate(options) series start endwhere: series The series used to compute the correlations.start end The range of entries to use. The default is the entire series.corrs Series used to save the autocorrelations (Optional). The principal options are: NUMBER= The number of autocorrelations to compute. The default is the integer value of one-fourth the total number of observations. PARTIAL= Series for the partial autocorrelations. If you omit this option, the PACF will not be calculated.QSTATS Use this option if you want the Ljung-Box Q-statistics.SPAN= Use with qstats to set the width of the intervals tested. For example, with quarterly data, you can set span = 4, to obtain Q(4), Q(8), Q(12), and so forth. In the example at hand, we can obtain the first twelve autocorrelations, partial autocorrelations (and the associated Q-statistics) of the residuals with: cor(number=12,partial=partial,qstats,span=4) resids


Restrictions

1. Exclusion and Sum Restrictions

Examples:

exclude# drs{5 to 7}

exclude# constant drs{5 to 7}

summarize# drs{5 to 7}


TEST: Individual Coefficient Restrictions

2. Test This instruction contains three lines:TEST # list of coefficients to be restricted# constants against which coeff. are testedExample:To test if beta(6) = 0.1, beta(7) = 0.1 and %beta(8) = 0., use:

test# 6 7 8# 0.1 0.1 0.0

To test the restriction that the constant equals zero and that 5 = 0.1, 6 = 0.1, and 7 = 0.1, use:test# 1 6 7 8# 0. 0.1 0.1 0.1


Restrict

3. RESTRICT can test multiple linear restrictions on the coefficients and estimate the restricted model. Each restriction is entered in the form: βiai + βjaj + ... + βkak = r

where: ai are the coefficients of the estimated model (i.e.,

each coefficient is referred to by its assigned number).βi are weights you assign to each coefficient.

r represents the restricted value of the sum


Restrict: II

Examples1. To test the joint restriction that a2 + 2a3 = 0 (i.e., 1.0*a2 + 2.0*a3 = 0), use:

restrict 1# 2 3# 1 2 0

2. To test the joint restriction that a2 + 2a3 = 0 (i.e., 1.0*a2 + 2.0*a3 = 0), use:

restrict 1# 2 3# 1 2 0

3. restrict(create) 2 resids# 5 6 7 8# 1 1 1 1 0# 2 3# 1 ‑1 0


Forecast

FORECAST: Creates dynamic forecasts of a previously defined equation. FORECAST uses a supplementary card for each equation in the system. In univariate forecasting, the typical syntax for FORECAST is: forecast(print) number steps start # equation forecasts

where: number The number of equations in the system. In univariate forecasting, number is necessarily equal to 1. steps The number of forecasts to create.start The starting period of the forecasts.equationThe name of the previously defined equation.forecasts The name of the series in which you want to save the

forecasts. This field is optional.


Forecast: II

Example:

lin(define=eq1) rcons 1947:1 1997:2 resids# constant trend t3 t4 t5

forecast(print) 1 12 1997:3# eq1 fore

rats dialog interface

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